Leucine-rich repeat-containing protein 19 suppresses colorectal cancer by targeting cyclin-dependent kinase 6/E2F1 and remodeling the immune microenvironment

Abstract

BACKGROUND

Colorectal cancer (CRC) is a leading cause of cancer-related mortality worldwide, primarily due to tumor heterogeneity and treatment resistance. The leucine-rich repeat-containing protein 19 (LRRC19) has been linked to immune regulation and tumor suppression, yet its specific role in CRC remains poorly understood.

AIM

To investigate the tumor-suppressive role of LRRC19 in CRC, focusing on cell cycle, immune microenvironment, and chemotherapy response.

METHODS

Bioinformatics analyses of Gene Expression Omnibus and The Cancer Genome Atlas databases identified differentially expressed genes in CRC. LRRC19 expression was validated in CRC tissues and cell lines by quantitative PCR, immunohistochemistry, and Western blotting. Functional assays, including proliferation, soft agar colony formation, flow cytometry, and xenograft models, assessed biological effects. Mechanistic studies with dual-luciferase reporter assays, molecular docking, and drug sensitivity testing explored LRRC19’s interaction with the cyclin-dependent kinase 6 (CDK6)/E2F1 axis and oxaliplatin (OXA) response. Single-cell sequencing and immune infiltration analyses assessed its impact on the immune microenvironment.

RESULTS

LRRC19 expression was significantly downregulated in CRC and associated with poor prognosis. Overexpression of LRRC19 inhibited CRC cell proliferation, induced G0/G1 phase arrest, and suppressed tumor growth in vivo. Mechanistically, LRRC19 suppressed CDK6 transcription by downregulating E2F1, leading to cell cycle arrest. Additionally, LRRC19 promoted immune cell infiltration, particularly B cells and CD4+ T cells, while decreasing immunosuppressive cells. LRRC19 also sensitized CRC cells to OXA, enhancing chemotherapy efficacy.

CONCLUSION

LRRC19 suppresses CRC by targeting the CDK6/E2F1 axis, modulating the immune microenvironment, and enhancing chemotherapy sensitivity, making it a promising therapeutic target for precision medicine in CRC.

Key Words: Colorectal cancer; Tumor suppressor; Cell cycle arrest; Immune microenvironment; Chemotherapy sensitivity; Cyclin-dependent kinase 6/E2F1 axis; Oxaliplatin response; Leucine-rich repeat-containing protein 19; Immune infiltration; Precision medicine

Core Tip: Colorectal cancer (CRC) remains a major global health challenge due to tumor heterogeneity and treatment resistance. Leucine-rich repeat-containing protein 19 (LRRC19) is identified as a novel tumor suppressor in CRC, inhibiting the cyclin-dependent kinase 6/E2F1 axis to induce cell cycle arrest, remodeling the immune microenvironment by enhancing immune cell infiltration, and improving chemotherapy sensitivity to oxaliplatin. These findings provide new insights into the molecular mechanisms of CRC progression and suggest LRRC19 as a promising therapeutic target for precision medicine in CRC management.

INTRODUCTION

Colorectal cancer (CRC) remains a major global health challenge, ranking third in cancer incidence (9.6% of new cases) and second in mortality (9.3% of cancer deaths) worldwide, with approximately 1.93 million new diagnoses and 0.90 million fatalities reported in 2022 according to GLOBOCAN statistics[1]. In recent years, the incidence and mortality of CRC in China are also on the rise[2]. The molecular pathogenesis of CRC involves a dynamic interplay between multistep genetic alterations and tumor microenvironment (TME) reprogramming. While therapeutic advances targeting cell cycle checkpoints [e.g., cyclin-dependent kinase (CDK4/CDK6) inhibitors] and immune checkpoint blockade [anti-programmed cell death protein 1 (PD-1)/cytotoxic T-lymphocyte-associated protein 4] have improved clinical outcomes, persistent challenges remain due to tumor heterogeneity and therapeutic resistance[3]. These clinical realities underscore the critical need for identifying novel regulatory hubs within CRC signaling networks to develop precision therapeutic strategies[4].

Functional transmembrane receptors (FTRs) play pivotal roles in cellular signaling and immunometabolic reprogramming[5,6]. As specialized pattern recognition receptors (PRRs), these molecules initiate intracellular signaling cascades through pathogen-associated molecular pattern recognition, orchestrating immune responses against pathogens and tumor progression[7,8]. Leucine-rich repeat-containing 19 (LRRC19), a recently identified PRR located at chromosome 9p21.9, encodes a 370-amino acid transmembrane protein (42.3 kDa). Evolutionary conservation across species underscores its fundamental role in innate and adaptive immunity[9,10]. LRRC19, as a transmembrane receptor featuring four extracellular leucine-rich repeat motifs with structural homology to Toll-like receptors (TLRs)[11], activates nuclear factor kappa B (NF-κB) signaling and induces proinflammatory cytokine production. Mechanistic studies reveal its capacity to modulate tumor necrosis factor (TNF) receptor-associated factor 6 (TRAF6)/transforming growth factor β-activated kinase 1 (TAK1)-mediated epithelial-mesenchymal transition (EMT) in CRC[12] and serve as a direct target of the Src inhibitor AZD0530[13]. While transcriptomic analyses associate LRRC19 downregulation with enhanced proliferative capacity in pressure injury models[10], its tumor-suppressive function is evidenced by prognostic correlations in clear cell renal carcinoma[14]. Emerging evidence implicates LRRC19 in gastrointestinal pathophysiology, demonstrating dual regulation of inflammatory responses and tumorigenesis[12-16]. In ulcerative colitis, LRRC19 exerts anti-inflammatory effects by suppressing STAT3 activation[15], whereas in CRC, LRRC19 reportedly promotes NF-κB-dependent inflammation via TRAF6/TAK1 signaling, facilitating tumor progression[12]. However, these studies primarily focused on inflammatory or angiogenic pathways, with limited exploration of cell cycle regulation or immune microenvironment remodeling.

Our work identified LRRC19 as a context-dependent tumor suppressor in CRC, uniquely targeting the CDK6/E2F1 axis (a previously unreported mechanism) and reprogramming immune cell infiltration. This dual mechanism, combining cell cycle arrest and immune microenvironment modulation, distinguishes its function in CRC from other malignancies and inflammatory diseases, underscoring the tissue-specificity of LRRC19 signaling.

MATERIALS AND METHODS

Analysis of differentially expressed genes and regulatory network construction in CRC

Logged into the Gene Expression Omnibus (GEO) database and conducted a search using the keywords "colorectal cancer and normal tissue", focusing primarily on comparative studies between CRC and normal tissues, and excluding other types of CRC datasets. Ultimately, the GSE41328 and GSE23878 datasets, both containing gene expression data for CRC tissues and adjacent normal tissues, were selected[17]. Initially, rigorous quality control and preprocessing were conducted on the raw data, which included removing low-quality samples (filtering criteria: Intersample correlation coefficient < 0.8 or outlier detection), background correction, quantile normalization (using rate-monotonic analysis algorithm), and batch effect correction (with the ComBat method) to ensure data reliability. Principal component analysis (PCA) was performed separately on these two datasets. Subsequently, differential expression analysis was performed using the R package DESeq2 and Limma, with a significance threshold set at adjusted P < 0.01 (Benjamini-Hochberg correction) and a fold change (FC) threshold of |log2FC| > 1.6 to screen for significantly differentially expressed genes (DEGs). Volcano plots were drawn using ggplot2, where the X-axis represents log2FC (logarithmic FC in gene expression), and the Y-axis represents -log10 (adjusted P value) (logarithmically transformed significance level). Genes were marked in red for upregulation, blue for downregulation, and gray for no significant difference, whereas the top 10 significant genes such as APC and KRAS were highlighted to emphasize their biological significance.

The expression matrix of DEGs was further extracted, and a hierarchical clustering heatmap was generated using the pheatmap package. The data were Z-score-standardized to display the expression patterns of genes across samples. The ggVennDiagram package was used to draw a Venn diagram comparing DEGs between GSE41328 and GSE23878, identifying common and unique gene sets. The common DEGs were imported into the STRING database to construct a protein-protein interaction (PPI) network, with a confidence threshold set at ≥ 0.400[18]. Cytoscape (v3.9.1) was used for network visualization, including hiding unconnected nodes to simplify the network, applying the MCODE plugin to identify core functional modules, setting node size based on connectivity, and color mapping based on log2FC values. Based on literature screening, 19 FTR-related genes were cross-analyzed with 33 candidate genes in the PPI network, and the overlap between the two gene sets was quantified using the UpSetR package to create an interactive Venn diagram.

A systematic analysis of the regulatory network associated with the LRRC19 gene was conducted using bioinformatics methods. Firstly, potential microRNAs (miRNAs) interacting with LRRC19 were predicted using the authoritative databases TargetScan, miRDB, and miRanda. The prediction results were integrated and deduplicated to obtain a comprehensive list of miRNAs. Then the spongeScan tool was used to screen for potential long non-coding RNAs (lncRNAs) targeting these miRNAs. The most likely key lncRNAs regulating LRRC19 expression through miRNAs were determined through comparative analysis, and the miRNAs-lncRNAs interaction relationships were visually presented using network diagrams.

To further uncover the biological functions of LRRC19, co-expression genes significantly correlated (both positively and negatively) with LRRC19 expression were mined using the UALCAN database[19]. These genes were subjected to Gene Ontology (GO) functional annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis. To clearly present the analysis results, a multi-layer network diagram was used for visualization: The innermost layer displayed KEGG pathway enrichment results, while the outer layers showed GO functional enrichment results, thereby revealing the biological processes and signaling pathways that LRRC19 may be involved in. All analysis results were compiled into a detailed analytical report[20,21]. Using the GSE41328 dataset, a single-gene GSEA enrichment analysis was conducted on LRRC19 to explore its potential biological functions.

Clinical analysis of LRRC19 in CRC

This study conducted an in-depth analysis based on The Cancer Genome Atlas database, examining RNA sequencing data and clinical follow-up information of patients with CRC[22]. It explored the correlation between LRRC19 gene expression levels and disease-free interval (DFI), compared the differences in disease-free survival (DFS) between the two groups of patients, and tested the statistical significance of LRRC19 gene expression levels in overall survival (OS). Additionally, survival analysis was performed on the progression-free interval. Moreover, a thorough analysis of LRRC19 gene expression levels in CRC samples from the TCGA database was conducted using the UALCAN database, including expression differences among patients of various age groups, sex comparisons, LRRC19 expression level contrasts in different histological subtypes (normal, adenocarcinoma, mucinous adenocarcinoma), classification comparisons based on lymph node metastasis status, LRRC19 gene expression level differences among different races, analysis of LRRC19 gene expression levels at various stages of colon adenocarcinoma (COAD), and classification comparisons based on tumor protein p53 (TP53) mutation status. Furthermore, the study investigated the differences in LRRC19 gene expression levels among patients with COAD across different body weight categories.

Analysis of LRRC19 drug sensitivity

The drug sensitivity of LRRC19 was analyzed based on the Genomics of Drug Sensitivity in Cancer database to identify candidate drugs associated with it[23]. The protein structure of LRRC19 was obtained from the Protein Data Bank database, and the molecular structures of the drugs were downloaded from the PubChem database[24,25]. Subsequently, the CB-Dock2 online molecular docking platform was used to predict the binding mode and binding energy between the drugs and LRRC19[26]. The docking results were evaluated using binding energy (kcal/mol) as the criterion, with lower binding energy indicating a more stable binding between the drug and the target protein. Using the GSE290028 dataset, which includes gene expression profiles of 13 patients with CRC insensitive to chemotherapy and 9 sensitive patients, we compared the expression differences of the LRRC19 gene between the two groups.

Analysis of the immune microenvironment of LRRC19

Utilizing the analytical tools provided by the Tumor Immune Estimation Resource database, we systematically investigated the correlation between the expression level of the LRRC19 gene in CRC tissues and the infiltration degree of various immune cells[27]. The specific immune cell subpopulations analyzed included B lymphocytes, cancer-associated fibroblasts (CAFs), macrophages, monocytes, myeloid dendritic cells (mDCs), neutrophils, natural killer (NK) cells, CD4+ T lymphocytes, CD8+ T lymphocytes, γδ T cells, NKT cells, and regulatory T cells (Tregs). We calculated the Spearman correlation coefficient (Rho) between LRRC19 expression level and the infiltration level of each immune cell, and assessed its statistical significance (P value). To explore the correlation of the LRRC19 gene with immune checkpoints in pan-cancer and specifically in CRC, we collected LRRC19 gene expression data and immune checkpoint-related gene expression data from multiple cancer types. Statistical methods (such as Spearman or Pearson correlation coefficients) were used to analyze the correlation between LRRC19 gene expression and immune checkpoint genes. The P value for each correlation coefficient was calculated to determine the significance level of the correlation. An analysis of the immune-inflammatory correlation of LRRC19 in pan-cancer was conducted. Based on this, we focused on the immune-inflammatory correlation of LRRC19 in CRC. By analyzing the correlation coefficients (cor) and significance levels (P) between different pathways and LRRC19 expression levels, we explored the role of LRRC19 in immune-inflammatory pathways. To conduct an in-depth analysis of the co-expression or correlation between LRRC19 and CD8 T cell exhaustion markers [PD-1/lymphocyte activation gene 3 (LAG3)] in CRC, a comprehensive list of gene symbols was first compiled. This list included inhibitory receptors [PD-1, CTLA4, hepatitis A virus cellular receptor 2 (HAVCR2), LAG3, T cell immunoreceptor with immunoglobulin and ITIM domain (TIGIT), B and T lymphocyte attenuator (BTLA), TNF receptor superfamily member 14 (TNFRSF14)], transcription regulators [thymocyte selection-associated HMG-box protein (TOX), ETS variant transcription factor 7 (ETV7), hepatocyte nuclear factor 1-alpha (HNF1A), nuclear receptor subfamily 4 group A member 1 (NR4A1)], metabolism-related genes, and other markers [interferon gamma (IFNG), TNF, granzyme B (GZMB), perforin 1 (PRF1)], as well as co-inhibitory molecules [CD274, PD-1, laminin G-like domain 2 (LG2), inducible T cell costimulator ligand (ICOSLG)]. Subsequently, the GSE41328 CRC dataset from the GEO database was preprocessed, which involved downloading the raw data files, extracting and organizing the expression matrix, and normalizing the data to ensure consistency and accuracy of the gene symbols.

Single-cell analysis of LRRC19 in CRC

This study utilized the GSE161277 CRC single-cell dataset, randomly selecting three pairs of normal/tumor tissue samples for analysis. To mitigate potential batch effects, the proportion of mitochondrial genes in each cell was first calculated, along with the distribution of gene counts, RNA, and mitochondrial genes to assess the overall cell state and quality. Subsequently, cells were filtered based on the criteria of nFeatureRNA > 200, nCountRNA > 1000, and nCountRNA < 20000 to remove low-quality cells and potential outliers, ensuring the accuracy of subsequent analyses. Then, the filtered cells were normalized to eliminate technical variations between different samples, a critical step in single-cell data analysis that helps to remove systematic biases between cells. Finally, PCA was performed, and the number of PCs determined for cell clustering was nine, which aided in the identification and differentiation of various cell populations. The t-distributed stochastic neighbor embedding (t-SNE) nonlinear dimensionality reduction technique was used to visualize cell distribution patterns. Cell types were identified based on marker genes from the Human_cell_markers database. Finally, the proportion distribution of different cell types in each sample was statistically analyzed.

Plasmids, reagents, and antibodies

The LRRC19-overexpression plasmid and corresponding control vectors were purchased from Miaoling Biotechnology (Wuhan, China). The CDK6 promoter plasmid and HA-tagged vectors were constructed by Qingke Biotechnology (Beijing, China). Overexpression plasmids PCDH-CDK6 and PCDH-E2F1 were generated by our laboratory. Specific antibodies used included anti-LRRC19 (PA5-20914) from Invitrogen (Grand Island, NY, United States); antibodies against cyclin A2 (Sc-53234), cyclin B1 (Sc-245), cyclin D1 (Sc-20044), cyclin E2 (Sc-481), CDK2 (Sc-6248), CDK4 (Sc-260), CDK6 (Sc-177), E2F1 (Sc-251), SP1 (Sc-59), and ETS-1 (Sc-350) from Santa Cruz Biotechnology (Santa Cruz, CA, United States); antibodies against forkhead box P3 (FOXP3) (12829S), ELK-1 (9182S), Flag (14793S), and HA (3724S) from Cell Signaling Technology (Boston, MA, United States); and antibodies specific for glyceraldehyde-3-phosphate dehydrogenase (10494-1-AP) and α-tubulin (66031-1-Ig) from Proteintech (Chicago, IL, United States).

Clinical specimens and cell culture

A total of 150 paired clinical CRC specimens and matched adjacent normal tissues were collected from the First Affiliated Hospital of Wenzhou Medical University (Zhejiang, China), with ethical approval from the Ethics Committee of Wenzhou Medical University. Comprehensive clinical information for these samples was obtained and confirmed, as detailed previously in our earlier studies. Human CRC cell lines HT29 (CBP60011; COBIOER, Nanjing, China), RKO (CBP60006; COBIOER), LoVo (CBP60032; COBIOER), SW620 (CBP60036; COBIOER), and SW480 (CBP60019; COBIOER) were cultured in RPMI-1640 medium (11875-093; Sigma, St. Louis, MO, United States). The HCT116 cell line (CBP60028; COBIOER) was maintained in McCoy’s 5A medium (PM150710; Procell Life Science & Technology Company, Wuhan, China). All cells were cultured at 37 °C in a humidified incubator containing 5% CO₂.

Quantitative PCR

Total RNA was extracted from cells using TRIzol reagent (15596018; Invitrogen). Complementary DNA was synthesized using a reverse transcription kit (RR037A; Takara Bio, Shiga, Japan) according to the manufacturer’s instructions. Quantitative PCR (qPCR) was conducted to determine mRNA expression levels using specific primers: LRRC19- forward: 5’-TGC TAT CAA GTG CCC AGT ATG-3’; LRRC19-reverse: 5’-TTT CTG CCT CAT GCT CTT CC-3’. CDK6-forward: 5’-GGT ACA GAG CAC CCG AAG TC-3’; CDK6-reverse: 5’-CTC CTG GGA GTC CAA TCA CG-3’. All experiments were performed according to standardized protocols. Additionally, total RNA was isolated using Qiagen miRNA extraction kits (Hilden, Germany), reverse-transcribed, and subjected to qPCR to assess the expression of miRNAs related to LRRC19 regulation.

Immunohistochemistry

Immunohistochemistry staining was performed to evaluate LRRC19 protein expression in 150 paired clinical CRC specimens. Tissue samples were fixed, dehydrated, embedded in paraffin, and sectioned. Sections underwent hematoxylin and eosin staining for histopathological classification. Subsequently, immunostaining was performed with anti-LRRC19 antibody (PA5-20914; Invitrogen) using a commercial kit (SA1022; Boston Bio, Pleasanton, CA, United States), including 3% hydrogen peroxide, 5% bovine serum albumin, secondary antibodies, SABC reagent, and DAB substrate (59718; Abcam, Cambridge, MA, United States), according to the manufacturer’s instructions. Images were captured using the AxioVision Rel.4.6 imaging system. Protein expression levels were quantified by integrated optical density (IOD) (IOD/area) using Image-Pro Plus software (version 6.0).

Cell proliferation, anchorage-independent growth, and cell cycle analysis

Cells were seeded into 96-well plates until adherence, starved in medium containing 0.1% fetal bovine serum (FBS) for 12 hours, and subsequently cultured in medium containing 10% FBS for the indicated durations. After incubation, the medium was discarded, and an ATP-based reagent mixed with phosphate-buffered saline was added to each well. Plates were shaken gently, incubated for 10 min, and analyzed using a Centro LB 960 Luminometer (Berthold Technologies, Bad Wildbad, Germany).

For anchorage-independent growth assays, cells were mixed with an upper layer of agarose gel placed over a pre-solidified lower agarose layer in 6-well plates and cultured at 37 °C with 5% CO₂. Colonies containing more than 32 cells were photographed under a microscope, counted, and analyzed.

For cell cycle analyses, cells seeded at 30%-40% confluence in 6-well plates were starved (0.1% FBS) and then cultured in complete medium (10% FBS) for 12 hours. Cells were harvested, fixed overnight in pre-chilled 70% ethanol at 4 °C, stained with propidium iodide (PI; 360 μL) and RNase A (40 μL), and incubated at room temperature for 30 minutes. Cell cycle distributions were analyzed using CytoFLEX flow cytometry (Beckman Coulter, San Diego, CA, United States).

Western blot analysis

Cells were lysed in lysis buffer, and protein concentrations in supernatants were measured using NanoDrop One (Thermo Fisher Scientific, Waltham, MA, United States). Proteins were separated via sodium dodecyl sulfate-polyacrylamide gel electrophoresis, transferred onto polyvinylidene fluoride membranes, blocked in 5% skim milk, incubated with primary and secondary antibodies, and developed using chemiluminescence reagents. Protein bands were visualized and analyzed with a phosphor imaging scanner (Typhoon FLA 7000; GE Healthcare, Chicago, IL, United States).

Cell viability assay

CRC cells (6000 cells/well) were seeded into 96-well plates, starved in medium containing 0.1% FBS for 12 hours, and subsequently treated with various concentrations of palbociclib diluted in medium containing 10% FBS for 48 hours. Cell viability was assessed using the ATP-based method as previously described.

Nude mouse xenograft model

All animal experiments were approved by the Animal Ethics Committee of Wenzhou Medical University. Twelve female BALB/c nude mice purchased from Jiangsu Jinyao Biotechnology (Nanjing, China) were housed under specific pathogen-free conditions in the Animal Facility at Wenzhou Medical University. After acclimation (approximately 1 week), mice were randomly divided into two groups and marked individually with ear tags. HCT116 (Vector) or HCT116 (LRRC19-Flag) cells were subcutaneously injected into the mice. Tumors were allowed to grow for approximately 3 weeks, after which mice were euthanized, and tumors were excised, photographed, and weighed.

Dual luciferase reporter assay

Cells stably expressing the E2F1 promoter-luciferase reporter were seeded in 96-well plates (8 × 10³ cells/well). When cells reached 70%-80% confluence, cells were transfected with TK plasmids and either wild-type or mutant CDK6 promoter plasmids. After 24 hours, cells were lysed in passive lysis buffer, incubated at room temperature for 15 minutes, and luciferase activity was measured using Dual Luciferase Reporter Assay System reagents (Promega, Madison, WI, United States) on a luminometer (Wallac 1420 Victor2; PerkinElmer, Waltham, MA, United States). Promoter activity was quantified based on relative luciferase units.

Chromatin immunoprecipitation assay

Chromatin immunoprecipitation (ChIP) was performed using the SimpleChIP^® Enzymatic Chromatin IP Kit (Catalog #9003; Cell Signaling Technology, Danvers, MA, United States) following the manufacturer’s protocol. Briefly, HCT116 cells (4 × 10⁶ per sample) were crosslinked with 1% formaldehyde for 10 minutes at room temperature, followed by chromatin fragmentation to 200-500 base pairs using enzymatic digestion. IP was carried out overnight at 4 °C with 2 μg of either normal rabbit immunoglobulin G (IgG) or E2F1-specific antibody. After reversing crosslinks and purifying DNA, quantitative PCR was performed using primers targeting the CDK6 promoter region. Amplified DNA fragments were resolved via 2% agarose gel electrophoresis, stained with ethidium bromide (0.5 μg/mL), and visualized under ultraviolet transillumination.

Drug sensitivity assays

Specific CRC cell lines, including HCT116 (Vector), HCT116 (LRRC19), HT29 (Vector), and HT29 (LRRC19), were treated with various concentrations of oxaliplatin (OXA) (O9512; Sigma-Aldrich) (0, 5, 10, 20, 40, and 80 μM) for 48 hours to assess drug sensitivity. Western blot analysis was performed to detect changes in LRRC19 expression. Morphological changes indicative of apoptosis or cytotoxicity induced by OXA treatment were observed using an inverted microscope.

Statistical analyses

All analyses were performed in R (v4.2.1). Data are presented as the mean ± SD. Differences between groups were assessed using Student’s t-test or analysis of variance, and survival outcomes were analyzed via Kaplan-Meier curves (log-rank test). Two-tailed P < 0.05 was considered statistically significant.

RESULTS

LRRC19 is a key regulatory factor in CRC

The PCA plot clearly revealed the distribution of samples from the two datasets within the principal component space, intuitively unveiling their similarities and potential batch effects (Supplementary Figure 1). Through an in-depth analysis of the CRC GSE41328 dataset, a group of genes with significant expression differences was successfully identified. The analysis results revealed a clear bidirectional regulatory pattern among these DEGs: Several genes, including P-cadherin, claudin-11, and FOXQ1, showed a significant upregulation trend, while genes such as fibroblast growth factor-binding protein 1, SPIB, and keratin 80 exhibited marked downregulation. Additionally, a large number of other genes also showed varying degrees of changes in expression levels. To systematically present these important findings, a multidimensional visualization analysis map was constructed. Figure 1A visually represents the heterogeneity of gene expression profiles among different samples through a heatmap. Figure 1B's volcano plot analysis comprehensively displays the expression change patterns and their statistical significance for all detected genes, with red dots representing significantly upregulated genes, blue dots indicating significantly downregulated genes, and gray dots signifying genes with no significant change in expression. After rigorous screening criteria, 296 genes with significant expression differences in tumor tissues were finally determined. In the validation analysis of the GSE23878 dataset, a total of 145 DEGs were identified. Figure 1C displays the expression distribution characteristics of some representative differential genes across all samples through a heatmap, where the color gradient from red to blue clearly reflects the transition from high to low gene expression. Figure 1D highlights several DEGs with statistical significance, including key genes such as ETV4, SCGB1D2, and UGT2B15, which all show significant expression regulatory trends. The same standardized presentation method is used in this figure, with red for upregulated genes, blue for downregulated genes, and gray for genes with no significant change.

Figure 1 Colorectal cancer differentially expressed gene analysis. A: Sample gene expression heterogeneity (heatmap); B: Genome-wide expression changes (volcano plot); C: Representative differentially expressed gene (DEG) expression distribution (heatmap); D: DEG statistical significance with key genes; E: Common DEGs between GSE41328 and GSE23878; F: Core gene interactions [streamlined protein-protein interaction (PPI)]; G: Leucine-rich repeat-containing 19 (LRRC19) overlap in transmembrane receptors and PPI network; H: Potential microRNAs/long non-coding RNAs regulating LRRC19; I: LRRC19-positively correlated genes; J: LRRC19-negatively correlated genes; K: Positive correlation gene enrichment [Kyoto Encyclopedia of Genes and Genomes (KEGG)/Gene Ontology (GO)]; L: Negative correlation gene enrichment (KEGG/GO). FTR: Functional transmembrane receptor.

Figure 1E reveals 60 common DEGs shared between the GSE41328 and GSE23878 datasets. Figure 1F presents a streamlined PPI network, where core genes such as chromogranin A, somatostatin, and glycogen phosphorylase B exhibit significant direct interactions with other genes, forming multiple tightly connected clusters. This suggests their potential cooperative roles in the same biological processes. Figure 1G identifies LRRC19 as the sole overlapping gene between transmembrane receptor-related genes and the PPI network, indicating its critical involvement in both biological pathways. Integrated analysis (Figure 1H) suggests that miRNAs (e.g., hsa-miR-186-5p, hsa-miR-223-3p) and lncRNAs (e.g., AC005614.3) may regulate LRRC19 expression through a broader interaction network. These findings highlight the complex, multi-layered molecular regulation of LRRC19, underscoring its potential key role in cellular functions and disease pathogenesis.

Figure 1I and J display genes positively and negatively correlated with LRRC19, respectively. KEGG enrichment analysis (Figure 1K) reveals that LRRC19 is closely associated with various metabolic pathways, including alpha-linolenic acid metabolism, unsaturated fatty acid biosynthesis, sugar metabolism (e.g., galactose, pyruvate, pentose, and glucuronate conversions), as well as the intestinal immune network for IgA production. GO analysis indicates that the gene is involved in regulating cellular processes related to structural components like cell protrusion size and microvillus organization, and possesses molecular functions such as guanosine diphosphate phosphatase and oxidoreductase. These findings suggest that LRRC19 plays a key role in cytoskeleton construction, metabolic regulation (particularly lipid and carbohydrate metabolism), and signal transduction, providing important clues for functional studies and the exploration of disease association mechanisms. Figure 1L presents the KEGG and GO enrichment analysis results for genes negatively correlated with LRRC19. The KEGG analysis shows that the LRRC19 gene is significantly associated with pathways such as "Ribosome", "Sphingolipid signaling pathway", and "Tuberculosis". GO analysis indicates that the gene primarily participates in the biological processes of "ribonucleoprotein complex biogenesis" and "translation", is enriched in ribosomal structures in the cellular component, and is involved in molecular functions related to "ribosome structure composition" and "growth factor binding". These discoveries suggest that LRRC19 may be involved in a variety of biological functions through the regulation of protein synthesis and metabolic processes, providing directions for further investigation into its molecular mechanisms. LRRC19 may have functioned by regulating multiple biological processes. The upregulated pathways were primarily involved in metabolic processes, including ascorbate and aldarate metabolism, other glycan degradation, pentose and glucuronate interconversions, proximal tubule bicarbonate reabsorption, and retinol metabolism. Conversely, the downregulated pathways were mainly associated with diseases and protein synthesis and degradation, such as CRC, ribosome, eukaryotic ribosome biogenesis, RNA degradation, and ubiquitin-mediated proteolysis (Supplementary Figure 2).

LRRC19 is significantly downregulated in CRC tissues

As shown in Figure 2A, the expression of LRRC19 in CRC tissues is significantly lower than in normal tissues (P < 0.01). This finding was validated in our independent clinical cohort (n = 150, P < 0.05; Figure 2B) and immunohistochemical results (P < 0.05; Figure 2C and D). Obvious brown granule deposition in the cytoplasm was observed in normal tissues, whereas the staining signal was significantly reduced in cancer tissues. Moreover, the expression of LRRC19 in metastatic CRC cell lines (HT29, HCT116, RKO, LoVo, and SW620) was significantly decreased compared to SW480 (P < 0.05; Figure 2E), with stable α-tubulin expression, suggesting that low LRRC19 expression may be associated with CRC metastasis.

Figure 2 Leucine-rich repeat-containing 19 expression in colorectal cancer tissues and cell lines. A: Leucine-rich repeat-containing 19 (LRRC19) expression is significantly lower in cancer tissues than in normal tissues (^aP < 0.01); B: Validation in an independent clinical cohort (n = 150, ^bP < 0.05); C and D: Immunohistochemical results showing reduced staining in cancer tissues (^bP < 0.05); E: Decreased LRRC19 expression in metastatic colorectal cancer cell lines compared to SW480 (^bP < 0.05), with stable α-tubulin expression.

Association of LRRC19 expression with prognosis and clinical characteristics in CRC

Figure 3A shows the results of survival analysis, indicating that although there were no statistically significant differences in DFI (P = 0.082) and DSS (P = 0.092) between the high LRRC19 expression group and the low expression group, the OS was significantly reduced (P < 0.05), and the probability of survival was markedly decreased (P < 0.01). Figure 3B illustrates the differential expression characteristics of LRRC19 in CRC: The LRRC19 expression level in normal tissues (n = 41) was significantly higher than that in tumor tissues across various comparisons, including sex (male n = 156: P < 1 × 10^-12; female n = 127: P < 1 × 10^-12), pathological type (adenocarcinoma/mucinous adenocarcinoma: P < 1 × 10^-12), lymph node metastasis status (N0/N1/N2: All P < 1 × 10^-12), ethnicity (Caucasian P < 1 × 10^-12; African American P < 1 × 10^-12; Asian P < 1 × 10^-7), clinical stage (stages I-IV: All P < 1 × 10^-12), TP53 mutation status (mutated group n = 160/non-mutated group n = 122: All P < 1 × 10^-12), and different body mass index categories (all P < 1 × 10^-12).

Figure 3 Leucine-rich repeat-containing 19 expression and survival outcomes in colorectal cancer. A: Survival analysis showing no significant differences in disease-free interval (DFI) (P = 0.082) and disease-specific survival (P = 0.092) between high and low leucine-rich repeat-containing 19 (LRRC19) expression groups, but significantly reduced overall survival (OS) (^bP < 0.05) and progression-free interval (^aP < 0.01); B: Differential expression of LRRC19 in normal (n = 41) vs tumor tissues, with significant differences across various comparisons, including sex, pathological type, lymph node status, ethnicity, clinical stage, tumor protein p53 (TP53) mutation status, and body mass index categories (all ^cP < 1 × 10^-12, except Asian ^dP < 1 × 10^-7).

LRRC19 demonstrates a strong affinity for targeted anti-cancer drugs

Figure 4 shows that a variety of drugs have high binding affinity to LRRC19, with amuvatinib exhibiting the lowest binding energy (-8.1 kcal/mol), suggesting it may have the strongest inhibitory effect. STF-62247 (-7.5 kcal/mol) and Docetaxel (-6.8 kcal/mol) also demonstrate good binding capabilities. The remaining drugs, such as Ciclopirox (-6.2 kcal/mol), Elesclomol (-6.2 kcal/mol), c-Jun N-terminal kinase inhibitor VIII (-6.0 kcal/mol), Piperlongumine (-6.1 kcal/mol), and Sepantronium bromide (-6.7 kcal/mol), all have binding energies below -6.0 kcal/mol, indicating they may have stable interactions with LRRC19. LRRC19 was significantly overexpressed in the chemoresistant group (P < 0.01), supporting the association between high expression of LRRC19 and chemoresistance in CRC, and suggesting its potential as a biomarker for predicting OXA response or as a target for therapeutic intervention (Supplementary Figure 3). Future studies can further validate the inhibitory effects and mechanisms of these drugs through in vitro experiments.

Figure 4 Drug binding affinity to leucine-rich repeat-containing 19 drug and molecular docking. A: Amuvatinib binding affinity to leucine-rich repeat-containing 19 (LRRC19) drug and molecular docking; B: Ciclopirox binding affinity to LRRC19 drug and molecular docking; C: Docetaxel binding affinity to LRRC19 drug and molecular docking; D: Elesclomol binding affinity to LRRC19 drug and molecular docking; E: JNK Inhibitor VIII binding affinity to LRRC19 drug and molecular docking; F: Piperlongumine binding affinity to LRRC19 drug and molecular docking; G: Sepantronium bromide binding affinity to LRRC19 drug and molecular docking; H: STF-62247 binding affinity to LRRC19 drug and molecular docking.

Impact of LRRC19 on the immune microenvironment in CRC

Figure 5A shows that the expression level of LRRC19 was significantly correlated with the degree of tumor infiltration by various immune cells in CRC. Specifically, LRRC19 expression was positively correlated with the infiltration levels of B cells (Rho = 0.159, P < 0.01), T cells/NK cells (Rho = 0.226, P < 0.001), and CD4⁺ T cells (Rho = 0.201, P < 0.001); whereas it showed a significant negative correlation with the infiltration levels of macrophages (Rho = -0.201, P < 0.001), mDCs (Rho = -0.16, P < 0.01), γδ T cells (Rho = -0.119, P < 0.05), neutrophils (Rho = -0.17, P < 0.01), NK cells (Rho = -0.367, P < 1 × 10^-7), and CD8⁺ T cells (Rho = -0.21, P < 0.001). Notably, the infiltration level of Tregs showed a weak positive correlation with LRRC19 expression (Rho = 0.158, P < 0.01). These results suggest that LRRC19 may influence the tumor immune response by regulating the infiltration of specific immune cells in the TME. Figure 5B reveals that in the pan-cancer analysis, the LRRC19 gene showed significant correlation with multiple immune checkpoint genes. Particularly in CRC, the correlation between the LRRC19 gene and several immune checkpoint genes was particularly significant, such as LRRC19's significant positive correlation with CD40 LG (r = 0.338, P < 1 × 10^-12), LGALS9 (r = 0.376, P < 1 × 10^-12), TMIGD2 (r = 0.357, P < 1 × 10^-12) genes, and significant negative correlation with CD44 (r = -0.341, P < 1 × 10^-12), CTLA4 (r = -0.286, P < 1 × 10^-7) genes. Figure 5C shows that in the pan-cancer analysis, LRRC19 exhibited a significant correlation with various immune-inflammatory pathways, which is particularly pronounced in CRC. Specifically, the expression level of LRRC19 was significantly positively correlated with the B cell receptor signaling pathway (cor = 0.199, P < 0.001), chemokine signaling pathway (cor = 0.107, P < 0.05), FC epsilon RI signaling pathway (cor = 0.231, P < 0.001), FC gamma R-mediated phagocytosis (cor = 0.118, P < 0.05), leukocyte transendothelial migration (cor = 0.102, P < 0.05), and T cell receptor signaling pathway (cor = 0.101, P < 0.05). These findings suggest that LRRC19 may play a significant role in the regulation of the tumor immune microenvironment. The significant positive correlation between LRRC19 and TNFRSF14, with a correlation coefficient of 0.61, indicated that LRRC19 might influence CD8 T cell exhaustion through the TNFRSF14 pathway. Additionally, LRRC19 was positively correlated with TOX, PRF1, GZMB, CTLA4, HAVCR2, LAG3, TIGIT, BTLA, ETV7, IFNG, TNF, PDCD1, HNF1A, and NR4A1, although these correlations were weaker. Conversely, LRRC19 was negatively correlated with TIGIT, HNF1A, CD274, PDCD1 LG2, and ICOSLG, suggesting an antagonistic relationship between LRRC19 expression and these genes (Supplementary Figure 4). These findings provide new insights into the potential interplay between LRRC19 and CD8 T cell exhaustion in CRC.

Figure 5 Leucine-rich repeat-containing 19 expression correlates with immune cell infiltration and immune checkpoint genes in colorectal cancer. A: Leucine-rich repeat-containing 19 (LRRC19) expression correlates with immune cell infiltration levels, showing positive correlations with B cells, T cells/natural killer (NK) cells, and CD4⁺ T cells, and negative correlations with macrophages, myeloid dendritic cells, γδ T cells, neutrophils, NK cells, and CD8⁺ T cells. A weak positive correlation is observed with regulatory T cells (Tregs); B: LRRC19 gene correlates significantly with immune checkpoint genes, particularly in colorectal cancer (CRC), showing significant correlations with cluster of differentiation 40 ligand (CD40 LG), galectin-9 (LGALS9), transmembrane and immunoglobulin domain containing 2 (TMIGD2), CD44, and cytotoxic T-lymphocyte associated protein 4 (CTLA4); C: LRRC19 expression correlates with immune-inflammatory pathways, with significant positive correlations in CRC for pathways like B cell receptor signaling, chemokine signaling, and T cell receptor signaling.

Single-cell analysis of CRC reveals LRRC19 expression correlation with cell types

Figure 6A shows that normal and tumor data partially overlap in the "nFeature_RNA" dimension, but the median was higher in the tumor group, suggesting more significant mitochondrial characteristics in their cells; the "nCount_RNA" also showed an overall higher trend in the tumor group, which may reflect an increase in metabolic activity and proliferation rate, while "percent.mt" indicated that both groups were mainly concentrated in the lower percentage range. Figure 6B indicated a weak negative correlation (r = -0.08) between mitochondrial gene proportion (percent_mt) and total mRNA molecule count (nCount_RNA), whereas nFeature_RNA and nCount_RNA showed a strong positive correlation (r = 0.92), indicating a significant increase in the number of features as the number of mRNA molecules increases. In Figure 6C, genes such as CD74 and HLA-DRA had higher positive loadings on PC1, whereas genes like GZMA and FGL2 displayed negative loadings on PC2, reflecting their expression differences across samples. Figure 6D shows that the P values for principal components PC1 to PC9 were extremely low, indicating they effectively captured the main variations in the single-cell population. Figure 6E demonstrates that the eigenvalues of the first nine principal components were significantly higher than those of the subsequent components, suggesting they were sufficient to explain the data's main structure. Figure 6F shows a trend of separation in gene expression space between normal samples (bm1-3) and tumor samples (bm4-6). Figure 6G further displays that normal and tumor samples formed distinct clusters on PC_1 to PC_6, confirming significant differences in gene expression profiles between patients with CRC and healthy individuals. Figure 6H highlights key genes such as apolipoprotein E and TP53 binding protein 2 that may be involved in the development of colon cancer. Figure 6I presents the expression levels of different MarkerGenes across various cell types. The color and position of the points in Figure 6J correspond to the 22 cell classifications and their two-dimensional spatial relationships. Figure 6K shows the trend of quantity changes for specific cell types in tumor tissues. Figure 6L illustrates that the six samples exhibited distinct cluster distributions in the t-SNE space. Figure 6M distinguishes 21 cell clusters by color. Figure 6N labels six major cell types: NK cells, T cells, macrophages, B cells, epithelial cells, and fibroblasts. Figure 6O shows the high expression of LRRC19 in B cells, macrophages, epithelial cells, and fibroblasts.

Figure 6 Single-cell RNA analysis in normal and tumor groups. A: Tumor group shows higher mitochondrial features and metabolic activity; B: Mitochondrial gene proportion vs mRNA count correlation; C: Gene loadings on first principal component (PC1) and PC2; D: Significance of PCs in variation capture; E: Eigenvalues of PCs explaining data structure; F: Gene expression separation (normal vs tumor); G: Normal/tumor clusters on PC1-6; H: Key colon cancer-associated genes; I: MarkerGenes expression across cell types; J: Cell classification and spatial distribution; K: Cell type quantity changes in tumors; L: t-distributed Stochastic Neighbor Embedding (t-SNE) cluster distribution; M: 21 color-coded cell clusters; N: Major identified cell types; O: Leucine-rich repeat-containing 19 expression in specific cell types.

LRRC19 suppresses CRC cell proliferation in vitro and in vivo

Figures 7A and B reveal that a marked enhancement of protein bands was indeed observed in the cell groups with stable overexpression of LRRC19 compared to the untreated control group or the cells transfected with the empty vector. This indicates that the target gene had been effectively expressed. Consequently, stable overexpression models of LRRC19 in two different CRC cell lines were successfully established, laying the foundation for further exploration of its role in tumor development and progression. The results showed that, compared to the vector control group, the number of colonies formed on soft agar by LRRC19-transfected HCT116 cells (Figure 7C and D) and HT29 cells (Figure 7E and F) was significantly reduced (P < 0.05), suggesting that LRRC19 effectively inhibits the non-adherent growth of these two cell types. The monolayer proliferation assay demonstrated that LRRC19 overexpression significantly suppressed cell growth, with the most pronounced effect observed on day 5. In HT-29 and HCT116 cells, the optical density values were significantly lower than the control group (P < 0.05; Figure 7G and H). The results indicate that, compared to the control group, the HCT116 cells overexpressing LRRC19 formed tumors with significantly reduced volume and weight (P < 0.05; Figure 7I-K), demonstrating that LRRC19 has a significant tumor-suppressive effect. Overall, these findings align with previous reports describing the antiproliferative role of LRRC19 in gastrointestinal malignancies, highlighting its tumor-suppressive functions mediated through intrinsic cellular mechanisms and TME regulation.

Figure 7 Leucine-rich repeat-containing 19 inhibits colorectal cancer cell proliferation in vitro and in vivo. A and B: Stable overexpression of leucine-rich repeat-containing 19 (LRRC19) plasmids and vector controls in HT29 and HCT116 cells confirmed by Western blotting; C-F: Anchorage-independent colony formation assays evaluating the effect of LRRC19 overexpression in HT29 and HCT116 cells; G and H: ATP-based cell proliferation assays assessing monolayer cell growth rates; data are shown as the mean ± SD (^bP < 0.05 compared with vector controls); I-K: Xenograft tumor growth in BALB/c nude mice subcutaneously injected with LRRC19-overexpressing or vector-control HCT116 cells. Tumors were excised, photographed, and weighed 4-5 weeks post-injection. Data are presented as the mean ± SD (^bP < 0.05 compared with controls).

LRRC19 induces cell cycle arrest via CDK6 downregulation

Figure 8A and B illustrates that, compared to the control, the proportion of LRRC19-overexpressing HCT116 cells in the G0/G1 phase increased from 65.31% to 72.75%, while HT29 cells increased from 51.69% to 58.44%, indicating a significant induction of cell cycle arrest in the G0/G1 phase by LRRC19 overexpression. Figure 8C demonstrates that under specific conditions, the protein level of CDK6 was significantly reduced (P < 0.05), while the expression levels of CDK2, CDK4, cyclin A2, cyclin B1, cyclin D1, and cyclin E2 remained stable. To investigate the functional involvement of CDK6 in the LRRC19-mediated suppression of CRC proliferation, we performed targeted transfection of CDK6 expression plasmids and matched control vectors into CRC cells with stable LRRC19 overexpression (Figure 8D). Figure 8E and F show that, compared to the control group overexpressing LRRC19 alone, cells simultaneously overexpressing CDK6 exhibited a significant increase in colony formation number on soft agar (P < 0.05), suggesting that CDK6 can partially restore the anchorage-independent growth capacity suppressed by LRRC19 overexpression. Additionally, flow cytometry data revealed that ectopic expression of CDK6 also promoted the release of cells from the G0/G1 phase into the S phase, thereby accelerating the progression of the entire cell cycle (P < 0.05; Figure 8G). These findings suggest that CDK6 may play a crucial role in regulating the proliferation of tumor cells.

Figure 8 Leucine-rich repeat-containing 19 induces G0/G1 cell cycle arrest by regulating cyclin-dependent kinase 6 expression. A and B: Cell cycle distribution analysis via flow cytometry in leucine-rich repeat-containing 19 (LRRC19)-overexpressing HCT116 and HT29 cells; C: Western blot analysis evaluating protein levels of cell cycle regulators (cyclin A2, cyclin B1, cyclin D1, cyclin E2, cyclin-dependent kinase 2 (CDK2), CDK4, and CDK6); glyceraldehyde-3-phosphate dehydrogenase (GAPDH) used as a loading control; D: Validation of CDK6 overexpression in HCT116 (LRRC19) cells transfected with pEGFP-CDK6 plasmids via Western blotting; E and F: Anchorage-independent growth assays demonstrating effects of CDK6 rescue in LRRC19-overexpressing cells; G: Flow cytometry analysis assessing cell cycle progression in CDK6-rescued LRRC19-overexpressing HCT116 cells. Data are presented as the mean ± SD. ^bP < 0.05 compared with respective controls.

LRRC19 transcriptionally represses CDK6 via E2F1 inactivation

Figure 9A and B reveal that in HCT116 cells, the relative expression of CDK6 mRNA in the LRRCl9 group was significantly decreased compared to the Vector group, with a statistically significant difference (P < 0.05). Similarly, a comparable phenomenon was observed in HT-29 cells, where the relative expression of CDK6 mRNA in the LRRCl9 group was also significantly reduced compared to the Vector group, and this change reached statistical significance (P < 0.05). These data suggest that LRRC19 may inhibit the transcription or translation process of the CDK6 gene in these two cancer cell lines through some mechanism, thereby affecting its protein expression level. Luciferase reporter assays confirmed transcriptional repression, showing decreased CDK6 promoter activity with LRRC19 overexpression (Figure 9C and D). Figure 9E identifies multiple potential E2F1 binding sites within the CDK6 promoter region, the presence of which implies that E2F1 may play a significant role in the transcriptional regulation of the CDK6 gene. Subsequent protein analyses revealed selective downregulation of E2F1 (P < 0.05), while the expression levels of other transcription factors, including Sp1, ETS-1, FOXP3, and ELK-1, remained unaffected (Figure 9F). Figure 9G demonstrates a negative correlation between LRRC19 and the cell cycle-related genes CDK6, E2F1, and CCND1. ChIP assays revealed that LRRC19 overexpression significantly reduced E2F1 binding to the CDK6 promoter (P < 0.05; Supplementary Figure 5A). Western blot analysis further demonstrated a marked reduction in total E2F1 protein levels (P < 0.05) without affecting its phosphorylation status (Supplementary Figure 5B). Immunofluorescence imaging confirmed diminished nuclear localization of E2F1 in LRRC19-overexpressing cells, evidenced in co-localization with the nuclear marker DAPI (P < 0.01;Supplementary Figure 5C). Collectively, these findings establish that LRRC19 inhibits E2F1-mediated transcriptional activation.

Figure 9 Leucine-rich repeat-containing 19 inhibits colorectal cancer proliferation via transcriptional suppression of E2F1 quantitative PCR analysis of cyclin-dependent kinase 6 mRNA expression levels in leucine-rich repeat-containing 19-overexpressing HCT116 and HT29 cells compared to vector controls. A and B: Quantitative PCR analysis of cyclin-dependent kinase 6 (CDK6) mRNA expression levels in leucine-rich repeat-containing 19 (LRRC19)-overexpressing HCT116 and HT29 cells compared to vector controls; C and D: Dual luciferase reporter assays evaluating CDK6 promoter activity; data are shown as the mean ± SD from triplicate experiments; E: Prediction of potential transcription factor binding sites within the CDK6 promoter region (-2000 to +1) using the TRANSFAC 8.3 engine; F: Western blot analysis of transcription factors Sp1, ETS-1, forkhead box P3 (FOXP3), ETS-like 1 (ELK-1), and E2F1; α-tubulin served as loading control; G: Scatter plots depicting correlation analyses between LRRC19 and key cell cycle-associated genes. ^bP < 0.05.

Chemosensitivity to OXA in CRC

OXA is the most commonly used chemotherapeutic agent in the clinical treatment of CRC[23], and when CRC cells increase their proliferation and metastasis, they become resistant to OXA chemotherapy. Experimental investigations demonstrated that OXA treatment induced an upregulation of LRRC19 protein expression in HCT116 and HT29 cells (P < 0.05; Figure 10A and B). Functional validation revealed that LRRC19 overexpression significantly sensitized CRC cells to OXA, with IC₅₀ values decreasing in HCT116 (P < 0.05) and HT29 (P < 0.05) after 48-hour treatment (Figure 10C-F). These findings collectively identify LRRC19 as both a predictive biomarker for OXA response and a therapeutic target to overcome chemoresistance in CRC.

Figure 10  Overexpression of leucine-rich repeat-containing 19 sensitizes colorectal cancer cells to oxaliplatin. A and B: Western blot analysis of leucine-rich repeat-containing 19 (LRRC19) protein expression in HCT116 and HT29 cells treated with various concentrations (0, 5, 10 μM and 0, 20, 40 μM) of oxaliplatin for 48 hours; C-F: Morphological assessment under inverted microscopy of cell death induced by oxaliplatin treatment at concentrations of 0, 5, 10, 20, 40, and 80 μM in HCT116 (Vector), HCT116 (LRRC19), HT29 (Vector), and HT29 (LRRC19) cells after 48 hours of exposure.

DISCUSSION

Despite advancements in therapeutic strategies, CRC remains challenging to treat, particularly in metastatic disease, where the 5-year survival rate is below 15%. Identifying novel molecular targets is thus crucial for improving CRC prognosis and therapeutic efficacy. In this study, we established that LRRC19 is recurrently downregulated in CRC (Figure 2A and E). Next, we determined whether its loss correlates with aggressive clinical features. Survival analysis revealed that patients with LRRC19-low disease exhibited significantly reduced OS (P < 0.05) but comparable DFS (P = 0.092; Figure 3A). This disparity suggests that LRRC19 depletion may not drive early recurrence but exacerbates late-stage mortality through mechanisms such as immune evasion or chemoresistance (explored in Figures 4-10). LRRC19 loss promotes CRC progression via two interconnected axes: (1) Cell cycle dysregulation-LRRC19 deficiency activates CDK6/E2F1 signaling (Figures 7-9), accelerating G1/S transition and uncontrolled proliferation; and (2) Immune suppression-LRRC19 depletion recruits immunosuppressive Tregs while depleting cytotoxic CD8⁺ T cells (Figure 5A), fostering an immune-evasive niche. These dual pathways collectively drive poor prognosis in patients with LRRC19-low disease (Figure 3B).

While LRRC19 low expression was associated with significantly worse OS (P < 0.05), no statistical differences were observed in DFS or DFI (P > 0.05). This apparent paradox may arise from several factors. First, OS reflects cumulative mortality from both cancer progression and non-cancer causes (e.g., chemotherapy toxicity, comorbidities), whereas DFS/DFI specifically captures tumor recurrence. The lack of DFS/DFI association suggests that LRRC19 may not directly regulate early recurrence but instead influences long-term survival through mechanisms such as therapy resistance or immune exhaustion (e.g., reduced CD8⁺ T cell infiltration; Figure 5A). Second, our cohort’s median follow-up time (15-30 months) may be insufficient to detect DFS/DFI differences, as CRC recurrence often occurs beyond 5 years. Third, clinical heterogeneity (e.g., adjuvant therapy regimens, microsatellite instability status) could dilute DFS/DFI signals, whereas OS aggregates broader risk factors. Future studies with extended follow-up and stratified analyses (e.g., by treatment type) are needed to resolve this discrepancy.

The CDK family plays a pivotal role in cell cycle regulation, with CDK6 being a key member. CDKs regulate cyclins, thereby influencing various cell cycle processes. Abnormal activation of cell cycle-related proteins can lead to excessive cell proliferation and tumorigenesis[28]. Consequently, CDKs have become prominent targets for inhibitors in cancer therapy[29,30]. CDK4 and CDK6, in conjunction with D-type cyclins, drive the transition from the G1 to S phase through RB phosphorylation and E2F transcription factor release. Dysregulation of the CDK4/6 axis, including gene amplification, mutations, and upstream signaling pathway activation, is observed in various cancers. Unlike broad-spectrum CDK4/6 inhibitors (e.g., palbociclib, ribociclib, abemaciclib)[31,32] employed in breast cancers, LRRC19 specifically targeted CDK6 without affecting other cyclins or kinases, indicating superior target specificity. In this study, CDK6 was the only kinase downregulated in two CRC cell lines overexpressing LRRC19, while CDK4 and other cell cycle-related proteins remained unchanged. This suggests that LRRC19 specifically suppresses CDK6 expression, inhibiting the G0/G1 to S phase transition in CRC cells. This regulatory relationship positions LRRC19 as a potential novel therapeutic target for CRC.

E2F1, a critical transcription factor, plays a central role in cell cycle regulation, DNA damage repair, and apoptosis[33,34]. By interacting with cell cycle regulators like Rb protein, E2F1 modulates the expression of multiple genes, driving cell proliferation[33-37]. Aberrant activation of E2F1 often leads to excessive cell proliferation and tumor progression[38,39]. Involvement of E2F1 in nuclear transcription factor Y subunit beta-E2F1-checkpoint kinase 1 signaling has been reported to contribute to OXA resistance in CRC[40]. Many drugs exert anti-CRC effects by affecting E2F1 expression. For example, downregulation of E2F1 by HGF and downregulation of E2F1 by inhibition of HSP90 in CRC cells can enhance the efficacy of 5-FU[41]. Nano-liposome-encapsulated small interfering RNA and others can downregulate E2F1, inhibit CRC cell growth, or enhance chemotherapy effect[42]. Our study showed that overexpression of LRRC19 inhibited CDK6 transcription by downregulating E2F1, although the exact binding site between LRRC19 and E2F1 remains unknown. Our study confirmed that LRRC19 regulates two genes with important biological functions. Clinicians may consider developing therapeutic strategies against LRRC19 (e.g., gene therapy, small molecule drugs) to regulate the expression of LRRC19 and enhance the response of CRC cells to chemotherapy.

By integrating predictive results from TargetScan, miRDB, and miRanda databases, we identified several miRNAs (e.g., hsa-miR-186-5p, hsa-miR-223-3p) potentially targeting LRRC19. miRNAs are known to regulate gene expression post-transcriptionally by binding to the 3' untranslated region (3'-UTR) of target mRNAs, thereby suppressing their stability or translation efficiency[43]. We hypothesize that these miRNAs may interact with the 3’-UTR of LRRC19 to modulate its expression levels. Notably, miR-186-5p[44] and miR-223-3p[45] have been implicated in various cancers, suggesting their potential involvement in CRC pathogenesis through LRRC19 regulation. Using the spongeScan tool, we further analyzed lncRNAs that could serve as competing endogenous RNAs for these miRNAs. Several lncRNAs, including AC005614.3, DYX1C1-CCPG1, and LA16C60D12.2, were predicted to form intricate regulatory networks with the identified miRNAs. LncRNAs typically function as "molecular sponges" to sequester miRNAs, thereby alleviating their inhibitory effects on target mRNAs[46].

The significant negative correlations between LRRC19 and immune checkpoint molecules CTLA4 and CD274/PD-L1 suggest that elevated LRRC19 expression may counteract T cell dysfunction by downregulating these inhibitory signals, thereby enhancing anti-tumor immunity[47,48]. This observation aligns with LRRC19-associated enrichment of plasma cells and activated dendritic cells in the TME. Plasma cells mediate humoral immunity through antibody secretion, while activated dendritic cells serve as central orchestrators of antigen presentation, potentially synergizing to amplify adaptive immune responses. The elevated TIDE scores (indicative of immune escape potential) in patients with LRRC19-low disease further corroborate its role as an immunostimulatory molecule[49,50]. Notably, LRRC19 expression negatively correlated with immunosuppressive cell populations, including M0/M1/M2 macrophages and Tregs, implying its capacity to restrict the recruitment or polarization of these cells, thereby enhancing TME immunogenicity[51]. Intriguingly, the inverse correlation between LRRC19 and CD8+ T cells may reflect functional exhaustion of cytotoxic T lymphocytes (e.g., high PD-1 expression) in CRC. Following this, we analyzed the correlation between LRRC19 and the aforementioned markers associated with CD8+ T cell exhaustion. Strikingly, LRRC19 expression exhibited a significant positive correlation with TNFRSF14 (Pearson’s r = 0.61, P < 0.001; Supplementary Figure 4), a co-stimulatory receptor implicated in T cell dysfunction and tumor immune evasion. We suspect that LRRC19 may promote CD8⁺ T cell exhaustion via TNFRSF14-mediated signaling, potentially through impaired effector function or enhanced inhibitory checkpoint activation. To dissect the heterogeneity of LRRC19-associated immune subsets, single-cell RNA sequencing will be employed in future studies[52]. This approach will resolve distinct CD8⁺ T cell states (e.g., progenitor exhausted vs terminally exhausted) and their spatial interplay within the TME, thereby elucidating context-specific LRRC19 regulatory networks. Analysis of the GSE139555 dataset revealed elevated LRRC19 expression in myofibroblasts, key stromal cells in the TME that regulate immune cell infiltration through extracellular matrix (ECM) and chemokine secretion (e.g., C-X-C motif chemokine ligand 12)[53]. Studies have shown that transforming growth factor beta signaling drives the development of LRRC15+ CAFs, which suppress CD8+ T cell function and limit the efficacy of immune checkpoint blockade. Targeting LRRC15+ CAFs can restore fibroblast homeostasis, enhance anti-tumor immunity, and improve immunotherapy outcomes. LRRC19 exerts dual functions by suppressing immune checkpoint molecules and reshaping immune cell infiltration patterns, highlighting its potential as a therapeutic target for combination treatment strategies. Our data demonstrate that LRRC19 suppresses cytotoxic CD8⁺ T cell infiltration while enhancing immunosuppressive Treg recruitment (Figure 4A). This dichotomy mirrors its dual roles in other pathologies: Xie et al[15] reported that LRRC19 deficiency exacerbates colitis-associated tumorigenesis via NF-κB-driven inflammation, while in CRC, LRRC19 promotes tumor progression through TRAF6/TAK1/NF-κB activation[12]. These parallels suggest a conserved TLR-like mechanism, where LRRC19 engages myeloid differentiation primary response 88 (MyD88)/NF-κB signaling to reshape the CRC immune landscape, though direct evidence remains lacking. While our current study focuses on LRRC19’s role in CDK6/E2F1-mediated cell cycle regulation and immune modulation (Figures 7-10), we acknowledge the need to validate its potential engagement with TLR-like pathways, such as MyD88/NF-κB signaling. To address this, future work will include co-immunoprecipitation assays to test direct LRRC19-MyD88 interactions in CRC cells, MyD88-knockout models to determine whether LRRC19’s immunosuppressive effects (e.g., Treg upregulation and CD8⁺ T cell suppression) are MyD88-dependent, and NF-κB luciferase reporter assays to quantify pathway activity in LRRC19-overexpressing cells. These experiments will elucidate whether LRRC19 exploits canonical TLR machinery to remodel the tumor immune microenvironment, bridging its structural homology to TLRs with functional relevance in CRC progression.

Chemoresistance remains a major obstacle in cancer therapy[54]. While our computational models identified LRRC19-binding drugs with potential therapeutic relevance (Figure 3), experimental validation (e.g., surface plasmon resonance, dose-response assays) is required to confirm binding kinetics and functional efficacy. Future studies will address this limitation. OXA is a cornerstone therapy in CRC, widely used in metastatic first-line. Our study found that LRRC19 overexpression significantly increased the sensitivity of CRC cells to OXA. This finding underscores LRRC19’s critical role in overcoming tumor immune evasion. Mechanistically, chemotherapy-induced upregulation of LRRC19 may reverse EMT or inhibit pro-survival signaling pathways, thereby overcoming drug resistance. These results provide a theoretical basis for optimizing chemotherapy regimens based on LRRC19 expression. These concentrations align with established in vitro models of CRC chemoresistance. Clinically, OXA is typically delivered via intravenous infusion at 85 mg/m² every 2 weeks (equivalent to approximately 1.5-3 μM plasma concentration). While our in vitro doses are higher than clinical plasma levels, this accounts for the shorter exposure time (48 hours vs sustained circulation in vivo) and compensates for reduced drug penetration in monolayer cultures. Future studies will validate these findings in patient-derived organoids and murine xenograft models with clinically relevant dosing schedules.

Although this study elucidates the multifaceted functions of LRRC19, its specific ligands and downstream signaling pathways require further investigation. For instance, does LRRC19 directly regulate anti-tumor immune responses by recruiting immune cells such as T cells? Additionally, how does the lncRNA/miRNA regulatory network influence LRRC19 expression? Future research should integrate single-cell sequencing and organoid models to explore LRRC19’s dynamic role in the TME and develop small-molecule agonists or gene therapy strategies targeting LRRC19.

CONCLUSION

This work establishes LRRC19 as a pleiotropic tumor suppressor in CRC, bridging cell cycle control, immune microenvironment remodeling, and chemotherapeutic response. Its dual mechanisms and stromal specificity position LRRC19 as both a predictive biomarker and a novel therapeutic target for precision oncology approaches. Clinical translation should prioritize combinatorial regimens integrating LRRC19 modulation with existing immunotherapies or chemotherapy protocols.