Research on the function of HGH1 in breast cancer remains lacking.

TCGAand GEO (GSE45827) datasets investigated discrepancies in HGH1 expression in BC. An aggregate of 106 clinical samples were gathered through immunohistochemistry, KM curves were drawn for prognostic analysis, and the function of HGH1 of BC was predicted. Finally, the effects of HGH1 knockdown on MDA-MB-231 and MCF-7 BC cells were verified via CCK8, invasion, wound healing and colony formation assays.

HGH1 is highly expressed in BC and is linked to unfavorable prognosis. HGH1 overexpression is connected to keratinization and the cell cycle and is closely related to ER and PR expression and tumor stage in BC patients. Knocking down HGH1 in BC cells inhibited the viability, invasion and migration.

Knockdown of HGH1 in breast cancer cell lines can inhibit the viability, invasion and migration of tumor cells.

Caspases (CASPs) are attractive targets for cancer therapy. Many prognostic models based on gene signatures include genes from the CASPs family in diffuse glioma. CASP3, CASP4 and CASP6 in glioma have been studied individually. However, specialized comprehensive analysis of the roles of CASPs family in glioma is lacking. Therefore, this study utilized bioinformatics methods to investigate this issue. CASP1-10 expressionlevels were significantly up-regulated in LGG and GBM and glioma, and varied significantly across different clinical subgroups of glioma and LGG and various cell types, and most of CASP1-10 members showed significant differences in recurrence status of LGG. 10 signatures (CASP1-10) were associated with poor overall survival (OS) in glioma and LGG and GBM. However, pan-cancer survival analysis showed that CASP1-10 were associated with the prognosis of LGG, but not GBM. CASP1-10 were related to poor prognosis of glioma and LGG, except for CASP9, which was the opposite of a protective factor. CASP1-10 were independent prognostic factors for OS in glioma and LGG, except for CASP5, and also for recurrence-free survival (RFS) in LGG. Most of CASP1-10 were also independent prognostic factors for disease-specific survival (DSS) and progression-free interval (PFI) and had diagnostic value in glioma and LGG. Genetic alterations of CASP1-10 genes set were associated with poor prognosis in LGG. CASP1-10 were involved in immune infiltration and programmed cell death in glioma and LGG and GBM, and might promote the apoptosis of immune cells. Compared to GBM, CASP1-10 had a more significant impact on the prognosis, cancer-related pathways, and immune infiltration in LGG, indicating that CASP1-10 might play important roles in the recurrence and progression of LGG, and might be promising therapeutic targets for LGG. Therefore, it is speculated that natural caspase inhibitor p35 may be a promising drug for the treatment of glioma, especially for LGG.

Thyroid cancer, the most common endocrine malignancy, has seen a significant rise in incidence, necessitating improved diagnostic and prognostic methods. Despite advancements in fine-needle aspiration biopsy (FNAB) and molecular mutation detection, these techniques have limitations, particularly in large nodules. This study aims to identify molecular markers and construct a comprehensive ceRNA regulatory network to enhance thyroid cancer diagnosis and prognosis. Using transcriptomic data from TCGA, GTEx, and GEO datasets, we performed differential expression analysis and WGCNA to identify key lncRNAs, miRNAs, and mRNAs involved in thyroid cancer. Gene Ontology and KEGG pathway analyses elucidated the biological functions and pathways of these genes. A ceRNA network was constructed, highlighting the interactions between 32 lncRNAs, 18 miRNAs, and 56 mRNAs. Survival analysis and the Cibersort algorithm further revealed the relationship between ceRNA regulatory networks and immune cell infiltration. A prognostic risk model was developed, incorporating key prognostic genes (PRR15, HCP5, and DUXAP8) and immune cells (resting NK cells, monocytes, M0 macrophages, and activated mast cells). DUXAP8 was positively correlated with activated mast cells and monocytes, while HCP5 was negatively correlated with resting NK cells. This study provides new insights into thyroid cancer pathogenesis, suggesting potential molecular markers for early diagnosis and personalized treatment. Integrating ceRNA regulatory mechanisms with immune cell analysis offers a novel perspective on the tumor microenvironment's role in thyroid cancer progression.

Lysyl oxidases (LOX/LOXL1-4) are crucial for cancer progression, yet their transcriptional regulation, potential therapeutic targeting, prognostic value and involvement in immune regulation remain poorly understood. This study comprehensively evaluates LOX/LOXL expression in cancer and highlights cancer types where targeting these enzymes and developing LOX/LOXL-based prognostic models could have significant clinical relevance.

We assessed the association of LOX/LOXL expression with survival and drug sensitivity via analyzing public datasets (including bulk and single-cell RNA sequencing data of six datasets from Gene Expression Omnibus (GEO), Chinese Glioma Genome Atlas (CGGA) and Cancer Genome Atlas Program (TCGA)). We performed comprehensive machine learning-based bioinformatics analyses, including unsupervised consensus clustering, a total of 10 machine-learning algorithms for prognostic prediction and the Connectivity map tool for drug sensitivity prediction.

The clinical significance of the LOX/LOXL family was evaluated across 33 cancer types. Overexpression of LOX/LOXL showed a strong correlation with tumor progression and poor survival, particularly in glioma. Therefore, we developed a novel prognostic model for glioma by integrating LOX/LOXL expression and its co-expressed genes. This model was highly predictive for overall survival in glioma patients, indicating significant clinical utility in prognostic assessment. Furthermore, our analysis uncovered a distinct LOXL2-overexpressing malignant cell population in recurrent glioma, characterized by activation of collagen, laminin, and semaphorin-3 pathways, along with enhanced epithelial-mesenchymal transition. Apart from glioma, our data revealed the role of LOXL3 overexpression in macrophages and in predicting the response to immune checkpoint blockade in bladder and renal cancers. Given the pro-tumor role of LOX/LOXL genes in most analyzed cancers, we identified potential therapeutic compounds, such as the VEGFR inhibitor cediranib, to target pan-LOX/LOXL overexpression in cancer.

Our study provides novel insights into the potential value of LOX/LOXL in cancer pathogenesis and treatment, and particularly its prognostic significance in glioma.

The eukaryotic initiation factor 5A2 gene (EIF5A2) is a highly conserved and multifunctional gene that significantly influences various cellular processes, including translation elongation, RNA binding, ribosome binding, protein binding and post-translational modifications. Overexpression of EIF5A2 is frequently observed in multiple cancers, where it functions as an oncoprotein. Additionally, EIF5A2 is implicated in drug resistance through the regulation of various molecular pathways. In the review, we describe the structure and functions of EIF5A2 in normal cells and its role in tumorigenesis. We also elucidate the molecular mechanisms associated with EIF5A2 in the context of tumorigenesis and drug resistance. We propose that the biological roles of EIF5A2 in regulating diverse cellular processes and tumorigenesis are clinically significant and warrant further investigation.

Gastric cancer (GC) remains a prevalent and aggressive malignancy worldwide, characterized by significant morbidity and mortality. The regulator of G-protein signaling 1 (RGS1) plays an oncogenic role in various cancers, including GC, but its clinical relevance and mechanisms remain underexplored. In this pilot study, we investigated RGS1 expression in GC tissues and its potential as a prognostic marker, laying the groundwork for future research. Our analysis of patient data from the TCGA data and our cohort of 375 surgically resected GC patients revealed that RGS1 was upregulated in GC tissues and had prognostic significance (TCGA: adjusted HR:1.49, 95%CI: 1.02-2.18; GC cohort: adjusted HR: 1.38, 95%CI: 1.02-1.85). GO function and KEGG enrichment analyses suggest that RGS1 is involved in macrophage-mediated immune responses in GC. We observed a positive correlation between RGS1 expression and M2 macrophage infiltration. Furthermore, co-occurrence of elevated RGS1 expression and M2 macrophage infiltration predicts a worse prognosis (adjusted HR: 1.73, 95%CI: 1.24-2.42 in our cohort). In vitro, RGS1 upregulation and the presence of M2 macrophages enhanced malignant phenotypes of GC cells. Additionally, we confirmed that RGS1 promoted macrophage recruitment and M2 polarization via upregulation of CCL4 expression in vivo. In conclusion, this study suggests that RGS1 could serve as a promising prognostic marker for GC and a potential target for immunotherapy. However, further investigation with more advanced experimental models is needed to confirm these preliminary findings.

B-cell lymphoma-2-associated transcription factor 1 (BCLAF1) is a versatile protein involved in the regulation of gene transcription and post-transcriptional processing. Although BCLAF1 exerts a broad tumor suppressor effect or tumor promoter effect in many cancer types, the specific roles concerning its expression levels, and its impact on tumorigenesis in Renal cell carcinoma (RCC) remain unclear. Here, we utilized the Cancer Genome Atlas (TCGA) and Genotype Tissue Expression (GTEx) datasets alongside R software and online tools to unravel the specific roles of BCLAF1 in 33 cancer types, including its expression levels, tumor immune and molecular subtypes, and its correlation with prognosis, diagnosis, DNA methylation, and immune microenvironment. Additionally, we carried out cell biology experiments to independently investigate the expression of BCLAF1 in RCC and its effects on tumor progression. BCLAF1 was differentially expressed in tumor tissues compared to normal tissues across various cancer types and was also differentially expressed in different immune and molecular subtypes. In RCC, patients with high BCLAF1 expression had a better prognosis and BCLAF1 was tightly correlated with the stage, gender, and histological grade of patients. Furthermore, BCLAF1 had higher DNA methylation levels and higher immune infiltration levels in tumor tissues. Additionally, cell functional experiments confirmed the low expression of BCLAF1 in RCC and that BCLAF1 significantly inhibited the proliferation, migration, and invasion, while inducing apoptosis and cell cycle arrest in RCC cells in vitro. Our study under-scored the potential of BCLAF1 as an important actor in tumorigenesis, especially concerning RCC where it may serve as an effective prognostic marker.

There is growing evidence linking neutrophil extracellular traps (NETs) to tumor genesis, growth, distant metastasis, and tumor-related thrombosis. However, the roles of NETs-related genes (NETRGs) on LGG prognosis remain unclear. The purpose of this study was to integrate multiple machine learning techniques and experiment validation to develop a reliable NETs-based signature that opens up novel approaches for assessing the prognosis and treatment response of LGG patients. Consensus clustering, k-means clustering and Nonnegative Matrix Factorization was used for the TCGA-LGG dataset and identified two NETs-related subgroups. The prognostic hallmark and nomogram for LGG were developed, which consist of five differentially expressed NETRGs (FPR1, PTAFR, SLC11A1, ICAM1, LTF) based on nine analytic approaches. The ROC curves and calibration curves of our NETRGs signature and nomogram exhibited strong and robust prognosis prediction abilities in both the TCGA-LGG training set and CGGA-325, CGGA-693 validation sets. The prognosis for LGG individuals in the low-risk category was better. The TISCH was used to examine the five NETRGs at the single-cell level. Common immunological checkpoints were expressed at greater levels in high-risk individuals. LGG individuals in the low-risk category posses a higher likelihood of being sensitive to Carmustine and Vincristine, as indicated by the drug sensitivity analysis. The qRT-PCR experiment and immunohistochemistry images confirmed that the expression of FPR1, PTAFR, SLC11A1 and ICAM1 are higher in low-grade oligodendroglioma. The NETRGs signature and nomogram can accurately and conveniently predict the LGG patients' prognosis, which can facilitate individualized treatment and the improvement of prognosis.

Given that hereditary prostate cancer (PCa) accounts for only a small fraction of PCa phenotypes, there is still a substantial journey ahead in exploring the somatic genetic drivers contributing to sporadic PCa. The expression quantitative trait loci (eQTLs) data were sourced from the GTEx dataset for prostate-specific genes, and the summary statistic information was collected for 5854 genes. Genetic associations with PCa were extracted from three well-established consortiums: the UK Biobank (9131 cases and 173,493 controls), the PRACTICAL study (79,148 cases and 61,106 controls), and the FinnGen cohort (13,216 cases and 119,948 controls). To prioritize potential causal targets, additional analysis, including the protein-protein interaction (PPI), The Cancer Genome Atlas (TCGA) dataset, and the single-cell-type expression analysis, was performed. Generally, a total of 150 common significant genes with the same causal association with PCa were identified. Out of the 150 genes examined, 67.33% (101/150) were found to have protein-coding functions, while only 30.67% (46/150) of these genes had prior mentions in the scientific literature. Notably, the analysis of the TCGA dataset showed that only 44.67% (67/150) of the genes produced consistent results with the Mendelian randomization (MR) analysis. Furthermore, the evaluation of single-cell RNA-seq data and colocalization analysis singled out MSMB as a critical gene associated with the occurrence of PCa. We pinpointed a range of prostate-specific genes that display causal associations with the onset of PCa. Among these, the MSMB gene emerged as a pivotal factor linked to PCa, demonstrating robust consistency across all four assessments, including the MR, TCGA dataset, single-cell RNA-seq data, and colocalization analysis. These findings provided fresh perspectives on the pathogenesis of PCa and presented potential targets for drug development.

Gastric cancer, characterized by rising global incidence and mortality, faces significant challenges due to the lack of effective early detection methods, delaying timely interventions and underscoring the need for novel biomarkers. Lipopolysaccharide-binding protein (LBP), implicated in cancers such as lung, colon, and cervical cancer, has emerged as a promising candidate. However, its specific roles and mechanisms in gastric cancer remain unclear, necessitating further investigation.

This study utilized data from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the Human Protein Atlas (HPA) to assess LBP mRNA and protein expression levels in gastric cancer patients and explore their associations with clinical outcomes. Analytical techniques included volcano plots, protein-protein interaction networks, Gene Ontology (GO) analysis, Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis, gene set enrichment analysis (GSEA), and immune infiltration assessments. Furthermore, lentiviral vectors containing interference sequences targeting LBP were used to manipulate its expression in AGS and HGC-27 gastric cancer cell lines, enabling the analysis of gene knockdown effects on malignant behaviors. Western blotting (WB) was performed to validate the impact of LBP knockdown on the expression of key signaling pathway proteins.

Our pan-cancer comparative analysis across 33 cancer types revealed significant upregulation of LBP in gastric cancer, with diagnostic ROC curve analysis yielding an AUC of 0.765. Univariate and multivariate Cox regression analyses revealed that high LBP expression was inversely related to patient survival. Additionally, immune infiltration and functional enrichment analyses revealed the involvement of LBP in pathways crucial to cancer development, such as immune response modulation and lipid metabolism. LBP knockdown in gastric cancer cell lines reduced proliferation, migration, and invasion. WB confirmed decreased expression of P65, P-P65, STAT3, and P-STAT3 upon LBP knockdown.

LBP is intricately linked to gastric cancer pathogenesis; it influences cell proliferation, migration, and invasion, thereby representing a valuable prognostic and diagnostic biomarker. This study not only highlights the potential of LBP as a therapeutic target but also provides the groundwork for future investigations into its mechanistic pathways in gastric cancer.

Not applicable. I would like to clarify that our research does not fall under clinical studies and therefore does not involve ethical concerns related to human or animal subjects. The cells used in this study are established cell lines purchased from a certified biotechnology company. All experimental procedures comply with standard research protocols and guidelines for cell line studies.

Ovarian cancer (OC) is a deadly gynecologic cancer associated with metastasis, recurrence, and treatment resistance. The expression of the alanine-serine-cysteine transporter 2 (ASCT2) has been linked to poor prognosis and immune cell infiltration in OC tumors, but the underlying mechanisms are unclear. Lentiviral constructs were used to manipulate ASCT2 expression in OC cells (SKOV-3). The effects of ASCT2 on SKOV-3 behaviors including proliferation, invasion, migration, apoptosis, and cell cycle were assessed using various assays. The correlation between ASCT2 expression and immune infiltration in OC was analyzed using the Cancer Genome Atlas (TCGA) database. Co-culture experiments were conducted to evaluate the impact of ASCT2 overexpression in SKOV-3 on NK cells, followed by transcriptomics and cytokine analysis. ASCT2 expression and cytokine levels were characterized using qPCR and western blotting. ASCT2 overexpression significantly promoted cell proliferation, invasion, migration, and the percentage of G1-phase cells, while inhibiting apoptosis. ASCT2 silencing had the opposite effect. The expression of ASCT2 was negatively associated with NK cells in OC. ASCT2 overexpression in SKOV-3 cells led to excessive IL-17A/F production and inhibited the antitumor activity of NK cells, possibly through activating the IL-17 signaling pathway. The core regulatory genes c-JUN/PTGS2 in this pathway were upregulated, and antitumor cytokines were decreased in co-cultured NK cells, resulting in decreased antitumor activity and immune infiltration within the tumor. Our results suggest that overexpression of ASCT2 may play a predominant role in OC and NK cell immune infiltration within the tumor.

With the low incidence of mutations in pediatric cancers, alternate genomic approaches are needed to identify therapeutic targets. Our study, the Comparative Analysis of RNA Expression to Improve Pediatric and Young Adult Cancer Treatment, was conducted by the UC Santa Cruz Treehouse Childhood Cancer Initiative and Stanford University School of Medicine. RNA sequencing data from 33 children and young adults with a relapsed, refractory or rare cancer underwent CARE analysis to reveal activated cancer driver pathways and nominate treatments. We compare our pipeline to other gene expression outlier detection approaches and discuss challenges for clinical implementation. Of our 33 patients, 31 (94%) had findings of potential clinical significance. Findings were implemented in 5 patients, 3 of which had defined clinical benefit. We demonstrate that comparator cohort composition determines which outliers are detected. This study highlights the clinical utility and challenges of implementing comparative RNA sequencing analysis in the clinic.

The SUSD3 protein, marked by the Sushi domain, plays a key role in cancer progression, with its expression linked to tumor advancement and patient prognosis. Altered SUSD3 levels could serve as a predictive biomarker for cancer progression. Recognized as a novel susceptibility marker, SUSD3 presents a promising target for antibody-based therapies, offering a potential approach for the prevention, diagnosis, and treatment of breast cancer.

Using the HPA and GeneMANIA platforms, the distribution of SUSD3 protein across tissues was analyzed, while expression levels in tumor and healthy tissues were compared using The Cancer Genome Atlas data. The TISCH and STOmics DB databases facilitated the mapping of SUSD3 expression in different cell types and its spatial relationship with cancer markers. Univariate Cox regression assessed the prognostic significance of SUSD3 expression in various cancers. Genomic alterations of SUSD3 were explored through the cBioPortal database. The potential of SUSD3 as a predictor of immunotherapy response was investigated using TIMER2.0, and GSEA/GSVA identified related biological pathways. Drugs targeting SUSD3 were identified through CellMiner, CTRP, and GDSC databases, complemented by molecular docking studies. In vitro experiments demonstrated that SUSD3 knockdown in breast cancer cell lines significantly reduced proliferation and migration.

SUSD3 expression variations in pan-cancer cohorts are closely linked to the prognosis of various malignancies. In the tumor microenvironment (TME), SUSD3 is predominantly expressed in monocytes/macrophages and CD4+ T cells. Research indicates a strong correlation between SUSD3 expression and key cancer immunotherapy biomarkers, immune cell infiltration, and immunomodulatory factors. To explore its immune regulatory role, StromalScore, ImmuneScore, ESTIMATE, and Immune Infiltration metrics were employed. Molecular docking studies revealed that selumetinib inhibits tumor cell proliferation. Finally, SUSD3 knockdown reduced cancer cell proliferation and migration.

These findings provide valuable insights and establish a foundation for further exploration of SUSD3's role in pan-carcinomas. Additionally, they offer novel perspectives and potential targets for the development of innovative therapeutic strategies in cancer treatment.

This study aims to explore whether tanshinone IIA can act on paclitaxel-resistant non-small cell lung cancer A549/Tax and analyze the possible mechanisms involved.

Using the Cell Counting Kit-8 (CCK-8), we preliminarily analyzed whether tanshinone IIA has an inhibitory effect on A549/Tax cells. We utilized public datasets, self-collected transcriptome datasets, and drug target analysis to identify potential targets. We employed real-time fluorescent quantitative polymerase chain reaction (RT-qPCR) to detect the expression of core genes before and after drug treatment to analyze potential target genes and validated them using data from The Cancer Genome Atlas (TCGA). We conducted enrichment analysis on co-expressed genes of the target genes to explore potential mechanisms. Furthermore, we employed molecular docking and western blot to verify the possible mechanisms involved.

The CCK8 results indicated that tanshinone IIA has a significant inhibitory effect on A549/Tax cells. The qPCR results and the analysis of TCGA data indicated that MMP7 is the target gene. Enrichment results of MMP7 co-expressed genes suggested that the PI3K-AKT signaling pathway might play a key role. Molecular docking results indicated that tanshinone IIA has strong binding activity with PI3K, AKT, mTOR, and MMP7. Western blotting results showed that tanshinone IIA might inhibit MMP7 through the PI3K-AKT-mTOR signaling pathway.

Tanshinone IIA may affect the proliferation of A549/Tax by influencing the expression of MMP7 through the PI3K-AKT-mTOR signaling pathway.

Background: Gastric cancer (GC) is a leading cause of mortality worldwide, particularly in China. Chronic atrophic gastritis (CAG) and intestinal metaplasia (IM) are recognized as precancerous conditions contributing to GC development. Qilianxiaopi formula (QLXP), a traditional Chinese medicine (TCM), has demonstrated significant therapeutic effects on CAG and IM; however, its underlying mechanisms remain poorly understood. Methods: This study utilized chromatography-mass spectrometry to identify the major compounds in QLXP. Network pharmacology was used to predict the associated targets of these components. Thermal proteome profiling (TPP) pinpointed the potential binding proteins of QLXP, which were validated by bioinformatic analyses. Bio-layer interferometry (BLI) was used to analyze the interactions between QLXP and its key target proteins, thereby determining their binding components. Molecular docking predicted the binding modes between the components and proteins. Results: ADAM17 was identified as a key binding protein for QLXP. Further investigation revealed that QLXP inhibits the enzymatic activity of ADAM17, thereby reducing the secretion of the pro-inflammatory cytokine TNF-α, contributing to the anti-inflammatory properties of QLXP. BLI confirmed direct and reversible binding interactions between QLXP and ADAM17. Narirutin, isolated from the ADAM17 binding fraction, displayed the highest affinity for QLXP. Conclusions: This study highlights ADAM17 as a key molecular target of QLXP and narirutin as its principal binding component. The integrated approach combining chromatography-mass spectrometry, network pharmacology, TPP, BLI, and molecular docking provides a robust framework for elucidating the mechanisms of action of TCM.

Currently, research on the role of hexokinase domain-containing protein-1 (HKDC1) in neoplasm metabolism remains sparse. This study seeks to conduct a thorough investigation of HKDC1's potential functions across thirty-three different tumor types, utilizing data obtained from The Cancer Genome Atlas (TCGA).

We conducted a thorough data extraction from the TCGA database, subsequently employing R (version 4.2.2) and its associated software packages for detailed analysis. Our investigation centered on evaluating the differential expression and prognostic significance of HKDC1, while also examining its connections to tumor heterogeneity, mutation profiles, and RNA modifications. Furthermore, we analyzed the relationship between HKDC1 expression and tumor immunity utilizing the TIMER analysis approach.

A comprehensive analysis of various tumor types has revealed that HKDC1 is significantly upregulated in many malignant tumors. Importantly, patients with elevated HKDC1 levels in their tumor tissues often experience poorer prognoses. The association between HKDC1 expression, immune cell infiltration, and the existence of immune checkpoints suggests a possible connection between the tumor microenvironment and HKDC1, alongside tumor advancement. Gene set enrichment analysis (GSEA) further substantiates the idea that HKDC1 may play a role in several critical pathways and biological processes associated with neoplasm. Additionally, the overexpression of HKDC1 is influenced by promoter methylation and alterations in DNA copy number amplification. Furthermore, in vitro experiments demonstrated that silencing HKDC1 resulted in a marked reduction in the proliferation, migration, and invasion capabilities of neoplasm cells.

Our initial pan-cancer analysis provided a comprehensive understanding of the oncogenic roles of HKDC1 across diverse cancer types. Moreover, HKDC1 has the potential to serve as a significant prognostic biomarker.

Colorectal cancer has high incidence and mortality rates. The signal transducer and activator of transcription (STAT) family plays vital roles in the tumorigenesis and development of colorectal cancer. The expression, prognostic value, and immune function of the STAT family are becoming much more clearly.

Our study collected data from several public data portals such as TCGA (644 samples) and GTEx database (308 samples) and clinical samples (30 samples, China). Then we systematically assessed the expression level and prognostic value of the STAT family in colorectal cancer samples. Moreover, the immune function and immune infiltration levels of prognosis-related STAT members were explored via single cell RNA-seq and spatial transcriptomics technology data. Several useful portals and tools have been utilized such as CancerSEA and TISIDB in single-cell analysis, CBio Cancer Genomics in multidimensional alterations, MethSurv in DNA methylation, and related R packages.

Our study found that STAT3 and STAT5B were significantly lower in colorectal cancer via multi-omics (P < 0.001). Higher STAT3 and STAT5B level were correlated with better future outcome. Nomograms were developed to predict the distal survival time (C-index = 0.724). The functions of STAT3 and STAT5B are associated with inflammation, the JAK/STAT pathway and the immune response. The major cell types of colorectal cancer were CD4Tconv, CD8T, CD8Tex, Tprolif, Treg and STAT3 and STAT5B widely expressed in these cells. STAT3 and STAT5B both correlated with CD244 and KDR for immune checkpoints.

STAT3 and STAT5B are downregulated in colorectal cancer and have great potential as prognostic biomarkers and novel immunotherapy targets.

Liver cancer, specifically hepatocellular carcinoma (LIHC), ranks as the second most common cause of cancer-related fatalities globally. Moreover, the occurrence rate of LIHC is steadily increasing. A recently identified gene, SPSB2, has been implicated in cell signaling, impacting the development and progression of non-small cell lung cancer. Nevertheless, studies on the role of SPSB2 in the pathogenesis of LIHC are lacking.

Using the TCGA, GTEx, and GEO databases, we obtained differentially expressed genes that affect the prognosis of patients with LIHC. We utilized the Kruskal-Wallis test, along with univariate and multivariate COX regression analyses, to determine the correlation between SPSB2 and patient clinical indicators. Potential biological functions of SPSB2 in LIHC were explored by enrichment analysis, ssGSEA, and Spearman correlation analysis. Finally, LIHC cell lines Huh7 and SMMC-7721 were used to validate the biological function of SPSB2.

The results showed LIHC patients with higher SPSB2 expression had a poorer prognosis, and SPSB2 expression was significantly correlated with LIHC patients' Histologic grade, Pathologic T stage, Prothrombin time, Pathologic stage, BMI, weight, adjacent hepatic tissue inflammation, AFP level, and OS event (p < 0.05). SPSB2 shows notable enrichment in pathways linked to tumorigenesis and the immune system. Moreover, its expression is strongly connected to immune cells and immune checkpoints. Knockdown of SPSB2 expression in Huh7 cells and SMMC-7721 cells inhibits SPSB2's biological functions, including proliferation, invasion, metastasis, and other phenotypes.

SPSB2 plays a crucial role in the development of LIHC. It is related to the immune response and unfavorable outcomes. SPSB2 may function as a clinical biomarker for prognosis.

Previous studies indicate that MAF BZIP Transcription Factor F (MAFF) facilitates ectopic metastasis and tumor cell migration. While its role in neoplasm progression is recognized, a thorough pan-cancer analysis of MAFF's impact remains pending.

MAFF expression across normal and tumor tissues was analyzed using transcriptomic data from Genomic Data Commons (GDC) and UCSC XENA, with protein details from Human Protein Atlas (HPA) and GeneMANIA. Tumor Immune Single-cell Hub (TISCH) and Spatial Transcriptomics Omics DataBase (STOmics DB) identified MAFF expression in the tumor microenvironment (TME). MAFF's prognostic significance and immune-related gene associations were evaluated through univariate Cox regression, TIMER2.0 immune cell infiltration analysis, and Spearman correlation. Critical pathways were identified using Gene Set Enrichment Analysis (GSEA) and Gene Set Variation Analysis (GSVA), while molecular docking explored anticancer agent interactions.

MAFF expression varies across cancers, affecting tumor prognosis, notably in monocytes/macrophages and endothelial cells. Copy number variation (CNV) positively correlates with MAFF expression, while methylation shows inverse correlation. MAFF mutations significantly affect LGG patient prognosis and correlate with immune therapy responses. ESTIMATE and immune profiling linked MAFF to immunosuppression pathways. Molecular docking identified MAFF-targeted drugs, with validated effects on breast cancer and endometrial cancer cell survival and migration in vitro.

Multi-omics analysis identified MAFF as a potential prognostic marker correlating with tumor immunity and microenvironment, suggesting its value for personalized cancer immunotherapy, particularly in BRCA and UCEC.

As the biological roles and disease implications of non-coding RNAs continue to emerge, the need to thoroughly characterize previously unexplored non-coding RNAs becomes increasingly urgent. These molecules hold potential as biomarkers and therapeutic targets. However, the vast and complex nature of non-coding RNAs data presents a challenge. We introduce MMnc, an interpretable deep-learning approach designed to classify non-coding RNAs into functional groups. MMnc leverages multiple data sources-such as the sequence, secondary structure, and expression-using attention-based multi-modal data integration. This ensures the learning of meaningful representations while accounting for missing sources in some samples.

Our findings demonstrate that MMnc achieves high classification accuracy across diverse non-coding RNA classes. The method's modular architecture allows for the consideration of multiple types of modalities, whereas other tools only consider one or two at most. MMnc is resilient to missing data, ensuring that all available information is effectively utilized. Importantly, the generated attention scores offer interpretable insights into the underlying patterns of the different non-coding RNA classes, potentially driving future non-coding RNA research and applications.

Data and source code can be found at EvryRNA.ibisc.univ-evry.fr/EvryRNA/MMnc.

We aimed to predict CD44 expression and assess its prognostic significance in patients with high-grade gliomas (HGG) using non-invasive radiomics models based on machine learning. Enhanced magnetic resonance imaging, along with the corresponding gene expression and clinicopathological data, was downloaded from online database. Kaplan-Meier survival curves, univariate and multivariate COX analyses, and time-dependent receiver operating characteristic were used to assess the prognostic value of CD44. Following the screening of radiomic features using repeat least absolute shrinkage and selection operator, two radiomics models were constructed utilizing logistic regression and support vector machine for validation purposes. The results indicated that CD44 protein levels were higher in HGG compared to normal brain tissues, and CD44 expression emerged as an independent biomarker of diminished overall survival (OS) in patients with HGG. Moreover, two predictive models based on seven radiomic features were built to predict CD44 expression levels in HGG, achieving areas under the curves (AUC) of 0.809 and 0.806, respectively. Calibration and decision curve analysis validated the fitness of the models. Notably, patients with high radiomic scores presented worse OS (p < 0.001). In summary, our results indicated that the radiomics models effectively differentiate CD44 expression level and OS in patients with HGG.

Understanding dysregulated genes and pathways in cancer is critical for precision oncology. Integrating mass spectrometry-based proteomic data with transcriptomic data presents unique opportunities for systematic analyses of dysregulated genes and pathways in pan-cancer. Here, we compiled a comprehensive set of datasets, encompassing proteomic data from 2404 samples and transcriptomic data from 7752 samples across 13 cancer types. Comparisons between normal or adjacent normal tissues and tumor tissues identified several dysregulated pathways including mRNA splicing, interferon pathway, fatty acid metabolism, and complement coagulation cascade in pan-cancer. Additionally, pan-cancer upregulated and downregulated genes (PCUGs and PCDGs) were also identified. Notably, RRM2 and ADH1B, two genes which belong to PCUGs and PCDGs, respectively, were identified as robust pan-cancer diagnostic biomarkers. TNM stage-based comparisons revealed dysregulated genes and biological pathways involved in cancer progression, among which the dysregulation of complement coagulation cascade and epithelial-mesenchymal transition are frequent in multiple types of cancers. A group of pan-cancer continuously upregulated and downregulated proteins in different tumor stages (PCCUPs and PCCDPs) were identified. We further constructed prognostic risk stratification models for corresponding cancer types based on dysregulated genes, which effectively predict the prognosis for patients with these cancers. Drug prediction based on PCUGs and PCDGs as well as PCCUPs and PCCDPs revealed that small molecule inhibitors targeting CDK, HDAC, MEK, JAK, PI3K, and others might be effective treatments for pan-cancer, thereby supporting drug repurposing. We also developed web tools for cancer diagnosis, pathologic stage assessment, and risk evaluation. Overall, this study highlights the power of combining proteomic and transcriptomic data to identify valuable diagnostic and prognostic markers as well as drug targets and treatments for cancer.

Mannosyl-glycoprotein beta-1,2-N-acetylglucosaminyltransferase 2 (MGAT2) and tumors' relevant research was in full swing recently. Therefore, we employed Mendelian Randomization (MR) alongside bioinformatics to thoroughly investigate the possible relationship between MGAT2 and glioblastoma (GBM).

We utilized the summary statistics of genome-wide association studies (GWAS) for MGAT2 (N = 35,559 from deCODE) and glioblastoma (N = 379,155 from FinnGen). MR was used to assess the causal relationship between MGAT2 and GBM. Bioinformatics was used for a more in-depth exploration of the relationship between MGAT2 and GBM.

MR analysis demonstrated a causal relationship, showing that elevated levels of MGAT2 are associated with an increased risk of GBM (OR = 2.59, 95 % CI: 1.13-5.91, p = 0.023). Further investigation revealed significant differences in MGAT2 expression across normal tissue, tumor tissue, and gliomas of different types. Additionally, we found that MGAT2 may influence GBM through immune-related pathways, particularly through the role of macrophages. Proteins associated with MGAT2 were also identified in the PPI network.

This study first validated the causal relationship between MGAT2 and glioblastoma, and used bioinformatics to explore the relationship from multiple perspectives. Additionally, we proposed hypotheses for further research to investigate the potential mechanisms underlying this connection.

Epoxide hydrolase 2 (EPHX2) regulates lipid signaling across various metabolites by encoding soluble epoxide hydrolase. However, its mechanisms and implications in human malignancies remain unknown. This research aimed to detail the prognostic landscape of EPHX2 in pan-cancer and explore its potential relationship with immune infiltration in the tumor microenvironment.

Herein, multiple bioinformatics tools were used to comprehensively evaluate the expression, diagnostic, and prognostic significance of EPHX2 and its roles in the tumor immune microenvironment in human cancers. The underlying EPHX2-associated signaling pathways in cancers were investigated by gene set variation analysis (GSVA). TIDE, GDSC, and CTRP databases were applied to predict the response of EPHX2 to immunotherapy and sensitivity to small molecule drugs. Furthermore, EPHX2 expression was also validated by qPCR experiments in various cancer cell lines.

Overall results revealed significant down-regulation of EPHX2 mRNA expression in most tumors. Despite its high predictive significance across cancers, EPHX2 played a protective or detrimental effect in distinct types of cancers. EPHX2 proved to be a valuable diagnostic biomarker in a range of tumor types, particularly in kidney renal clear cell carcinoma, cervical squamous cell carcinoma, and endocervical adenocarcinoma. Genetic alterations of EPHX2 in 33 tumors were also investigated. EPHX2 expression was significantly linked to immune cell infiltrations (particularly tumor-associated macrophages), tumor mutation burden, microsatellite instability, immune modulators, and immunotherapeutic biomarkers. Single-cell sequencing and GSVA highlighted the relevance of EPHX2 in regulating various cancer-related biological processes, including cell cycle and apoptosis. In this view, targeting EPHX2-dependent signaling could be a promising therapeutic strategy for tumor immunotherapy.

EPHX2 may serve as a potential molecular biomarker for diagnosis and prognosis in pan-cancer and could become a novel therapeutic target for various cancers.

Although inflammation has been widely associated with cancer development, how it affects the outcomes of immunotherapy and chemotherapy remains incompletely understood. Here, we show that CKLF-like MARVEL transmembrane domain-containing member 4 (CMTM4) is highly expressed in multiple human and murine cancer types including Lewis lung carcinoma, triple-negative mammary cancer and melanoma. In lung carcinoma, loss of CMTM4 significantly reduces tumor growth and impairs NF-κB, mTOR, and PI3K/Akt pathway activation. Furthermore, we demonstrate that CMTM4 can regulate epidermal growth factor (EGF) signaling post-translationally by promoting EGFR recycling and preventing its Rab-dependent degradation. Consequently, CMTM4 knockout sensitizes human lung tumor cells to EGFR inhibitors. In addition, CMTM4 knockout tumors stimulated with EGF show a decreased ability to produce inflammatory cytokines including granulocyte colony-stimulating factor (G-CSF), leading to decreased recruitment of polymorphonuclear myeloid-derived suppressor cells (PMN-MDSCs) and therefore establishing a less suppressive tumor immune environment in both lung and mammary cancers. We also present evidence indicating that CMTM4-targeting siRNA-loaded liposomes reduce lung tumor growth in vivo and prolong animal survival. Knockout of CMTM4 enhances immune checkpoint blockade or chemotherapy to further reduce lung tumor growth. These data suggest that CMTM4 represents a novel target for the inhibition of tumor inflammation, and improvement of the immune response and tumor drug sensitivity.

Ovarian cancer is the most common type of gynecological malignant tumor, with the highest mortality rate among female genital malignant tumors. In this study, we initially identified forkhead box F1 (FOXF1) as a potential prognostic biomarker of ovarian cancer through bioinformatics analysis. FOXF1 expression was higher in ovarian cancer tissue samples and served as an unfavorable prognostic factor. In vitro and in vivo experiments demonstrated that FOXF1 enhanced ovarian cancer cell migration and tumor dissemination. Chromatin immunoprecipitation-polymerase chain reaction and luciferase assays revealed that FOXF1 bound directly to the high-mobility group AT-hook 2 (HMGA2) promoter and significantly induced its transcriptional activity. Subsequent co-immunoprecipitation and mass spectrometry analyses demonstrated that HMGA2 stabilized S100 calcium-binding protein A6 (S100A6) protein through recruitment of the deubiquitinase, ubiquitin-specific peptidase 30 (USP30), thereby inhibiting S100A6 degradation. Rescue experiments further illustrated that FOXF1 induced ovarian cancer cell mobility in an HMGA2/S100A6-dependent manner. Additionally, FOXF1, HMGA2, USP30, and S100A6 were clinically relevant in patients with ovarian cancer. This is the first study to reveal the molecular mechanisms underlying FOXF1-mediated ovarian cancer metastasis and demonstrate that FOXF1 represents a potential therapeutic target in patients with metastatic ovarian cancer.

Clear cell renal cell carcinoma (ccRCC), is highly metastatic with unfavorable oncologic outcomes. The metastatic dissemination and underlying mechanisms of ccRCC remain insufficiently understood. The expression of fucosyltransferases (FUTs) has been explored in multiple cancer types, which affect survival of tumor cells and oncology progress. However, the role of fucosyltransferase 10 (FUT10), a member of the FUT family, is still unclear in ccRCC. We aimed to investigate the effects of FUT10 on the prognosis and immune infiltration of ccRCC via The Cancer Genome Atlas (TCGA) database.

The relationship between FUT10 expression and clinical-pathologic features was evaluated by Welch's t-test, Wilcoxon signed-rank test, Dunn's test, and logistic regression based on TCGA datasets. The FUT10 expression level was converted into a categorical variable by receiver operating characteristic (ROC) and the area under the curve (AUC). The factors associated with the prognosis were determined by Kaplan-Meier method. The function of FUT10 was identified by functional enrichment analysis, gene set enrichment analysis (GSEA), gene correlation analysis, and immune infiltration analysis. At last, we verified the FUT10 messenger RNA (mRNA) expression in ccRCC and adjacent kidney tissues by quantitative real-time polymerase chain reaction (qRT-PCR).

Downregulated FUT10 expression in ccRCC was associated with the clinical stage (P<0.001), T stage (P<0.001), M stage (P<0.001), and overall survival (OS) event (P<0.001). The ROC curve suggested that FUT10 had a certain accuracy in the diagnostic ability in ccRCC (AUC =0.787). It was shown that patient survival was prolonged in the FUT10 high-expression group. Meanwhile, multivariate analysis displayed that FUT10 was an independent risk factor for ccRCC patients (P=0.003). Moreover, we uncovered that FUT10 was involved in the phenotype of the immune response, oxidative phosphorylation (OXPHOS), arachidonic acid (AA) metabolism, and primary immunodeficiency (PID) by function enrichment analysis and GSEA. In addition, in the high FUT10 expression group, natural killer (NK) CD56bright cells exhibited lower enrichment scores, and central memory T cells exhibited higher enrichment scores. Especially, ARL8B, a key factor in NK-mediated cytotoxicity, had a certain correlation with FUT10 (r=0.590, P<0.001). Compared to the normal kidney tissues, the FUT10 mRNA expression in the ccRCC was decreased (P=0.004).

FUT10 might be a promising immune therapy target and prognostic biomarker in ccRCC.

Cervical cancer poses a substantial global health challenge. Estrogen-related receptor alpha (ERRα) is a central regulator of cellular energy metabolism associated with poor cancer prognosis. However, the effect of ERRα expression on cervical cancer prognosis and immune infiltration has not been explored. This study aims to clarify the expression pattern and role of ERRα in cervical cancer.

We analyzed ERRα expression and its clinical prognosis in cervical cancer using multiple databases, including The Cancer Genome Atlas (TCGA) and Tumor Immune Estimation Resource (TIMER). The results were further validated through immunohistochemistry (IHC) on 221 cervical cancer tissue samples. Furthermore, Kaplan-Meier and Cox regression analyses were used to assess the clinical significance of ERRα in cervical cancer patients. All calculations were performed using the R package.

ERRα expression was significantly higher in cervical cancer tissues compared to normal tissues. High ERRα expression was associated with poor overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS). Multivariate Cox regression analysis confirmed ERRα as an independent prognostic factor. Additionally, ERRα expression correlated with various immune cell types and immune checkpoints, indicating its role in the tumor immune microenvironment.

ERRα emerges as a promising prognostic biomarker in cervical cancer, influencing immune cell infiltration and potentially guiding personalized therapeutic approaches. Future investigations are warranted to delineate the mechanistic pathways through which ERRα contributes to cervical cancer progression and to assess its viability as a target for innovative immunotherapy strategies.

m6A methylation modification is a new regulatory mechanism involved in tumorigenesis and tumor-immunity interaction. However, its impact on glioma immune microenvironment and clinical outcomes remains unclear.

Comprehensive expression profiles of 18 m6A regulators were used to identify molecular subtypes exhibiting distinct m6A modification patterns in 1673 glioma samples sourced from public datasets. A multi-genes signature was constructed for predicting clinical outcomes and response to immunotherapy in glioma patients. Immunohistochemistry and cellular experiments were performed for validation.

Two m6A subtypes of gliomas were identified. The m6A-low-risk subtype was characterized by paucity of immune infiltrates; While the m6A-high-risk subtype had higher abundances of multiple immune cells including lymphocyte and macrophage as well as increased expression of PD-L1, corresponding to an immunosuppressive phenotype. The m6A-high-risk subtype had poorer survival than the m6A-low-risk subtype in both the glioblastoma and lower grade gliomas cohorts. Eight m6A-related hub genes of high prognostic significances were identified and selected for developing a scoring signature termed as m6Ascore. Elevated m6Ascore indicated worse survival for glioma patients under standard care, but showed enhanced response to immunotherapy. Moreover, we demonstrated that overexpression of FTO, a m6A demethylase, inhibited the expressions of m6A-related hub genes (PTX3, SPAG4), impaired glioma cell viability and reduced macrophage chemotaxis.

This work develops an immune- and clinical-relevant m6A subtyping and a scoring model, which enhances our understanding of the role of m6A modification in regulating immune infiltration microenvironment in gliomas and helps to identify patients who are more likely to benefit from immunotherapy.

ENOPH1 (Enolase-phosphatase 1), a member of the HAD-like hydrolase superfamily, has been linked to a range of physiological conditions, including neurological disorders. However, its involvement in tumorigenesis remains underexplored. This study is the first to conduct a pan-cancer analysis of ENOPH1, aiming to elucidate its role in multiple cancers through various bioinformatics platforms.

We conducted a thorough analysis using data from UCSC databases. ENOPH1 expression in tumor and normal tissues was evaluated using R language software. Survival analyses, genetic alterations, and RNA modifications were assessed through the GEPIA2 and cBioPortal platforms. The relationships between ENOPH1 and immune infiltration, tumor mutational burden (TMB), microsatellite instability (MSI), and homologous recombination deficiency (HRD) were examined using TIMER2 and R software. ENOPH1-related gene enrichment analysis was performed using the STRING and GEPIA2 databases, followed by Gene Ontology (GO) and KEGG pathway enrichment analyses.

ENOPH1 expression was significantly upregulated in various cancers, including ACC, BLCA, BRCA, and COAD. High ENOPH1 expression was associated with poor overall survival (OS) in cancers such as KICH, LIHC, BRCA and LUAD. High ENOPH1 expression was associated with poor disease specific survival (DSS) in cancers such as KICH, LIHC, BRCA and MESO. Genetic alterations of ENOPH1, primarily mutations and deep deletions, were identified in UCEC, BLCA, and OV. ENOPH1 showed significant correlations with RNA modifications (m1A, m5C, m6A), immune checkpoints, and immune modulators across multiple cancer types. ENOPH1 was positively correlated with TMB, MSI, and HRD in cancers like BLCA, BRCA, and STAD. Furthermore, enrichment analysis revealed that ENOPH1 interacts with proteins involved in critical pathways such as AMPK, Hippo, and PI3K-AKT, suggesting its role in cancer progression.

This pan-cancer analysis reveals ENOPH1's potential as a prognostic biomarker and its involvement in key signaling pathways across multiple cancers. Our findings provide new insights into the role of ENOPH1 in tumorigenesis and highlight its potential as a therapeutic target in cancer treatment.

Integrins, a family of transmembrane receptor proteins, are well known to play important roles in cancer development and metastasis. However, a comprehensive understanding of these roles has not been achieved due to the complex relationships between specific integrins, cancer types, and the stages of cancer progression. Publicly accessible repositories from the Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) projects provide rich datasets for exploring these relationships using machine learning (ML). In this study, integrin RNA-Seq expression data of ~ 8 healthy tissues in GTEx and corresponding tumors in TCGA were selected. Integrin expression was used to train ML models to distinguish between different healthy tissues, solid tumors, as well as normal and tumor samples from the same tissue type. These ML models can classify samples by tissue origin or disease status with high accuracy, and the integrins essential to these classifiers were identified. In some cases, the expression of only one or two integrins was needed to classify tissue type, tumor type or disease status with accuracy > 0.9. For example, expression of ITGA7 alone can distinguish healthy and cancerous breast tissue. Additionally, integrin co-expression networks in healthy and cancerous breast tissues were compared and were found to change significantly from healthy to cancer, indicating changes in functional involvement of integrins due to cancer. Integrin expression in metastatic tumors were further examined using data from the AURORA project for Metastatic Breast Cancer (MBC), and several integrins such as ITGAD, ITGA4, ITGAL, and ITGA11 were found to have significantly lower expression in metastases than in primary tumors.

This study aims to elucidate the role of minichromosome maintenance protein 4 (MCM4) in malignant melanoma (MM) and explore the underlying mechanism. Initially, data from The Cancer Genome Atlas (TCGA) database and the Molecular Signature Database (MSigDB) were used to investigate the biological impact of MCM4 on MM. Further, a prognostic model using Cox regression analysis was developed to predict the overall survival (OS) rate in the MM patients. The effects of MCM4 on the proliferation, migration, and invasion abilities of MM (B16F0 and A375) cells were demonstrated using the CCK-8, colony formation, EDU, wound scratch, and Transwell assays. In subcutaneous tumor models in C57BL/6 mice in vivo, the expression levels of MCM4 in MM cells and tumors were detected using Western blot and immunofluorescence approaches. The bioinformatics analysis indicated that MCM4 was expressed higher in MM tissues than in the normal tissues (p < 0.05). The established OS prediction model could significantly contribute to devising follow-up strategies and treating MM patients. MCM4 knockdown resulted in reduced proliferation, migration, and invasion abilities of MM cells, which were reversed by MCM4 overexpression (p < 0.05). Moreover, MCM4 could activate the phosphatidylinositol 3'-kinase (PI3K)/AKT pathway in MM cells. The PI3K inhibitor (LY294002) could reverse the effects of MCM4 on MM cells. MCM4 could substantially prompt the tumor growth of MM in mice through the PI3K/AKT pathway in vivo. In summary, MCM4 prompted the development and metastasis of MM by activating the PI3K/AKT pathway.

Gliomas are a prevalent form of primary malignant brain tumor, yet the intricate molecular mechanisms underlying its pathogenesis remain unclear. This study aimed to identify new genetic targets linked to glioma by analyzing microarray datasets to uncover genetic factors involved in its onset and progression. We obtained two independent glioma datasets from the Gene Expression Omnibus database, processed and normalized them using R software, and evaluated the relationship between differentially expressed genes and glioma by differential expression, expression quantitative trait loci, and Mendelian randomization (MR) analyses. Gene set enrichment analysis and immunocytometric analysis further explored the biological functions and pathways of identified genes, which were validated using The Cancer Genome Atlas and Genotype-Tissue Expression datasets. We identified eight co-expressed genes-C1QB, GPX3, LRRC8B, TRIOBP, SNAPC5, SPI1, TSPYL5, and FBXL16-that are crucial in various biological processes. CIBERSORT analysis revealed significant immune cell-type distributions within gliomas, underscoring the significance of immune cell infiltration. Validation in additional datasets confirmed the MR analysis results and upstream regulatory factors were identified using NetworkAnalyst. Our findings offer fresh perspectives on the molecular underpinnings of glioma and highlight potential targets for therapeutic interventions.

To overcome the paucity of known tumor-specific surface antigens in pediatric high-grade glioma (pHGG), we contrasted splicing patterns in pHGGs and normal brain samples. Among alternative splicing events affecting extracellular protein domains, the most pervasive alteration was the skipping of ≤30 nucleotide-long microexons. Several of these skipped microexons mapped to L1-IgCAM family members, such as NRCAM . Bulk and single-nuclei short- and long-read RNA-seq revealed uniform skipping of NRCAM microexons 5 and 19 in virtually every pHGG sample. Importantly, the Δex5Δex19 (but not the full-length) NRCAM proteoform was essential for pHGG cell migration and invasion in vitro and tumor growth in vivo. We developed a monoclonal antibody selective for Δex5Δex19 NRCAM and demonstrated that "painting" of pHGG cells with this antibody enables killing by T cells armed with an FcRI-based universal immune receptor. Thus, pHGG-specific NRCAM and possibly other L1-IgCAM proteoforms are promising and highly selective targets for adoptive immunotherapies.

Existing targets for chimeric antigen receptors (CAR)-armed T cells are often shared by CNS tumors and normal tissues, creating the potential for on-target/off-tumor toxicities. Here we demonstrate that in CNS tumors of glial origin, cell adhesion molecules have alternatively spliced proteoforms, which could be targeted by highly selective therapeutic antibodies.

Low-grade glioma (LGG) is a primary brain tumor with relatively low malignancy. NCOA4 is a key regulator of ferritinophagy-related processes and is involved in the occurrence and development of many cancers. However, the role of NCOA4 in LGG remains poorly understood.

This study comprehensively analyzed several mainstream bioinformatics databases to explore the expression, diagnostic efficacy, clinical pathological features, immune infiltration, prognostic value, and biological functions of NCOA4 in LGG. Immunohistochemistry experiments were conducted using LGG tissue samples collected from our hospital to validate the bioinformatics analysis results.

NCOA4 expression was significantly elevated in LGG (p < 0.05), with an Area Under the Receiver Operating Characteristic Curve (AUC) of 0.973, suggesting it as a potential diagnostic marker. High NCOA4 expression was associated with younger age (21-40 years), lower malignancy (oligodendroglioma), and better prognosis (IDHmut-non-codel and IDHmut-codel subtypes) (all p < 0.05) in LGG. Kaplan-Meier survival curves from three databases showed that high NCOA4-expressing LGG patients had better prognosis (all p < 0.05). NCOA4 correlated weakly with B cells, CD8 + T cells, macrophages, and dendritic cells infiltration (all with correlation coefficients r < 0.3, and p < 0.05) in LGG. Multivariate Cox regression identified NCOA4, age, CD8 T cells, and macrophages as LGG independent prognostic factors (all p < 0.05). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses indicated that NCOA4's primary function in LGG is related to autophagy processes (all p < 0.05).

Our findings suggest that NCOA4 could be a potential prognostic marker and therapeutic target in LGG.

Recent research has highlighted lactate's crucial role in epigenetic regulation, particularly by influencing histone modifications that drive the initiation and progression of hepatocellular carcinoma (HCC). While mitochondria are known to regulate tumor behavior, the interaction between lactate metabolism and mitochondrial function in cancer tissues remains underexplored. Understanding this relationship may provide deeper insights into tumor metabolic reprogramming and reveal novel therapeutic targets for HCC and other malignancies.

We conducted a comprehensive screening of lactylation- and mitochondria-associated genes (LMRGs) in HCC patients, followed by clustering based on these genes. Prognostic outcomes and pathway enrichment were analyzed across the identified clusters. Additionally, we developed a prognostic model based on LMRGs, examining its implications for survival, immune response, and drug sensitivity. In vitro experiments were performed to validate the expression patterns and functional role of MRPL3 in HCC.

We developed a prognostic model, named the LMRG model, incorporating three key genes: ACACA, MRPL3, and MRPS23. This model revealed significant differences in survival outcomes, immune responses, and drug sensitivity between patients with high and low LMRG scores. MRPL3 was found to be overexpressed in HCC, playing a critical role in tumor growth and metastasis. These results were further validated through in vitro experiments, confirming MRPL3's role in HCC cell proliferation and invasion.

We created a predictive model, LMRG, and identified MRPL3 as a key biomarker. Our findings suggest that MRPL3 has significant potential as a reliable predictive biomarker for clinical applications in HCC diagnosis and treatment.

Tumor microenvironment (TME) plays a crucial role in tumor growth and metastasis. Exploring biomarkers that are significantly associated with TME can help guide individualized treatment of patients.

We analyzed the expression and survival of P4HB in pan-cancer through the TCGA database, and verified the protein level of P4HB by the HPA database. In addition, we used the Metascape database to construct protein-protein interaction networks and the single-cell Sequencing database for functional analysis. An immune cell infiltration analysis was performed to explore the potential role of P4HB in TME. We further analyze the relationship between P4HB and immune checkpoint molecules to explore the role of P4HB in immune checkpoint blockade therapy. Finally, the oncogenic role of P4HB in RCC cells was validated using colony formation and wound healing assays.

RNA and protein levels of P4HB were extensively up-regulated in pan-cancer. However, high P4HB expression was associated with poor survival in KIRC. The clinical relevance analyses of P4HB suggested that high P4HB expression was associated with advanced clinical TNM stage. Moreover, multivariate cox regression analysis indicated that P4HB (HR = 1.372, 95% CI 1.047-1.681, P = 0.019) was an independent risk factor for OS in KIRC. Functional analysis revealed that P4HB is involved in hypoxia, TME and immune system processes. Our study also found that high P4HB expression was significantly correlated with elevated infiltration levels in CD8 + T cells and M2 macrophages. The results of colony formation and wound healing assays showed that knockdown of P4HB inhibited the RCC growth and migration.

P4HB is a specific biomarker for KIRC prognosis and is significantly associated with clinical characteristics. In addition, P4HB may play an influential role in TME and is a biomarker for ICB therapy.

Ovarian cancer is a common malignant tumor in women, exhibiting a certain sensitivity to chemotherapy drugs like gemcitabine (GEM). This study, through the analysis of ovarian cancer single-cell RNA sequencing (scRNA-seq) data and transcriptome data post-GEM treatment, identifies the pivotal role of hypoxia-inducible factor 1 alpha (HIF-1α) in regulating the treatment process. The results reveal that HIF-1α modulates the expression of VEGF-B, thereby inhibiting the fibroblast growth factor 2 (FGF2)/FGFR1 signaling pathway and impacting tumor formation. In vitro experiments validate the mechanistic role of HIF-1α in GEM treatment, demonstrating that overexpression of HIF-1α reverses the drug's effects on ovarian cancer cells while silencing fibroblast growth factor receptor 1 (FGFR1) can restore treatment efficacy. These findings provide essential molecular targets and a theoretical foundation for the development of novel treatment strategies for ovarian cancer in the future.

The Ribonucleoside-diphosphate Reductase subunit M2 (RRM2) is known to be overexpressed in various cancers, though its specific functional implications remain unclear. This aims to elucidate the role of RRM2 in the progression of Lung Adenocarcinoma (LUAD) by exploring its involvement and potential impact.

RRM2 data were sourced from multiple databases to assess its diagnostic and prognostic significance in LUAD. We evaluated the association between RRM2 expression and immune cell infiltration, analyzed its function, and explored the effects of modulating RRM2 expression on LUAD cell characteristics through laboratory experiments.

RRM2 was significantly upregulated in LUAD tissues and cells compared to normal counterparts (p < 0.05), with rare genetic alterations noted (approximately 2%). This overexpression clearly distinguished LUAD from normal tissue (area under the curve (AUC): 0.963, 95% confidence intervals (CI): 0.946-0.981). Elevated RRM2 expression was significantly associated with adverse clinicopathological characteristics and poor prognosis in LUAD patients. Furthermore, a positive association was observed between RRM2 expression and immune cell infiltration. Pathway analysis revealed a critical connection between RRM2 and the cell cycle signaling pathway within LUAD. Targeting RRM2 inhibition effectively suppressed LUAD cell proliferation, migration, and invasion while promoting apoptosis. This intervention also modified the expression of several crucial proteins, including the downregulation of CDC25A, CDC25C, RAD1, Bcl-2, and PPM1D and the upregulation of TP53 and Bax (p < 0.05).

Our findings highlight the potential utility of RRM2 expression as a biomarker for diagnosing and predicting prognosis in LUAD, shedding new light on the role of RRM2 in this malignancy.