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PubMed2026

Analysis of the molecular mechanism underlying di(2-ethylhexyl) phthalate-induced bladder carcinogenesis via network toxicology and molecular docking approaches: An observational study.

This study aims to investigate the toxicity of di(2-ethylhexyl) phthalate (DEHP) and the potential molecular mechanisms of DEHP-induced bladder cancer (BLCA) using network toxicology and molecular docking strategies. The toxicity of DEHP was assessed using Prox-II software, and potential targets for DEHP-induced BLCA were identified by integrating data from ChEMBL database, Search Tool for Interactions of Chemicals, SwissTargetPrediction, GeneCards, Therapeutic Target Database, Online Mendelian Inheritance in Man, and The Cancer Genome Atlas. STRING database and Cytoscape were employed to construct target networks and determine core targets. The expression levels of core targets were analyzed using R. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses were performed on potential and core targets. Molecular docking was carried out using CB-Dock 2 to verify the interactions between DEHP and core targets. A total of 105 potential targets related to DEHP-induced BLCA were identified, from which 7 core targets were selected: cyclin-dependent kinase 1, interleukin 6, cyclin-dependent kinase 2, cyclin B1, Erb-B2 receptor tyrosine kinase 2, cyclin B2, and B-cell lymphoma 2. IL-6 and B-cell lymphoma 2 showed downregulated expression in tumor tissues, while cyclin-dependent kinase 1, cyclin-dependent kinase 2, cyclin B1, Erb-B2 receptor tyrosine kinase 2, and cyclin B2 were upregulated. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses indicated that these targets were enriched in cell signaling and cancer-related pathways. Molecular docking confirmed that DEHP interacts with these core targets. DEHP may promote the development of BLCA by interacting with key proteins and signaling pathways. This study provides a theoretical basis for understanding the molecular mechanisms of DEHP-induced BLCA and offers references for future prevention and treatment strategies.

Diethylhexyl Phthalate↗
PubMed2005

Identification of new splice variants and differential expression of the human kallikrein 10 gene, a candidate cancer biomarker.

The human kallikrein gene 10 (KLK10) is a member of the kallikrein gene family on chromosome 19q13.4. This gene was identified by its downregulation in breast cancer, and preliminary evidence suggests that it may act as a tumor suppressor. A computer-based analysis was performed on EST and SAGE clones from the Cancer Genome Anatomy Project and other databases. Experimental verification of differential expression of KLK10 in cancer was performed by PCR using gene-specific primers. The mRNA and EST analysis allowed the construction of the longest transcript of the gene and characterization of a 5' extension of the reported mRNA. In addition, seven new splice variants of KLK10 were identified. One of these variants, named KLK10 splice variant 3 (KLK10-SV3) which starts with a novel first exon, was experimentally verified. This variant is predicted to encode for the same protein as the 'classical' KLK10 mRNA, since the first exon is untranslated. One variant mRNA partially matches with the sequence of KLK10, while the rest of the mRNA matches with a portion of the polycystic kidney disease gene, found on chromosome 15. This variant could not be experimentally verified in either normal or cancerous tissues. There are 39 reported single nucleotide polymorphisms (SNPs) for the gene, in which three result in amino acid substitutions. SAGE analysis shows a clear upregulation of KLK10 in ovarian, pancreatic, colon, and gastric cancers. The gene is, however, downregulated in breast and prostate cancers. A three-fold decrease in expression levels was noted in actinic keratosis, compared to normal skin from the same patient. The differential regulation of KLK10 in ovarian and prostate cancers was experimentally verified by RT-PCR analysis. In addition, a significant number of clones were isolated from carcinomas of the head and neck. Fewer clones were found in carcinomas of the skin, brain and prostate. Orthologues were identified in three other species, with the highest degree of homology observed with the mouse and rat orthologues (42% in each). In conclusion new splice variants of the KLK10 gene were identified. These in silico analyses show a differential expression of the gene in various malignancies and provide the basis for directing experimental efforts to investigate the possible role of the gene as a cancer biomarker.

Databases, Genetic↗
PubMed2024

Post-transcriptional control drives Aurora kinase A expression in human cancers.

Aurora kinase A (AURKA) is a major regulator of the cell cycle. A prominent association exists between high expression of AURKA and cancer, and impairment of AURKA levels can trigger its oncogenic activity. In order to explore the contribution of post-transcriptional regulation to AURKA expression in different cancers, we carried out a meta-analysis of -omics data of 18 cancer types from The Cancer Genome Atlas (TCGA). Our study confirmed a general trend for increased AURKA mRNA in cancer compared to normal tissues and revealed that AURKA expression is highly dependent on post-transcriptional control in several cancers. Correlation and clustering analyses of AURKA mRNA and protein expression, and expression of AURKA-targeting hsa-let-7a miRNA, unveiled that hsa-let-7a is likely involved to varying extents in controlling AURKA expression in cancers. We then measured differences in the short/long ratio (SLR) of the two alternative cleavage and polyadenylation (APA) isoforms of AURKA mRNA across cancers compared to the respective healthy counterparts. We suggest that the interplay between APA and hsa-let-7a targeting of AURKA mRNA may influence AURKA expression in some cancers. hsa-let-7a and APA may also independently contribute to altered AURKA levels. Therefore, we argue that AURKA mRNA and protein expression are often discordant in cancer as a result of dynamic post-transcriptional regulation.

Humans↗
PubMed2005

Prognosis of non-small cell lung cancer patients by detecting circulating cancer cells in the peripheral blood with multiple marker genes.

PURPOSE: Current lung cancer staging and prognosis methods are based on imaging methods, which may not be sensitive enough for early and accurate detection of metastasis. This study aims to validate the use of a panel of markers for circulating cancer cell detection to improve the accuracy of cancer staging, prognosis, and as a rapid assessment of therapeutic response. EXPERIMENTAL DESIGN: We analyzed the National Cancer Institute-Cancer Genome Anatomy Project database to identify potential marker genes for the detection of circulating cancer cells in peripheral blood. Nested real-time quantitative PCR and a scoring method using cancer cell load Lc were employed to correlate the amount of circulating cancer cells with clinical outcomes in 54 non-small cell lung cancer (NSCLC) patients. The Kaplan-Meier method was employed for analysis of prognostic variables. RESULTS: A panel of four marker genes was identified and experimentally validated. With these marker genes, we achieved an overall positive detection rate of 72% for circulating cancer cells in the peripheral blood of NSCLC patients. Patients who had higher Lc values had worse outcomes and shorter survival times. Patients with poor therapeutic response were revealed by positive detection of circulating cancer cells after therapy. The results correlated well with the patients' survival time. CONCLUSION: Circulating cancer cell detection by a panel of markers and the Lc scoring method can supplement the current tumor, node, metastasis staging method for improved prognosis and for rapid assessment of therapeutic response. Together, they may facilitate the design of better therapeutic strategies for the treatment of NSCLC patients.

Aged↗
PubMed2025

Anticancer drug response prediction integrating multi-omics pathway-based difference features and multiple deep learning techniques.

Individualized prediction of cancer drug sensitivity is of vital importance in precision medicine. While numerous predictive methodologies for cancer drug response have been proposed, the precise prediction of an individual patient's response to drug and a thorough understanding of differences in drug responses among individuals continue to pose significant challenges. This study introduced a deep learning model PASO, which integrated transformer encoder, multi-scale convolutional networks and attention mechanisms to predict the sensitivity of cell lines to anticancer drugs, based on the omics data of cell lines and the SMILES representations of drug molecules. First, we use statistical methods to compute the differences in gene expression, gene mutation, and gene copy number variations between within and outside biological pathways, and utilized these pathway difference values as cell line features, combined with the drugs' SMILES chemical structure information as inputs to the model. Then the model integrates various deep learning technologies multi-scale convolutional networks and transformer encoder to extract the properties of drug molecules from different perspectives, while an attention network is devoted to learning complex interactions between the omics features of cell lines and the aforementioned properties of drug molecules. Finally, a multilayer perceptron (MLP) outputs the final predictions of drug response. Our model exhibits higher accuracy in predicting the sensitivity to anticancer drugs comparing with other methods proposed recently. It is found that PARP inhibitors, and Topoisomerase I inhibitors were particularly sensitive to SCLC when analyzing the drug response predictions for lung cancer cell lines. Additionally, the model is capable of highlighting biological pathways related to cancer and accurately capturing critical parts of the drug's chemical structure. We also validated the model's clinical utility using clinical data from The Cancer Genome Atlas. In summary, the PASO model suggests potential as a robust support in individualized cancer treatment. Our methods are implemented in Python and are freely available from GitHub (https://github.com/queryang/PASO).

Deep Learning↗
PubMed2026

Mature Tertiary Lymphoid Structures in Breast Cancers Are Associated With Antitumor Immunity and Better Prognosis.

Tertiary lymphoid structures (TLSs) are immune cells accumulated in nonlymphoid tissues, with an inner core of B cells encompassed by T cells. The aim of this study was to evaluate the clinical importance of mature TLSs in breast cancer, including their association with immunotherapy response and their role in modulating the tumor immune microenvironment. We analyzed histopathological data of 726 consecutive primary breast cancers and transcriptomic data of 824 breast cancer samples from the publicly available The Cancer Genome Atlas database to estimate the clinical and immunological values of mature TLSs in breast cancer. Additionally, we utilized pretreatment transcriptomic data of 69 patients with breast cancer from the publicly available I-SPY2 clinical trial to investigate the relation between TLS-related gene signatures and patient responses to immune checkpoint inhibitors. The existence of mature TLSs was identified in ⁓5.6% (41/726) of all patients with breast cancer (hormone receptor-positive human epidermal growth factor receptor-2 negative (HR+HER2-): 0.92%; triple-negative breast cancer (TNBC): 14.96%; and human epidermal growth factor receptor-2 positive (HER2+): 10.98%) and was independently associated with improved recurrence-free survival after adjusting for subtypes, tumor-infiltrating lymphocyte levels, and tumor stage after the multivariable Cox regression analysis in our patient cohort. Notably, the presence of mature TLSs was related to immune cell infiltration in our breast cancer patient cohort. In line with these findings, TLS-related gene signatures analyzed through transcriptomic data reliably reflected the existence of mature TLSs and were related to better clinical responses to immune checkpoint inhibitors in patients with breast cancer. In conclusion, our findings show that mature TLS formation is linked with immune cell infiltration, contributes to a favorable prognosis, and may function as a potential complementary biomarker for immunotherapy response in breast cancer.

Humans↗
PubMed2026

Non-small cell lung cancer and tumor-educated platelets: screening of biomarkers and construction of a prognostic model.

BACKGROUND: Lung cancer is a leading cause of cancer-related mortality worldwide, emphasizing the urgent need for effective early detection strategies. Traditional Chinese medicine (TCM) provides a unique perspective on tumor pathogenesis, focusing on concepts such as "long-term stasis leading to accumulation". Tumor-educated platelets (TEPs) offer potential as biomarkers due to their ability to reflect cancer heterogeneity and facilitate less invasive diagnostic approaches. This study aims to identify TEP-related prognostic biomarkers for non-small cell lung cancer (NSCLC) and to construct and validate a multigene prognostic model by integrating platelet transcriptomic data with tumor tissue datasets. METHODS: We performed comprehensive analysis of gene expression datasets obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) repositories to characterize transcriptomic differences among lung cancer specimens, normal tissue samples, and TEPs. Using R software, we identified Differentially expressed genes (DEGs) and subsequently applied a multi-stage analytical pipeline to TEP-associated DEGs, incorporating univariate Cox proportional hazards regression, least absolute shrinkage and selection operator (LASSO) regression, multivariate Cox regression, and stepwise regression modeling to pinpoint genes with prognostic significance. These prognostically relevant genes served as the foundation for developing a risk stratification model. We computed individual risk scores across both training and validation cohorts, enabling patient stratification into high- and low-risk categories. Model robustness was assessed through internal cross-validation and external validation procedures, while predictive performance was quantified using risk calibration metrics and receiver operating characteristic (ROC) curve analysis. RESULTS: Through systematic bioinformatics screening, we identified a four-gene prognostic signature comprising NELL2, C4orf48, PRAM1, and KLHL35, which served as the foundation for developing our risk stratification algorithm. Rigorous internal cross-validation and external cohort validation substantiated the moderate predictive performance of this signature. Comprehensive clinicopathological correlation analysis revealed that elevated risk indices, advanced pathological staging (stage III-IV), increased primary tumor dimensions, regional lymph node metastasis, and distant organ dissemination each demonstrated statistically significant associations with diminished overall survival (OS) outcomes in lung cancer patients. The clinical nomogram exhibited acceptable calibration, with calibration plots showing reasonable concordance between predicted and observed survival probabilities across all time points. Discriminative capacity assessment via time-dependent ROC analysis yielded area under the curve (AUC) values consistently surpassing 0.6, confirming moderate prognostic discrimination. Furthermore, decision curve analysis (DCA) demonstrated that our integrated multi-gene model conferred potential net clinical benefit compared to individual prognostic variables across the full spectrum of clinically relevant threshold probabilities (0-1 range), thereby establishing its potential utility for risk-informed clinical decision-making. CONCLUSIONS: This study identified NELL2, C4orf48, PRAM1, and KLHL35 as candidate TEP-related prognostic biomarkers for non-small cell lung cancer (NSCLC). The developed prognostic model shows preliminary potential for patient stratification, but its clinical application, particularly as a platelet-based liquid biopsy tool, requires further validation in independent TEP-based cohorts.

Tumor-educated platelets (TEPs)↗
PubMed2026

High NKAP expression predicts poor prognosis of breast cancer patients.

NF-κB activating protein (NKAP) plays important roles in various cancers, including breast cancer. However, its expression and prognosis value in breast cancer remains uncertain. Gene expression profiling interactive analysis, Human protein atlas database, and University of Alabama at Birmingham Cancer data analysis portal database were used to predict the expression and prognostic value of NKAP in breast cancer. Immunohistochemistry, quantitative real time polymerase chain reaction (qRT-PCR) and western blot were performed to detect NKAP expression. The effects of NKAP on cell proliferation, migration and drug sensitivity were investigated in MDA-MB-231 and SK-BR-3 cells. NKAP protein expression differed in breast cancer tissues and paraneoplastic tissues based on the cancer genome atlas data. The high NKAP expression was significantly correlated with a poor prognosis in breast cancer patients. The results of immunohistochemistry, western blot assay, and qRT-PCR proved that NKAP was highly expressed in breast cancer tissues compared with paraneoplastic tissues. In addition, qRT-PCR results showed that high expression of NKAP was significantly correlated with the larger tumor size and higher TNM stage. Moreover, knockdown of NKAP significantly inhibited the proliferation, migration, and enhanced drug sensitivity of MDA-MB-231 and SK-BR-3 cells. NKAP is highly expressed in breast cancer tissues, and its high expression is closely associated with poor prognosis. NKAP also promotes proliferation, migration, and inhibits drug sensitivity of breast cancer cells.

Humans↗
PubMed2026

Stromal ARHGEF15 Correlates With Inflammatory EMT and Stromal-Immune Crosstalk During Inflammatory Bowel Disease-To-Colorectal Cancer Progression.

Patients with inflammatory bowel disease (IBD) have an increased risk of colorectal cancer (CRC), but how chronic intestinal inflammation drives malignant transformation remains unclear. We retrospectively reanalyzed published single-cell transcriptomic datasets from intestinal biopsies of healthy individuals and patients with IBD; differential expression was assessed using independent t tests with Benjamini-Hochberg false discovery rate correction. We then integrated those single-cell findings with the Cancer Genome Atlas bulk transcriptomes and pharmacogenomic cohorts to trace stromal programs across the IBD-to-cancer continuum. ARHGEF15 emerged as a stromal gene enriched in CD74hi HLA-DRB1hi arterial pericytes within inflamed tissue. Its expression rose steadily from IBD to CRC and tracked with epithelial-mesenchymal transition (EMT) activity. In CRC, higher ARHGEF15 expression was associated with shorter overall and progression-free survival. These retrospective, in silico findings identify ARHGEF15 as an exploratory stromal biomarker associated with inflammatory EMT and stromal-immune remodeling during IBD-to-CRC progression. Prospective experimental and clinical validation is required to establish its prognostic or therapeutic relevance.

ARHGEF15↗
PubMed2026

Multi-omics Investigations of Immune Microenvironment of Human Colorectal Cancer.

BACKGROUND/AIM: Colorectal cancer (CRC) remains a leading cause of cancer-related morbidity and mortality worldwide. Although immunotherapy has improved outcomes for a subset of patients, its limited efficacy in many cases highlights the need for a more comprehensive understanding of the CRC immune microenvironment. This study aimed to characterize the molecular landscape of the CRC immune microenvironment using an integrated multi-omics approach and to identify candidate regulatory molecules associated with immune remodelling. MATERIALS AND METHODS: We integrated structural variation, DNA methylation, chromatin accessibility, proteomic, and phosphoproteomic data generated from an in-house CRC cohort with transcriptomic data from The Cancer Genome Atlas (TCGA). Analyses focused on 1,539 immune-related genes (IRGs) associated with CD4+ T cells, B cells, and natural killer (NK) cells. Multi-layered genomic and proteomic analyses were performed to identify altered immune-related pathways, hub genes, candidate transcription factors, and upstream kinases. RESULTS: Higher infiltration of CD4+ T cells, B cells, and NK cells was associated with CRC. IRGs exhibited widespread alterations across genomic, epigenomic, transcriptomic, proteomic, and phosphoproteomic levels. IL10, LEP, ITGAM, and EGFR emerged as candidate hub genes. EGFR phosphorylation at S991 and T693 was significantly decreased in CRC. STAT2 and HSF1 were identified as candidate upstream transcription factors, while CDK2 emerged as a candidate upstream kinase associated with immune infiltration and immune checkpoint expression. CONCLUSION: This study provides a systematic multi-omics characterization of immune microenvironment remodelling in CRC and identifies candidate molecular regulators that may serve as potential targets for future immunotherapy research.

Humans↗
PubMedDate not supplied

In-silico analysis of kallikrein gene expression in pancreatic and colon cancers.

Human kallikreins are a cluster of 15 serine protease genes located in the chromosomal band 19q13.4, a non-randomly rearranged region in many solid tumors, including pancreatic cancer. We utilized the SAGE and EST databases of the Cancer Genome Anatomy Project to perform in-silico analysis of kallikrein gene expression in normal and cancerous pancreatic and colon tissues and cell lines using virtual Northern blotting (VNB), digital differential display (DDD) and X-profiler. At least two kallikreins, KLK6 and KLK10, are significantly up-regulated in pancreatic cancer. We probed 2 normal and 6 pancreatic cancer SAGE libraries with gene-specific tags for each of these kallikreins. KLK6 was found to be expressed in 5/6 cancer libraries and showed the most marked (5-fold) increase in average expression levels in cancer vs. normal. These data were verified by screening the EST databases, where all mRNA clones isolated were from cancerous libraries, with no clones detected in normal pancreatic tissues or cell lines. X-profiler comparison of two pools of normal and cancerous pancreatic libraries further verified the significant increase of KLK6 expression levels in pancreatic cancer. DDD data showed a 13-fold increase in KLK10 expression in pancreatic cancer. Three kallikrein genes, KLK6, 8 and 10 are overexpressed in colon cancer compared to normal colon, while one kallikrein, KLK1, is down-regulated. While no expression of KLK6 was detected in normal colon, KLK6-specific tags were detectable in 2 cancer libraries. Similar results were obtained by EST screening; no KLK6 clones were detected in any of the 28 normal libraries examined, while 10 KLK6 EST clones were found in colon adenocarcinoma. KLK10 was not detectable in normal colon. Gene-specific tags were, however, detectable with high density in colon cancer and 7 EST clones were found to be expressed in colon Adenocarcinoma.

Adenocarcinoma↗
PubMed2004

Reprogramming of a melanoma genome by nuclear transplantation.

We have used nuclear transplantation to test whether the reprogramming activity of oocytes can reestablish developmental pluripotency of malignant cancer cells. We show here that the nuclei of leukemia, lymphoma, and breast cancer cells could support normal preimplantation development to the blastocyst stage but failed to produce embryonic stem (ES) cells. However, a blastocyst cloned from a RAS-inducible melanoma nucleus gave rise to ES cells with the potential to differentiate into multiple cell types in vivo including melanocytes, lymphocytes, and fibroblasts. Chimeras produced from these ES cells developed cancer with higher penetrance, shorter latency, and an expanded tumor spectrum when compared with the donor mouse model. These results demonstrate that the secondary changes of a melanoma nucleus are compatible with a broad developmental potential but predispose mice to melanomas and other malignant tumors on reactivation of RAS. Our findings serve as a paradigm for studying the tumorigenic effect of a given cancer genome in the context of a whole animal.

Animals↗
PubMed2021

A full-proteome, interaction-specific characterization of mutational hotspots across human cancers.

Rapid accumulation of cancer genomic data has led to the identification of an increasing number of mutational hotspots with uncharacterized significance. Here we present a biologically informed computational framework that characterizes the functional relevance of all 1107 published mutational hotspots identified in approximately 25,000 tumor samples across 41 cancer types in the context of a human 3D interactome network, in which the interface of each interaction is mapped at residue resolution. Hotspots reside in network hub proteins and are enriched on protein interaction interfaces, suggesting that alteration of specific protein-protein interactions is critical for the oncogenicity of many hotspot mutations. Our framework enables, for the first time, systematic identification of specific protein interactions affected by hotspot mutations at the full proteome scale. Furthermore, by constructing a hotspot-affected network that connects all hotspot-affected interactions throughout the whole-human interactome, we uncover genome-wide relationships among hotspots and implicate novel cancer proteins that do not harbor hotspot mutations themselves. Moreover, applying our network-based framework to specific cancer types identifies clinically significant hotspots that can be used for prognosis and therapy targets. Overall, we show that our framework bridges the gap between the statistical significance of mutational hotspots and their biological and clinical significance in human cancers.

Genomics↗
PubMed2026

The Genomic Landscape of MYC-, MYCL-, and MYCN-Amplified Solid Tumors.

PURPOSE: MYC, MYCN, and MYCL amplifications are recurrent oncogenic events across solid tumors. Currently, no standardized selection biomarker is available to identify patients with MYC-dependent tumors. EXPERIMENTAL DESIGN: We analyzed copy-number alterations of MYC family genes and their features in more than 68,000 tumor-normal paired samples from pediatric and adult patients sequenced with MSK-IMPACT (Memorial Sloan Kettering-Integrated Mutation Profiling of Actionable Cancer Targets) and annotated with FACETS (Fraction and Allele-Specific Copy Number Estimates from Tumor Sequencing). The relationship between amplification features and MYC mRNA expression levels were evaluated in more than 10,000 samples from The Cancer Genome Atlas (TCGA). RESULTS: Across MSK Cancer Center samples, MYC amplifications were most common, found in 2,949 samples compared with 310 in MYCL and 217 in MYCN. Although MYCN and MYCL amplifications were predominantly focal (<10 Mb, 79% and 93%, respectively), MYC amplifications were frequently broader (>10 Mb, 62%). Although most tumor types showed similar features between broad and focal amplifications of MYC, in select cancer types, we identified differing co-occurrence and mutual exclusivity patterns with other disease-specific drivers. Furthermore, although MYC-amplified TCGA samples showed higher mRNA expression than wild-type ones, the focality of MYC amplification was seen to have limited influence on expression levels. CONCLUSIONS: Our results suggest that MYC dependency likely depends on many factors, including, but not limited to, total copy number of the detected amplification, lineage-specific factors, concomitant presence or absence of additional oncogenic alterations, and in some cases amplification focality.

Humans↗
PubMed2026

A nonlinear multi-omics data integration and classification model based on pathway self-attention and graph convolutional networks.

The abundance of omics data has significantly advanced the development of multi-omics data integration techniques. Non-linear embedding approaches for data integration have gradually become the mainstream in multi-omics research, as these approaches can substantially improve cancer analysis by enhancing the quality of the embeddings. However, current multi-omics data integration methods are typically confined to omics measurements, neglecting domain-specific prior knowledge encompassing biological pathways. In this study, we proposed a multi-omics integrated classification model, PathTransGCN, based on pathway self-attention and graph convolutional networks (GCN). The model integrated biological pathway information into multi-omics data analysis with the aim of enhancing the accuracy of cancer classification. Multi-omics data for breast cancer (BRCA), non-small cell lung cancer (NSCLC), and low-grade glioma (LGG) were obtained from The Cancer Genome Atlas (TCGA) and UCSC Xena databases. These data included gene mutations, DNA methylation, copy number variations, and gene expression, and were used to assess the model's generalizability across different cancers. First, PathTransGCN employed a pathway self-attention module to learn latent representations of samples across different pathways, thereby obtaining multi-omics integration vectors. Concurrently, a patient similarity network (PSN) was constructed using the similarity network fusion (SNF) approach. Second, the integrated vectors and the PSN were jointly fed into a GCN for end-to-end training, enabling precise classification of cancer subtypes. Through multi-omics data analysis of the BRCA dataset, PathTransGCN outperformed several popular algorithms (such as MoGCN and DeePathNet) in the five-class classification of cancer subtypes, achieving an accuracy rate of 87.6% and an F1 score of 86.4%. Moreover, the model demonstrated robust generalization capabilities across both NSCLC and LGG datasets, while effectively identifying key disease-associated biomarkers at the pathway level. Experimental results demonstrate that PathTransGCN exhibits outstanding performance in integrating omics data and delivering interpretable classification outcomes, presenting significant potential for clinical applications.

Humans↗
PubMed2025

The MTORC1 signaling pathway related gene POLR3G serves as a potential prognostic biomarker in Hepatocellular Carcinoma.

This study aims to investigate the prognostic significance and potential biological functions of the MTORC1 signaling pathway-associated gene POLR3G in Hepatocellular carcinoma (HCC). A prognostic risk model for HCC was developed by integrating HCC-related datasets and associated clinical data obtained from The Cancer Genome Atlas (TCGA) database. The GSVA website was employed to analyze the model genes across pan-cancer datasets, focusing on copy number variations (CNV), single nucleotide variations (SNV), methylation differences, drug sensitivity and immune cell infiltration profiles. Subsequently, we examined the expression levels and prognostic significance of POLR3G in HCC. Utilizing Spearman correlation analysis, we identified genes associated with POLR3G. Furthermore, Gene Set Enrichment Analysis (GSEA) was employed to elucidate the potential signaling pathways in which POLR3G may be involved. The relationship between POLR3G expression and immune cell abundance in HCC samples was assessed using the ssGSEA algorithm. Finally, the impact of POLR3G on HCC cell proliferation was validated through CCK-8 and EDU cell proliferation assays. Through univariate Cox regression analysis and LASSO regression analysis, we established a prognostic risk model for HCC comprising 13 genes. The analysis revealed that individuals categorized in the low-risk group had a markedly improved overall survival probability relative to those in the high-risk group. POLR3G exhibited a markedly elevated expression in HCC tissues when compared to adjacent normal tissues. The expression of POLR3G was correlated with tumor grade, and elevated POLR3G expression was associated with poor prognosis in HCC patients. Furthermore, the expression level of POLR3G was found to be correlated with the level of immune cell infiltration. Knockdown of POLR3G significantly inhibited the proliferative capacity of hepatocellular carcinoma cells. The findings suggest that POLR3G may serve as a potential biomarker influencing the prognosis of hepatocellular carcinoma patients by modulating the tumor immune microenvironment.

Humans↗
PubMed2004

LINE-1 amplification accompanies explosive genome repatterning in rodents.

Transposable elements (TEs) sometimes induce karyotypic changes following recombination, breakage and rearrangement. We used FISH and Southern blot analyses to investigate the amount and distribution of LINE-1 retrotransposons in rodents (genus Taterillus, Muridae, Gerbillinae) that have recently undergone an important genome repatterning. Our results were interpreted in a known phylogenetic framework and clearly showed that LINE-1 elements were greatly amplified and non-randomly distributed in the most rearranged karyotypes. A comparison between FISH and conventional banding patterns provided evidence that LINE-1 insertion sites and chromosome breakpoints were not strongly correlated, thus suggesting that LINE-1 amplification subsequently accompanied Taterillus chromosome evolution. Similar patterns are observed in some cases of genomic stresses (hybrid genomes, cancer and DNA-damaged cells) and usually associated with DNA hypomethylation. We propose that intensively repatterned genomes face transient stress phases during which some epigenetic features, such as DNA methylation, are relaxed, thus allowing TE amplification.

Animals↗
PubMed2026

Elevated Triggering Receptor Expressed on Myeloid Cells 2 Expression in Tumor-Associated Macrophages Suppresses Cytotoxic T Cell Infiltration and Facilitates Immune Escape in Colorectal Cancer.

BACKGROUND & AIMS: Emerging evidence supports a crucial role for tumor-associated macrophages in shaping the immunosuppressive tumor microenvironment. Furthermore, research has identified that the triggering receptor expressed on myeloid cells 2 has immunomodulatory functions. The present investigated the potential effect of triggering receptor expressed on myeloid cells 2 expression in tumor-associated macrophages on facilitating immune evasion in colorectal cancer. METHODS: Immunohistochemical analysis of clinical specimens, complemented by extensive data mining from The Cancer Genome Atlas, revealed a significant upregulation of triggering receptor expressed on myeloid cells 2 in colorectal cancer-associated tumor-associated macrophages, with this upregulation exhibiting a correlation with poor patient prognosis. RESULTS: Mechanistically, triggering receptor expressed on myeloid cells 2+ tumor-associated macrophages were found to drive fibroblast activation through transforming growth factor-&#x3b2; signaling, inducing fibroblast-activated protein-positive cancer-associated fibroblasts that secrete collagen I/III to establish dense peritumoral barriers. Spatial profiling revealed that these fibrous structures physically impede CD8+ T-cell infiltration, restricting cytotoxic lymphocytes to stromal compartments. Intriguingly, triggering receptor expressed on myeloid cells 2 deficiency enhanced the secretion of matrix metalloproteinase 13 by macrophages, thereby promoting extracellular matrix degradation and improving T-cell penetration. In vivo, Trem2-knockout mice showed a reduction in tumor growth with enhanced intratumoral CD8+ T-cell infiltration compared with wild-type controls. CONCLUSIONS: Our findings establish triggering receptor expressed on myeloid cells 2+ tumor-associated macrophages as central regulators of stromal remodeling and suggest that therapeutic targeting of the triggering receptor expressed on myeloid cells 2/transforming growth factor-&#x3b2;/fibroblast-activated protein pathway may overcome immune resistance in patients with colorectal cancer.

Colorectal Neoplasms↗