The mutated in colorectal cancer (MCC) gene is closely associated with the onset and progression of colorectal cancer. MCC plays a critical role in regulating the cell cycle and various signaling pathways and is recognized to inhibit cancer cell proliferation via the β-catenin signaling pathway. β-catenin is a key component of the WNT signaling pathway that influences cell growth, differentiation, survival, and migration, thereby positioning MCC as an important tumor suppressor. Notably, MCC has also been implicated in other cancer types, including lung, liver, and brain cancers. However, the precise mechanisms by which MCC functions in these malignancies remain inadequately understood. Comprehensive investigations into the interactions among MCC, various signaling pathways, and metabolic processes are essential for uncovering the molecular mechanisms of cancer and the pathological features characteristic of different cancer stages. This review presents the structural characteristics of MCC and its cell growth regulation mechanisms and functional roles within tissues, with the aims of enhancing our understanding of the role of MCC in cancer biology and highlighting potential therapeutic strategies targeting this gene.

Tumour protein p53 (TP53) is a tumour suppressor gene that is frequently mutated in cancers. Traditional TP53 detection methods, such as polymerase chain reactions, are time-consuming and demand skilled laboratory personnel. As an alternative, in the current study, we have demonstrated a high resistivity silicon (HR-Si) based conductometric biosensor designed for the rapid and specific identification of TP53 point mutations directly at the point-of-need. This biosensor accurately detected R248Q and R248W point mutant single strand DNA (ssDNA) as models, in real-time. Both R248Q and R248W mutant ssDNA exhibited a limit of detection (LOD) of 0.5 ng/mL in human plasma. The selectivity studies revealed that both R248Q and R248W mutant ssDNA can be detected 10 × lower molar content against their wild-type ssDNA. Validation of the sensor using clinical samples harbouring known TP53 mutations demonstrated a sensitivity of 100%, a specificity of 100%, and a LOD of 2.5 ng/mL. This precision biosensing platform at the point-of-need has the potential to revolutionise cancer diagnostics.

One in five deaths in developed countries is related to cancer. The cancer prevalence is likely to grow with aging population. The affordable and accurate early diagnostics of cancer based on detection of cancer biomarkers at low concentration during its early stages is one of the most efficient way to decrease mortality and human suffering from cancer. The data from 201 analytical papers are tabulated in 9 tables, illustrated in 8 figures and used for comparative analysis of methods applied for cancer biomarker detection, including polymerase chain reaction, Loop-mediated isothermal amplification (LAMP), mass spectrometry, enzyme-linked immunosorbent assay, electroanalytical methods, immunoassays, surface enhanced Raman scattering, Fourier Transform Infrared and others in terms of above-mentioned performance parameters. Median and/or average limit of detection (LOD) are calculated and compared between different analytical methods. We also described and compared LOD of the methods used for detection of three frequently detected cancer biomarkers: carcinoembryonic antigen, prostate-specific antigen and alpha-fetoprotein. Among those methods of detection, the reported electrochemical sensors often demonstrate relatively high sensitivity/low LOD while they often have a moderate instrumental cost and fast time to results. The review tabulates, compares and discusses analytical papers, which report LOD of cancer biomarkers and comprehensive quantitative comparison of various analytical methods is made. The discussion of those techniques applied for cancer biomarker detection included brief summary of pro and cons for each of those methods.

Horizontal transfer of nuclear DNA between cells of host and cancer is a potential source of adaptive variation in cancer cells. An understanding of the frequency and significance of this process in naturally occurring tumors is, however, lacking. We screened for this phenomenon in the transmissible cancers of dogs and Tasmanian devils and found an instance in the canine transmissible venereal tumor (CTVT). This involved introduction of a 15-megabase dicentric genetic element, composed of 11 fragments of six chromosomes, to a CTVT sublineage occurring in Asia around 2,000 y ago. The element forms the short arm of a small submetacentric chromosome and derives from a dog with ancestry associated with the ancient Middle East. The introduced DNA fragment is transcriptionally active and has adopted the expression profile of CTVT. Its features suggest that it may derive from an engulfed apoptotic body. Our findings indicate that nuclear horizontal gene transfer, although likely a rare event in tumor evolution, provides a viable mechanism for the acquisition of genetic material in naturally occurring cancer genomes.

The identification of tissue-specific mutational patterns associated with cancer is challenging due to the low frequency of certain mutations and the high variability among tumors within the same cancer type. To address the inter-tumoral heterogeneity issue, our study aims to uncover infrequent mutational patterns by proposing a novel intra-clustering analysis.

A Network Graph of 8303 patients and 198 genes was constructed using single-point-mutation data from The Cancer Genome Atlas (TCGA). Patient-gene groups were retrieved with the parallel use of two separate methodologies based on the: (a) Barber's modularity index, and (b) network dynamics. An intra-clustering analysis was employed to explore the patterns within smaller patient subgroups in two phases: i) to determine the significant presence of a gene with a cancer type using the Fisher's exact test and ii) to determine gene-to-gene patterns using multiple correspondence analysis and DISCOVER. The results are followed by a Benjamini-Hochberg false discovery rate of 5%.

This analysis was applied over 24 statistically meaningful groups of 2619 patients spanning 21 cancer types and it recovered 42 mutational patterns that are not reported in the TCGA consortium publications. Notably, our findings: (i) suggest that AMER1 mutations are a putative separative element between colon and rectal adenocarcinomas, (ii) highlight the significant presence of RAC1 in head and neck squamous cell carcinoma (iii) suggest that EP300 mutations in head and neck squamous cell carcinoma are irrelevant of the HPV status of the patients and (iv) show that mutational-based clusters can contain patients with contrasting genetic alterations.

The proposed intra-clustering analysis extracted statistically significant relationships within clusters, uncovering putative clinically relevant connections and disentangling mutational heterogeneity.

This review explores the intricate relationship between viral infections and Bacillus Calmette-Guerin (BCG) efficacy, emphasizing immune modulation mechanisms that may influence treatment outcomes. Since its introduction in 1976, intravesical BCG has been a cornerstone in managing non-muscle invasive bladder cancer (NMIBC) after transurethral resection of bladder tumors (TURBT). Despite its success, variability in response rates suggests that host immune status, influenced by persistent infections, immunosenescence, and antigenic overload, may play a crucial role in therapeutic effectiveness. Chronic viral infections can modulate T cell responses, leading to immune exhaustion and impaired antitumor immunity. This review discusses the interplay between viral antigenic load, immune dysfunction, and tumor microenvironment remodeling, highlighting their potential impact on immunotherapies. By integrating insights from virome analysis, immune profiling, and tumor characterization, this review proposes personalized strategies to enhance immunotherapy efficacy. A deeper understanding of viral-induced immune dysregulation may improve prognostic assessment, optimize treatment protocols, and reduce healthcare costs associated with bladder cancer. Future research should focus on targeted interventions to mitigate the immunosuppressive effects of chronic infections, ultimately improving patient outcomes in NMIBC management.

FANCA is one of the 23 genes whose deficiencies lead to defective DNA interstrand crosslink repair and cancer-prone Fanconi anemia disease. Beyond its functions in DNA repair and tumor suppression, we report that high FANCA expression is strongly associated with breast cancer development. Overexpression of WT-FANCA significantly promotes breast cancer cell proliferation and tumor growth both in vitro and in vivo, while FANCA deficiency severely compromises the proliferation of breast cancer cells, but not non-tumorigenic breast epithelial cells. Heterozygous knockout of FANCA in breast cancer mouse models is sufficient to cause significant reduction of breast tumor growth in vivo. Furthermore, we have shown that high FANCA expression in breast cancer correlates with promoter hypomethylation in a TET-dependent manner, and TET inhibition recapitulates the proliferation defects caused by FANCA deficiency. Our study identifies the oncogenic properties of WT-FANCA and shows that FANCA is a promising target for breast cancer intervention.

Tumor subclones refer to distinct cell populations within the same tumor that possess different genetic characteristics. They play a crucial role in understanding tumor heterogeneity, evolution, and therapeutic resistance. The formation of tumor subclones is driven by several key mechanisms, including the inherent genetic instability of tumor cells, which facilitates the accumulation of novel mutations; selective pressures from the tumor microenvironment and therapeutic interventions, which promote the expansion of certain subclones; and epigenetic modifications, such as DNA methylation and histone modifications, which alter gene expression patterns. Major methodologies for studying tumor subclones include single-cell sequencing, liquid biopsy, and spatial transcriptomics, which provide insights into clonal architecture and dynamic evolution. Beyond their direct involvement in tumor growth and invasion, subclones significantly contribute to tumor heterogeneity, immune evasion, and treatment resistance. Thus, an in-depth investigation of tumor subclones not only aids in guiding personalized precision therapy, overcoming drug resistance, and identifying novel therapeutic targets, but also enhances our ability to predict recurrence and metastasis risks while elucidating the mechanisms underlying tumor heterogeneity. The integration of artificial intelligence, big data analytics, and multi-omics technologies is expected to further advance research in tumor subclones, paving the way for novel strategies in cancer diagnosis and treatment. This review aims to provide a comprehensive overview of tumor subclone formation mechanisms, evolutionary models, analytical methods, and clinical implications, offering insights into precision oncology and future translational research.

Gallbladder cancer, the most prevalent malignant neoplasm of the biliary tract, has garnered significant attention due to its dismal prognosis and high degree of malignancy. Identifying key regulatory genes is crucial for the development of effective therapeutic strategies. The differential gene expression in biliary tract malignancies was identified using the Gene Expression Omnibus (GEO) database. Subsequently, the interactions among these differentially expressed genes were analyzed employing the STRING database, and the resultant regulatory network was visualized using Cytoscape software. Utilizing the Cytoscape plugin CytoHubba, the core genes within the network were identified, followed by Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analyses. Ultimately, the overexpression of THBS1 in the gallbladder cancer cell line (NOZ) was achieved through lentiviral transfection and both in vivo and in vitro experiments were conducted to evaluate its effects. We found that thrombospondin-1 (THBS1) was the core gene of gallbladder cancer and its expression was low in gallbladder cancer. Experimental data, both in vivo and in vitro, indicate that the up-regulation of THBS1 exerts an inhibitory effect on the proliferation, migration, and invasion of gallbladder cancer cells. Furthermore, it facilitates the process of apoptosis and suppresses tumor growth and angiogenesis. The expression of THBS1 is low in gallbladder cancer. Up-regulation of THBS1 can effectively inhibit the occurrence and development of gallbladder cancer and can be used as a biomarker for the diagnosis and treatment of gallbladder cancer.

Disease-specific subtype identification can deepen our understanding of disease progression and pave the way for personalized therapies, given the complexity of disease heterogeneity. Large-scale transcriptomic, proteomic, and imaging datasets create opportunities for discovering subtypes but also pose challenges due to their high dimensionality. To mitigate this, many feature selection methods focus on selecting features that distinguish known diseases or cell states, yet often miss features that preserve heterogeneity and reveal new subtypes. To overcome this gap, we develop Preserving Heterogeneity (PHet), a statistical methodology that employs iterative subsampling and differential analysis of interquartile range, in conjunction with Fisher's method, to identify a small set of features that enhance subtype clustering quality. Here, we show that this method can maintain sample heterogeneity while distinguishing known disease/cell states, with a tendency to outperform previous differential expression and outlier-based methods, indicating its potential to advance our understanding of disease mechanisms and cell differentiation.

Clear cell renal cell carcinoma (ccRCC) is the most prevalent type of renal cell carcinoma. However, our understanding of ccRCC risk genes remains limited. This gap in knowledge poses challenges to the effective diagnosis and treatment of ccRCC. To address this problem, we propose a deep reinforcement learning-based computational approach named RL-GenRisk to identify ccRCC risk genes. Distinct from traditional supervised models, RL-GenRisk frames the identification of ccRCC risk genes as a Markov Decision Process, combining the graph convolutional network and Deep Q-Network for risk gene identification. Moreover, a well-designed data-driven reward is proposed for mitigating the limitation of scant known risk genes. The evaluation demonstrates that RL-GenRisk outperforms existing methods in ccRCC risk gene identification. Additionally, RL-GenRisk identifies eight potential ccRCC risk genes. We successfully validated epidermal growth factor receptor (EGFR) and piccolo presynaptic cytomatrix protein (PCLO), corroborated through independent datasets and biological experimentation. This approach may also be used for other diseases in the future.

Comprehensive genomic profiling (CGP) refers to the detailed genomic analysis of cancers for oncology patients. With the rapid development of next-generation sequencing (NGS) technologies, CGP has been widely applied to clinical practice and managing oncology patients. CGP can be performed on the tumor DNA and RNA, as well as non-tumor tissues (e.g., blood, pleural effusion, and ascites). In this article, we review the current evidence supporting the use of CGP in the management of oncology patients, both in real-world practice and the bridging to clinical trials. We also discuss the role of the molecular tumor board on the application of CGP in oncology patients. We provide an overview of the current scheme of CGP reimbursement in Taiwan and the precision oncology branch of the National Biobank Consortium of Taiwan. Finally, we discuss about the potential barriers and challenges of applying CGP in managing oncology patients and the future perspectives of CGP in precision oncology.

This study aimed to investigate the prognostic value of TRP ion channel genes (TRPICGs) in colorectal adenocarcinoma (COAD) and explore its related mechanisms.

The COAD dataset was downloaded from the Cancer Genome Atlas (TCGA) database. The differential expression genes (DEGs) were screened between COAD and normal samples. The differentially expressed TRPICGs (DE-TRPICGs) were obtained via intersection of DEGs and 28 TRPICGs. The Kaplan-Meier (K-M) survival curve was used to screen DE-TRPICGs with survival differences as prognostic markers. Afterward, the correlation of prognostic marker with clinical, immune cell, copy number variation were explored. Finally, immunohistochemistry (IHC) was used to verify the expression of prognostic marker.

Overall, 6003 DEGs were screened, and 6 DE-TRPICGs were obtained. Only TRPA1 was identified as prognostic biomarker. Survival and clinical correlation analyses implied that TRPA1 played an inhibitory role in colon adenocarcinoma pathogenesis and progression. Gene Set Enrichment Analysis (GSEA) indicated that TRPA1 was associated with cell cycle and immune-related pathways. Immune infiltration analysis showed that TRPA1 expression was significantly correlated with the infiltration of B cells, CD4+T cells, CD8+T cells, neutrophils and dendritic cells. Eventually, TRPA1 expression was down-regulated at the protein level in COAD samples, which presented consistent results with expression in the database.

TRPA1 was identified in COAD as a prognostic marker associated with TRP ion channels, which provided a powerful reference value and a new direction for the diagnosis and treatment of COAD.

Pure drug nanomedicines (PDNs) encompass active pharmaceutical ingredients (APIs), including macromolecules, biological compounds, and functional components. They overcome research barriers and conversion thresholds associated with nanocarriers, offering advantages such as high drug loading capacity, synergistic treatment effects, and environmentally friendly production methods. This review provides a comprehensive overview of the latest advancements in PDNs, focusing on their essential components, design theories, and manufacturing techniques. The physicochemical properties and in vivo behaviors of PDNs are thoroughly analyzed to gain an in-depth understanding of their systematic characteristics. The review introduces currently approved PDN products and further explores the opportunities and challenges in expanding their depth and breadth of application. Drug nanocrystals, drug-drug cocrystals (DDCs), antibody-drug conjugates (ADCs), and nanobodies represent the successful commercialization and widespread utilization of PDNs across various disease domains. Self-assembled pure drug nanoparticles (SAPDNPs), a next-generation product, still require extensive translational research. Challenges persist in transitioning from laboratory-scale production to mass manufacturing and overcoming the conversion threshold from laboratory findings to clinical applications.

Subclonal reconstruction algorithms use bulk DNA sequencing data to quantify parameters of tumor evolution, allowing an assessment of how cancers initiate, progress and respond to selective pressures. We launched the ICGC-TCGA (International Cancer Genome Consortium-The Cancer Genome Atlas) DREAM Somatic Mutation Calling Tumor Heterogeneity and Evolution Challenge to benchmark existing subclonal reconstruction algorithms. This 7-year community effort used cloud computing to benchmark 31 subclonal reconstruction algorithms on 51 simulated tumors. Algorithms were scored on seven independent tasks, leading to 12,061 total runs. Algorithm choice influenced performance substantially more than tumor features but purity-adjusted read depth, copy-number state and read mappability were associated with the performance of most algorithms on most tasks. No single algorithm was a top performer for all seven tasks and existing ensemble strategies were unable to outperform the best individual methods, highlighting a key research need. All containerized methods, evaluation code and datasets are available to support further assessment of the determinants of subclonal reconstruction accuracy and development of improved methods to understand tumor evolution.

Cancer involves dynamic changes caused by (epi)genetic alterations such as mutations or abnormal DNA methylation patterns which occur in cancer driver genes. These driver genes are divided into oncogenes and tumor suppressors depending on their function and mechanism of action. Discovering driver genes in different cancer (sub)types is important not only for increasing current understanding of carcinogenesis but also from prognostic and therapeutic perspectives. We have previously developed a framework called Moonlight which uses a systems biology multi-omics approach for prediction of driver genes. Here, we present an important development in Moonlight2 by incorporating a DNA methylation layer which provides epigenetic evidence for deregulated expression profiles of driver genes. To this end, we present a novel functionality called Gene Methylation Analysis (GMA) which investigates abnormal DNA methylation patterns to predict driver genes. This is achieved by integrating the tool EpiMix which is designed to detect such aberrant DNA methylation patterns in a cohort of patients and further couples these patterns with gene expression changes. To showcase GMA, we applied it to three cancer (sub)types (basal-like breast cancer, lung adenocarcinoma, and thyroid carcinoma) where we discovered 33, 190, and 263 epigenetically driven genes, respectively. A subset of these driver genes had prognostic effects with expression levels significantly affecting survival of the patients. Moreover, a subset of the driver genes demonstrated therapeutic potential as drug targets. This study provides a framework for exploring the driving forces behind cancer and provides novel insights into the landscape of three cancer sub(types) by integrating gene expression and methylation data.

DNA methylation marks have recently been used to build models known as epigenetic clocks, which predict calendar age. As methylation of cytosine promotes C-to-T mutations, we hypothesized that the methylation changes observed with age should reflect the accrual of somatic mutations, and the two should yield analogous aging estimates. In an analysis of multimodal data from 9,331 human individuals, we found that CpG mutations indeed coincide with changes in methylation, not only at the mutated site but with pervasive remodeling of the methylome out to ±10 kilobases. This one-to-many mapping allows mutation-based predictions of age that agree with epigenetic clocks, including which individuals are aging more rapidly or slowly than expected. Moreover, genomic loci where mutations accumulate with age also tend to have methylation patterns that are especially predictive of age. These results suggest a close coupling between the accumulation of sporadic somatic mutations and the widespread changes in methylation observed over the course of life.

Breast cancer is one of the most prevalent malignant tumors in women, but the side effects and drug resistance limit the long-term effectiveness of existing drugs. To address these issues, we designed and synthesized a series of novel mono- and bis-indole-substituted 3-indolylbenzoquinone derivatives and evaluated their inhibitory activity against breast cancer. Among them, compound 1b demonstrated the most potent inhibitory activity against the MDA-MB-231 breast cancer cell line (IC50 = 3.2 µM) as well as the drug-resistant variant, MCF-7/ADR (IC50 = 8.36 µM). It demonstrated minimal toxicity and superior tumor suppression in a Balb/c mouse model of 4 T1 breast cancer. Mechanistically, compound 1b induced apoptosis and cell cycle arrest at the G2/M phase. Through computational study and CESTA assay, we implicated phosphoinositide 3-kinase α (PI3Kα) as a potential target. Thus, we present compound 1b as a lead candidate for the development of novel, safe, and effective small-molecule therapies against breast cancer.

Cancer arises from genetic alterations that impact both the genome and transcriptome. The utilization of nanopore sequencing offers a powerful means of detecting these alterations due to its unique capacity for long single-molecule sequencing. In the context of DNA analysis, nanopore sequencing excels in identifying structural variations (SVs), copy number variations (CNVs), gene fusions within SVs, and mutations in specific genes, including those involving DNA modifications and DNA adducts. In the field of RNA research, nanopore sequencing proves invaluable in discerning differentially expressed transcripts, uncovering novel elements linked to transcriptional regulation, and identifying alternative splicing events and RNA modifications at the single-molecule level. Furthermore, nanopore sequencing extends its reach to detecting microorganisms, encompassing bacteria and viruses, that are intricately associated with tumorigenesis and the development of cancer. Consequently, the application prospects of nanopore sequencing in tumor diagnosis and personalized treatment are expansive, encompassing tasks such as tumor identification and classification, the tailoring of treatment strategies, and the screening of prospective patients. In essence, this technology stands poised to unearth novel mechanisms underlying tumorigenesis while providing dependable support for the diagnosis and treatment of cancer.

Mutational burden tests are essential for detecting signals of positive selection in cancer driver discovery by comparing observed mutation rates with background mutation rates (BMRs). However, accurate BMR estimation is challenging due to the diversity of mutational processes across genomes, complicating driver discovery efforts. Existing methods rely on various genomic regions and features for BMR estimation but lack a model that integrates both intergenic intervals and functional genomic elements on a comprehensive set of genomic features. Here, we introduce eMET (element-specific Mutation Estimator with boosted Trees), which employs 1372 (epi)genomic features from intergenic data and fine-tunes it with element-specific data through transfer learning. Applied to PCAWG somatic mutations, eMET significantly improves BMR accuracy and has potential to enhance driver discovery. Additionally, we provide an extensive analysis of BMR estimation, examining different machine learning models, genomic interval strategies, feature categories, and dimensionality reduction techniques.

The persistently high mortality rates associated with cancer underscore the imperative need for innovative, efficacious, and safer therapeutic agents, as well as a more nuanced understanding of tumor biology. Patient-derived organoids (PDOs) have emerged as innovative preclinical models with significant translational potential, capable of accurately recapitulating the structural, functional, and heterogeneous characteristics of primary tumors. When integrated with cutting-edge genomic tools such as CRISPR, PDOs provide a powerful platform for identifying cancer driver genes and novel therapeutic targets. This comprehensive review delves into recent advancements in CRISPR-mediated functional screens leveraging PDOs across diverse cancer types, highlighting their pivotal role in high-throughput functional genomics and tumor microenvironment (TME) modeling. Furthermore, this review highlights the synergistic potential of integrating PDOs with CRISPR screens in cancer immunotherapy, focusing on uncovering immune evasion mechanisms and improving the efficacy of immunotherapeutic approaches. Together, these cutting-edge technologies offer significant promise for advancing precision oncology.

Background/Objectives: Endoplasmic reticulum (ER) stress occurs when ER homeostasis is disrupted, leading to the accumulation of misfolded or unfolded proteins. This condition activates the unfolded protein response (UPR), which aims to restore balance or trigger cell death if homeostasis cannot be achieved. In cancer, ER stress plays a key role due to the heightened metabolic demands of tumor cells. This review explores how metabolomics can provide insights into ER stress-related metabolic alterations and their implications for cancer therapy. Methods: A comprehensive literature review was conducted to analyze recent findings on ER stress, metabolomics, and cancer metabolism. Studies examining metabolic profiling of cancer cells under ER stress conditions were selected, with a focus on identifying potential biomarkers and therapeutic targets. Results: Metabolomic studies highlight significant shifts in lipid metabolism, protein synthesis, and oxidative stress management in response to ER stress. These metabolic alterations are crucial for tumor adaptation and survival. Additionally, targeting ER stress-related metabolic pathways has shown potential in preclinical models, suggesting new therapeutic strategies. Conclusions: Understanding the metabolic impact of ER stress in cancer provides valuable opportunities for drug development. Metabolomics-based approaches may help identify novel biomarkers and therapeutic targets, enhancing the effectiveness of antitumor therapies.

Neuroblastoma (NB) poses a significant challenge in pediatric cancer care due to its aggressive nature and poor prognosis. While advances have been made in clinical treatments, therapy resistance remains a tough hurdle in NB treatment. While much research has focused on identifying oncogenes in NB, there has been less emphasis on understanding tumor suppressors. This study aimed to discover a new transcription factor that could address patient stage, risk level, and MYCN amplification status while exhibiting tumor-suppressive properties in NB patients. Using advanced bioinformatics techniques, we identified unique transcription factor signature that corresponded to patient characteristics. By analyzing regulon specificity scores, we prioritized Forkhead Box J3 (FOXJ3) as a potential novel driver transcription factor with tumor-suppressive functions in NB. Validation experiments on NB patients and patient-derived xenograft (PDX) tumors confirmed higher FOXJ3 expression in low-risk versus high-risk patients and in PDXs from diagnostic tumors versus relapse-specific tumors. Notably, the overexpression of FOXJ3 was associated with reduced cell density, proliferation, cells in S phase, colony-formation ability, transwell migration, neurosphere formation, spheroid diameter, and inhibition of AKT signaling in NB cells. Overall, these findings suggest that FOXJ3 functions as a novel tumor suppressor in NB, holding promise for potential therapeutic interventions.

Cu(II) coordination complexes are emerging as promising anticancer agents due to their ability to induce oxidative stress through reactive oxygen species (ROS) generation. In this study, we synthesized and characterized two novel Cu(II) metallopeptide systems, 1/Cu(II) and 2/Cu(II), derived from the oligocationic bipyridyl cyclopeptides 1 and 2, and designed to enhance the transport of Cu(II) into cells and increase ROS levels. Spectroscopic and electrochemical analyses confirmed the formation of stable metallopeptide species in aqueous media. Inductively coupled plasma mass spectrometry (ICP-MS) studies demonstrated that both metallopeptides significantly increase intracellular Cu(II) accumulation in NCI/ADR-RES cancer cells, highlighting their role as efficient Cu(II) transporters. Additionally, ROS generation assays revealed that 1/Cu(II) induces a substantial increase in intracellular ROS levels, supporting the hypothesis of oxidative stress-induced cytotoxicity. Cell-viability assays further confirmed that both 1/Cu(II) and 2/Cu(II) exhibit strong anticancer activity in a number of cancer cell lines, with IC50 values significantly lower than those of their free cyclopeptide counterparts or Cu(II) alone, showing an order of activity higher than that of cisplatin. Finally, molecular modeling studies provided further insights into the structural stability and coordination environment of Cu(II) within the metallopeptide complexes. These findings suggest that these Cu(II) cyclometallopeptide systems hold potential as novel metal-based therapeutic agents, leveraging Cu(II) transport and ROS increase as key strategies for cancer treatment.

Primary or acquired mutations in RAS/RAF genes resulting in cetuximab resistance have limited its clinical application in colorectal cancer (CRC) patients. The mechanism of this resistance remains unclear. RNA sequencing from cetuximab-sensitive and -resistant specimens revealed an activation of the tryptophan pathway and elevation of IDO1 and IDO2 in cetuximab-resistant CRC patients. In vitro, in vivo, and clinical specimens confirmed the upregulation of IDO1and IDO2 and the Kyn/Trp after cetuximab treatment. Additionally, the IDO inhibitor, epacadostat, could effectively inhibit the migration and proliferation of cetuximab-resistant CRC cells while promoting apoptosis. Compared to epacadostat monotherapy, the combination of cetuximab and epacadostat showed a stronger synergistic anti-tumor effect. Furthermore, in vivo experiments confirmed that combination therapy effectively suppressed tumor growth. Mechanistically, KEGG pathway analysis revealed the activation of the IFN-γ pathway in cetuximab-resistant CRC tissues. Luciferase reporter assays confirmed the transcriptional activity of IDO1 following cetuximab treatment. Silencing IFN-γ then suppressed the upregulation induced by cetuximab. Moreover, we observed that the combination reduced the concentration of the tryptophan metabolite kynurenine, promoted the infiltration of CD8+ T lymphocytes, and enhanced the polarization of M1 macrophages within the tumor microenvironment, thereby exerting potent anti-tumor immune effects. Overall, our results confirm the remarkable therapeutic efficacy of combining cetuximab with epacadostat in cetuximab-resistant CRC. Our findings may provide a novel target for overcoming cetuximab resistance in CRC.

Somatic variants in the cancer genome influence gene expression through diverse mechanisms depending on their specific locations. However, a systematic evaluation of the effects of somatic variants located in 3' untranslated regions (3' UTRs) on alternative polyadenylation (APA) of mRNA remains lacking. In this study, we analyze 10,199 tumor samples across 32 cancer types and identify 1,333 somatic single nucleotide variants (SNVs) associated with abnormal 3' UTR APA. Mechanistically, these 3' UTR SNVs can alter cis-regulatory elements, such as the poly(A) signal and UGUA motif, leading to changes in APA. Minigene assays confirm that 3' UTR SNVs in multiple genes, including RPS23 and CHTOP, induce aberrant APA. Among affected genes, 62 exhibit differential stability between tandem 3' UTR isoforms, including HSPA4 and UCK2, validated by experimental assays. Finally, we establish that SNV-related abnormal APA usage serves as an additional layer of expression regulation for tumor-suppressor gene HMGN2 in breast cancer. Collectively, this study reveals 3' UTR APA as a critical mechanism mediating the functional impact of somatic noncoding variants in human cancers.

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.

Background: As a modulator of pre-mRNA splicing, the anti-cancer agent indisulam can induce aberrantly spliced neoantigens, enabling immunologic anti-tumor activity. Consequently, combining indisulam with immunotherapy is expected to be a promising novel approach in cancer therapy. However, a prerequisite for such a combination is that immune effector cells remain functional and unharmed by the chemical. Methods: To ensure the immunocompetence of human immune effector cells is maintained, we investigated the influence of indisulam on ex vivo-isolated T cells and monocyte-derived dendritic cells (moDCs) from healthy donors. We used indisulam concentrations from 0.625 µM to 160 µM and examined the impact on the following: (i) the activation of CD4+ and CD8+ T cells by CD3-crosslinking and via a high-affinity TCR, (ii) the cytotoxicity of CD8+ T cells, (iii) the maturation process of moDCs, and (iv) antigen-specific CD8+ T cell priming. Results: We observed dose-dependent inhibitory effects of indisulam, and substantial inhibition occurred at concentrations around 10 µM, but the various functions of the immune system exhibited different sensitivities. The weaker activation of T cells via CD3-crosslinking was more sensitive than the stronger activation via the high-affinity TCR. T cells remained capable of killing tumor cells after treatment with indisulam up to 40 µM, but T cell cytotoxicity was impaired at 160 µM indisulam. While moDC maturation was also rather resistant, T cell priming was almost completely abolished at a concentration of 10 µM. Conclusions: These effects should be considered in possible future combinations of immunotherapy with the mRNA splicing inhibitor indisulam.

Background and Objectives: Neurotrophic receptor tyrosine kinase 3 (NTRK3) is a member of the tropomyosin receptor kinase family of receptor tyrosine kinases, which play a crucial role in neural development. However, owing to the limited number of studies about NTRK3 and cancer, we aimed to investigate NTRK3 as a potential prognostic marker for breast cancer (BC). Materials and Methods: We conducted a comprehensive analysis of NTRK3 expression in BC using the Tumor Immune Estimation Resource, Gene Expression Profiling Interactive Analysis 2, and Kaplan-Meier Plotter databases. We also explored the association between NTRK3 expression and tumor-infiltrating immune cells. Results: Low NTRK3 expression showed poorer prognosis in BC, as well as with T stage, pathology, and the Luminal subtype. In BC (BRCA), NTRK3 was positively correlated with CD4+ T cell, CD8+ T cell, macrophage, and neutrophil infiltration. Conclusions: These results suggest that NTRK3 may serve as a prognostic biomarker and provide novel insights into tumor immunology in BC. Therefore, NTRK3 represents a potential diagnostic and therapeutic target for BC treatment.

The identification of disease-specific subtypes can provide valuable insights into disease progression and potential individualized therapies, important aspects of precision medicine given the complex nature of disease heterogeneity. The advent of high-throughput technologies has enabled the generation and analysis of various molecular data types, such as single-cell RNA-seq, proteomic, and imaging datasets, on a large scale. While these datasets offer opportunities for subtype discovery, they also pose challenges in finding subtype signatures due to their high dimensionality. Feature selection, a key step in the machine learning pipeline, involves selecting signatures that reduce feature size for more efficient downstream computational analysis. Although many existing methods focus on selecting features that differentiate known diseases or cell states, they often struggle to identify features that both preserve heterogeneity and reveal subtypes. To address this, we utilized deep metric learning-based feature embedding to explore the statistical properties of features crucial for preserving heterogeneity. Our analysis indicated that features with a notable difference in interquartile range (IQR) between classes hold important subtype information. Guided by this insight, we developed a statistical method called PHet (Preserving Heterogeneity), which employs iterative subsampling and differential analysis of IQR combined with Fisher's method to identify a small set of features that preserve heterogeneity and enhance subtype clustering quality. Validation on public single-cell RNA-seq and microarray datasets demonstrated PHet's ability to maintain sample heterogeneity while distinguishing known disease/cell states, with a tendency to outperform previous differential expression and outlier-based methods. Furthermore, an analysis of a single-cell RNA-seq dataset from mouse tracheal epithelial cells identified two distinct basal cell subtypes differentiating towards a luminal secretory phenotype using PHet-based features, demonstrating promising results in a real-data application. These results highlight PHet's potential to enhance our understanding of disease mechanisms and cell differentiation, contributing significantly to the field of personalized medicine.

Chronic traumatic encephalopathy (CTE) is a neurodegenerative disease that is linked to exposure to repetitive head impacts (RHI), yet little is known about its pathogenesis. Applying two single-cell whole-genome sequencing methods to hundreds of neurons from prefrontal cortex of 15 individuals with CTE, and 4 with RHI without CTE, revealed increased somatic single-nucleotide variants in CTE, resembling a pattern previously reported in Alzheimer's disease (AD). Furthermore, we discovered remarkably high burdens of somatic small insertions and deletions in a subset of CTE individuals, resembling a known pattern, ID4, also found in AD. Our results suggest that neurons in CTE experience stereotyped mutational processes shared with AD; the absence of similar changes in RHI neurons without CTE suggests that CTE involves mechanisms beyond RHI alone.

Liquid-liquid phase separation (LLPS) is a novel concept that could explain how living cells precisely modulate internal spatial and temporal functions. However, a comprehensive bibliometric analysis on LLPS and immune signaling processes in cancer is still scarce. This study aims to perform a bibliometric assessment of research to explore the landscape of LLPS research in immune signaling pathways for cancer.

Utilizing the Web of Science Core Collection database and multiple analysis software, we performed quantitative and qualitative analyses of the study situation between LLPS and immune signaling in cancer from 1992 to 2024.

The corresponding authors were primarily from China and the USA. The most relevant references were the "International Journal of Molecular Sciences", "Proteomics". The annual number of publications exhibited a fast upward tendency from 2020 to 2024. The most frequent key terms included expression, separation, activation, immunotherapy, and mechanisms. Qualitative evaluation emphasized the TCR, BCR, cGAS-STING, RIG-1, NF-κB signaling pathways associated with LLPS processes.

This research is the first to integratively map out the knowledge structure and forward direction in the area of immune transduction linked with LLPS over the past 30 years. In summary, although this research area is still in its infancy, illustrating the coordinated structures and communications between cancer and immune signaling with LLPS within a spatial framework will offer deeper insights into the molecular mechanisms of cancer development and further enhance the effectiveness of existing immunotherapies.

Larger, longer-lived species are expected to have a higher cancer prevalence compared to smaller, shorter-lived species owing to the greater number of cell divisions that occur during their lifespan. Yet, to date, no evidence has been found to support this expectation, and no association has been found between cancer prevalence and body size across species-a phenomenon known as Peto's paradox. Specifically, while anticancer mechanisms have been identified for individual species, wider phylogenetic evidence has remained elusive. Here, we show that there is no evidence for Peto's paradox across amphibians, birds, mammals, and squamate reptiles: Larger species do in fact have a higher cancer prevalence compared to smaller species. Moreover, we demonstrate that the accumulation of repeated instances of accelerated body size evolution in mammals and birds is associated with a reduction in the prevalence of neoplasia and malignancy, suggesting that increased rates of body size evolution are associated with the evolution of improved cellular growth control. These results represent empirical evidence showing that larger body size is related to higher cancer prevalence, thus rejecting Peto's paradox, and demonstrating the importance of heterogenous routes of body size evolution in shaping anticancer defenses.

In recent years, remarkable technological advances have illuminated aspects of the pathogenesis of myeloid malignancies-yet outcomes for patients with these devastating diseases have not significantly improved. We posit that a synthesized view of the three dimensions through which hematopoietic cells transit during their healthy and diseased life-clonal evolution, stem cell hierarchy, and ontogeny-promises high yields in new insights into disease pathogenesis and new therapeutic avenues.

Genome sequencing of cancer and normal tissues, alongside single-cell transcriptomics, continues to produce findings that challenge the idea that cancer is a 'genetic disease', as posited by the somatic mutation theory (SMT). In this prevailing paradigm, tumorigenesis is caused by cancer-driving somatic mutations and clonal expansion. However, results from tumor sequencing, motivated by the genetic paradigm itself, create apparent 'paradoxes' that are not conducive to a pure SMT. But beyond genetic causation, the new results lend credence to old ideas from organismal biology. To resolve inconsistencies between the genetic paradigm of cancer and biological reality, we must complement deep sequencing with deep thinking: embrace formal theory and historicity of biological entities, and (re)consider non-genetic plasticity of cells and tissues. In this Essay, we discuss the concepts of cell state dynamics and tissue fields that emerge from the collective action of genes and of cells in their morphogenetic context, respectively, and how they help explain inconsistencies in the data in the context of SMT.

Cancer is a complex disease with heterogeneous mutational and gene expression patterns. Subgroups of patients who share a phenotype might share a specific genetic architecture including protein-protein interactions (PPIs). We developed the Atlas of Protein-Protein Interactions in Cancer (APPIC), an interactive webtool that provides PPI subnetworks of 10 cancer types and their subtypes shared by cohorts of patients. To achieve this, we analyzed publicly available RNA sequencing data from patients and identified PPIs specific to 26 distinct cancer subtypes. APPIC compiles biological and clinical information from various databases, including the Human Protein Atlas, Hugo Gene Nomenclature Committee, g:Profiler, cBioPortal and Clue.io. The user-friendly interface allows for both 2D and 3D PPI network visualizations, enhancing the usability and interpretability of complex data. For advanced users seeking greater customization, APPIC conveniently provides all output files for further analysis and visualization on other platforms or tools. By offering comprehensive insights into PPIs and their role in cancer, APPIC aims to support the discovery of tumor subtype-specific novel targeted therapeutics and drug repurposing. APPIC is freely available at https://appic.brown.edu.