Comparative and Functional Genomics最新文献

{"title":"Prognostic and Immunomodulatory Roles of BNIP3 in Osteosarcoma Revealed by Integrated Single-Cell and Bulk Transcriptomic Profiling","authors":"Jiamiao Li,&nbsp;Wei Wang,&nbsp;Li Li,&nbsp;Kangjun Yu","doi":"10.1155/ijog/9272264","DOIUrl":"10.1155/ijog/9272264","url":null,"abstract":"<p>Osteosarcoma is a highly aggressive bone tumor with a complex tumor microenvironment (TME) that contributes to its progression and therapeutic resistance. In this study, we integrated single-cell RNA sequencing (scRNA-seq) and bulk RNA-seq datasets to characterize the TME and identify key prognostic genes in osteosarcoma. Using scRNA-seq data from 16 osteosarcoma samples, we defined eight major cell types within the TME and performed functional enrichment analyses. Through weighted gene co-expression network analysis (WGCNA) focused on an inflammation-related gene signature, we identified the yellow module as the most correlated with inflammation. By intersecting tumor-upregulated genes with WGCNA-derived genes, we identified BNIP3 as the only significant prognostic gene associated with poor survival in both the TARGET and GSE21257 cohorts. Functional annotation revealed that high BNIP3 expression is negatively correlated with immune-related pathways and immune cell infiltration, including T cells, B cells, NK cells, and neutrophils. Additionally, BNIP3-high patients exhibited a reduced sensitivity to several potential therapeutic agents. Our findings highlight BNIP3 as a hazardous gene in osteosarcoma, with important roles in immune evasion and prognosis, suggesting its potential as a therapeutic target.</p>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12968322/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147432687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Single-Cell Transcriptomics Reveals Dynamic Cellular Interactions and Molecular Mechanisms in Myocardial Infarction Recovery","authors":"Jianfeng Zhao,&nbsp;Junhui Gong,&nbsp;Cunzhi Zhu","doi":"10.1155/ijog/4888573","DOIUrl":"10.1155/ijog/4888573","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Repair and remodeling following myocardial infarction (MI) are complex processes with a wide array of cellular and molecular mechanisms; however, the cell source mediating repair is still poorly understood in terms of heterogeneity and temporal dynamics.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We performed a single-cell RNA sequencing (scRNA-seq) analysis of cardiac tissues from different time points post-MI, as well as in gene knockout (ChrisKO) and health control groups. The data were mined by UMAP and t-SNE dimension reduction visualization, pseudotime trajectory analysis, cell communication network analysis, and gene expression pattern cluster.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>A collection of cell types contributing to cardiac repair was identified, including fibroblasts, macrophages, endothelial cells, and cardiomyocytes that each expressed gene markers and showed temporal distributions associated with distinct injury phases. Pseudotime trajectory analysis identified a continuous change in cellular state from inflammatory to reparative phase, with immune cells in early stages and tissue repair cells at latter stages. The activation of macrophage migration inhibitory factor (MIF) signaling pathway is highly involved in repair after MI, where chemokine-secreting cells and cardiac fibroblasts act as major MIF signal sources. Network analysis of the intercellular communication revealed that macrophages are key orchestrators of repair. When analyzing branch-specific gene expression, we found that several important regulatory factors including Atpdv1h, Lypla1, Mrpl15, Tcea1, Apoa, Cldn1, Dpep1, and Map had changing trends at different phases during regeneration.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Our study profiled a panoramic landscape of cellular and molecular dynamics after MI at single-cell resolution, demonstrating key cell communication networks and regulatory genes that present novel targets for developing therapeutic strategy toward cardiac repair.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12966613/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147377335","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Causal Effects of Yogurt Intake on Gut Microbiota: A European Mendelian Randomization Study","authors":"Mengqi Yang,&nbsp;Liping Wang,&nbsp;Peng Zhou,&nbsp;Jiazeng Xia","doi":"10.1155/ijog/2921181","DOIUrl":"10.1155/ijog/2921181","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Yogurt is reported to maintain the balance of gut microbiota and prevent disease, but the causal relationship remains unclear.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We selected data from UK Biobank and MiBioGen to perform Mendelian randomization analysis. MR Egger, inverse variance weighted, and so forth were employed to assess the causality between yogurt intake, low-fat and full-fat yogurt, and 196 taxa of gut microbiota. Parallelly, low-fat and full-fat yogurt were integrated to perform multivariable Mendelian randomization. Then, we summarized preliminary results according to microbiotic taxonomy.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Statistics hinted at the implicit associations between yogurt intake and <i>Haemophilus</i> (OR = 2.08), <i>Clostridium sensu stricto_1</i> (OR = 1.84), <i>Peptostreptococcaceae</i> (OR = 1.53), <i>Betaproteobacteria</i> (OR = 0.70), <i>Bilophila</i> (OR = 0.58), and <i>Ruminococcaceae UCG-011</i> (OR = 0.40), along with the associations between low-fat yogurt and <i>Eubacterium ruminantium</i> (OR = 2.48), <i>Methanobacteriaceae</i> (OR = 3.06). The findings were causal and consistent, albeit with some false positive rates.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Yogurt intake suggestively increased the abundance of <i>Haemophilus</i>, <i>Clostridium sensu stricto_1</i>, and <i>Peptostreptococcaceae</i> and decreased the abundance of <i>Ruminococcaceae UCG-011</i>, <i>Betaproteobacteria</i> and <i>Bilophila</i>; low-fat yogurt suggestively increased the abundance of <i>Eubacterium ruminantium</i> and <i>Methanobacteriaceae</i>.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-03-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12957542/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147365234","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated Transcriptomic Analysis Identifies Immune Remodeling and Prognostic Signatures in Uveal Melanoma","authors":"Zhongmin Li,&nbsp;Youmeng Yang,&nbsp;Houhong Wang,&nbsp;Jing Wang","doi":"10.1155/ijog/6511018","DOIUrl":"10.1155/ijog/6511018","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Uveal melanoma (UVM) is the most common primary intraocular malignancy in adults and exhibits a high propensity for liver metastasis, often leading to poor prognosis. However, effective prognostic biomarkers and therapeutic strategies for metastatic UVM remain limited.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We comprehensively analyzed transcriptomic data from both single-cell and bulk RNA sequencing cohorts, integrating data from TCGA and GEO (GSE139829, GSE22138, and GSE84976). After batch effect correction and cell type annotation, differentially expressed genes (DEGs) between primary and metastatic malignant cells were identified. These were intersected with 900 prognosis-related genes from TCGA, and 11 key prognostic genes were selected via least absolute shrinkage and selection operator (LASSO) regression to construct a risk prediction model. Model performance was evaluated across multiple cohorts. Furthermore, immune infiltration was assessed using CIBERSORT, and drug sensitivity was predicted based on chemotherapeutic IC50 values.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The 11-gene risk model effectively stratified UVM patients into high-risk and low-risk groups with distinct survival outcomes. High-risk patients exhibited a more immunosuppressive tumor microenvironment and were associated with altered sensitivity to multiple chemotherapeutic agents. Immune checkpoint gene expression also varied significantly between risk groups, indicating potential implications for immunotherapy response.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This study identifies critical molecular features underlying UVM metastasis and immune remodeling, providing novel prognostic markers and potential therapeutic targets for clinical management of UVM.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12948723/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147326094","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"m5C: Novel Diagnostic and Drug Repurposing Targets for Nonalcoholic Steatohepatitis","authors":"Shuxian Chen,&nbsp;Renquan Duan,&nbsp;Jingyi Qiu,&nbsp;Zhiyu Lei,&nbsp;Wei Chen,&nbsp;Xiumei Li","doi":"10.1155/ijog/4309290","DOIUrl":"10.1155/ijog/4309290","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Epigenetic medication, such as RNA 5-methylcytosine (m5C), is well-recognized as a key regulator in hepatic metabolism and immune responses. However, m5C regulatory mechanisms in NASH pathogenesis have not yet been clearly elucidated.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>By utilizing three bulk profiles of NASH patients acquired from GEO and integrative bioinformatic pipelines, such as Limma framework, consensus clustering, and machine learning, we first identified m5C-related molecular subgroups and hub genes for NASH patients. Besides, diagnostic performance and biological characteristics of m5C-related hub gene were estimated at bulk level. Indeed, the heterogeneity of m5C-related hub gene for NASH patients was deciphered in single-cell transcriptomic profiles at temporal and spatial manners, especially in artificial intelligence (AI)-driven virtual cells. Furthermore, potential therapeutic agents targeting m5C-associated hub genes for the treatment of NASH were enriched by AI-driven drug enrichment framework (DrugReflector) based on NASH bulk profile and then validated by molecular docking. Finally, in vitro studies quantified the expression of m5C-associated hub genes compared to normal control.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>m5C can divide NASH patients into two various consensus groups with different molecular and immune patterns. Furthermore, ERCC2 and FOXC2 can be considered two upregulated m5C-associated hub genes involved in NASH pathogenesis, which were mainly distributed at cholangiocyte. BRD-K93672499 can be considered a multitarget therapeutic strategy targeting ERCC2 and FOXC2 for the treatment of NASH.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>Our study first deciphered the m5C in predictive and therapeutic potential for NASH patients, which gains more insight into their personalized and precision medicine.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12933632/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147305544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Extracellular Matrix–Associated Biomarkers for Hepatocellular Carcinoma: Insights From Machine Learning and Single-Cell Analysis","authors":"Pedram Asadi Sarabi,&nbsp;Elham Rismani,&nbsp;Amir Ali Judaki,&nbsp;Amirhossein Farrokhzad,&nbsp;Zahra Hendi,&nbsp;Moustapha Hassan,&nbsp;Massoud Vosough","doi":"10.1155/ijog/6654142","DOIUrl":"10.1155/ijog/6654142","url":null,"abstract":"<p>The 5-year overall survival rate for hepatocellular carcinoma (HCC) patients remains below 20%. Alterations in the extracellular matrix (ECM) are increasingly recognized as central drivers of HCC initiation and progression. This study applied a system biology framework integrating omics data and machine learning to analyze gene expression and regulatory networks in HCC using The Cancer Genome Atlas. Eight ECM-associated genes (<i>CSPG4</i>, <i>CD34</i>, <i>C1orf35</i>, <i>ESM1</i>, <i>MAPT</i>, <i>PLXDC1</i>, <i>STC2</i>, and <i>THBS4</i>) were identified as upregulated diagnostic biomarkers with strong discriminatory power. Among them, <i>MAPT</i>, <i>PLXDC1</i>, and <i>STC2</i> showed significant associations with poor overall survival, defining a prognostic subset. Validation in the GSE104310 and GSE144269 datasets confirmed consistent expression patterns across cohorts. Functional enrichment linked these genes to tissue remodeling and angiogenesis. Single-cell RNA sequencing revealed <i>MAPT</i> upregulation in T cells, <i>PLXDC1</i> enrichment in cancer-associated fibroblasts, and mild <i>STC2</i> elevation in tumor-associated macrophages and endothelial cells. These findings identify key ECM-based biomarkers with potential for early detection, prognosis, and therapeutic targeting in HCC.</p>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12932914/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147305506","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Circulating Metabolic Factors Mediating the Effect of Obesity-Related Indicators on Meniscal Injuries: A Mendelian Randomization Study","authors":"Dong Ye,&nbsp;Zhiping Zhang,&nbsp;Jun Zhang","doi":"10.1155/ijog/8056288","DOIUrl":"10.1155/ijog/8056288","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Meniscal injuries and obesity are major global public health problems. The Mendelian randomization approach can overcome the limitations of traditional observational study designs with respect to confounding and reverse causation. The aims of the current study are to assess the causal relationship between obesity-related indicators and meniscal injuries using MR analyses and to explore the potential underlying mechanism.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>In total, seven obesity-related indicators and 11 circulating metabolic indicators were downloaded as instrumental variables from published genome-wide association studies (GWAS), and the meniscal injury data from the FinnGen database was used as the outcome indicators. The causal relationships and mediating factors were analyzed using two-sample univariate MR, multivariate MR, and intermediate MR.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>After applying the Bonferroni correction, the IVW model indicated that five obesity-related indicators, namely, waist circumference (<i>p</i> &lt; 0.001, OR = 1.6333), BMI (<i>p</i> &lt; 0.001, OR = 1.5175), body fat percentage (<i>p</i> &lt; 0.001, OR = 1.5449), and left (<i>p</i> &lt; 0.001, OR = 1.9432)/right (<i>p</i> &lt; 0.001, OR = 1.9099) leg fat percentage, increased the risk of meniscal injuries, and total body bone mineral density (<i>p</i> = 0.0014, OR = 1.1098) also increased the risk of meniscal injury. The direction of MR-identified causal relationships was consistent without horizontal pleiotropy. Multivariate and mediated MR analyses revealed that the hazardous effects of body fat percentage might be mediated by serum uric acid levels.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our study suggests that increases in serum uric acid levels, driven by body fat percentage, may increase the risk of meniscal injuries. We hope that these findings will provide new insights for the prevention and treatment of meniscal injury.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12929031/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147289774","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Epigenetics in B-CLL","authors":"Alexandra Chu,&nbsp;Flavia Soto,&nbsp;Rodrigo Hurtado,&nbsp;Carlos A. Tirado","doi":"10.1155/ijog/5877313","DOIUrl":"10.1155/ijog/5877313","url":null,"abstract":"<p>B-cell chronic lymphocytic leukemia (B-CLL) is the most common hematological malignancy in adults. Its clinical course is heterogeneous, ranging from indolent forms with slow progression to aggressive variants refractory to conventional treatment. In recent years, it has been shown that epigenetic alterations, such as DNA methylation, histone modifications, and regulation by noncoding RNAs, especially microRNAs (miRNAs), play a central role in the prognosis of this disease. For this reason, the analysis of epigenetic mechanisms has become an essential approach both to understand the progression of B-CLL and to predict therapeutic response and patient survival.</p>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12914334/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146226728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Establishment of a m6A-Related Molecular Pattern in the Prognosis and Immune Infiltration of Osteosarcoma Using Machine Learning and Experiments","authors":"Na He,&nbsp;Xia Chen,&nbsp;Chunyan Zhang","doi":"10.1155/ijog/2000690","DOIUrl":"10.1155/ijog/2000690","url":null,"abstract":"&lt;div&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Background&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;To determine the prognosis of osteosarcoma, multiple predictive models have been constructed in recent years. Nevertheless, the model for N6-methyladenosine (m&lt;sup&gt;6&lt;/sup&gt;A)-related genes, a critical subset of molecular regulators for osteosarcoma, has not been identified.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Methods&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;Gene expression matrices and clinical data were extracted from the GEO datasets GSE21257 and GSE16091. Randomly selected 70% of samples from GSE21257 were assigned as the training dataset, while the remaining 30% of samples from GSE21257 and all samples from GSE16091 were designated as the internal test and external test datasets, respectively. The predictive model was developed using elastic net–penalized Cox regression. Receiver operating characteristic (ROC) analysis, Kaplan–Meier analysis, and Wilcoxon′s tests were conducted in the training, internal test, and external test datasets to validate its efficacy. Additionally, a clinical nomogram was established for prognostic prediction. The expression of several signature genes was verified in osteosarcoma cell lines and clinical samples. In vitro experiments were performed to elucidate the impact of signature genes on the osteosarcoma phenotype. Immune infiltration analysis and gene set enrichment analysis (GSEA) were further integrated to validate the ability of the risk model to discriminate cancer characteristics.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Results&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;A total of 110 m&lt;sup&gt;6&lt;/sup&gt;A-related and survival-significant genes were identified from GSE21257. Among these, 14 genes were ultimately included in the prognostic model for osteosarcoma. ROC analysis showed that the AUC values in the training, internal test, and external test datasets were 0.8304, 0.9091, and 0.7123, respectively. Furthermore, the AUC values for predicting 1-, 3-, and 5-year overall survival were 0.8827, 0.8709, and 0.7664, respectively, with an overall AUC of 0.8275. Under this framework, a clinical nomogram was successfully constructed. Notably, immune infiltration analysis revealed a reduced immune score in the high-risk group. GSEA demonstrated enrichment of several well-known malignancy-related gene sets in the high-risk group, including E2F target genes, MYC targets, mitotic spindle, and hypoxia-related pathways, among others.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusions&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;A prognostic model based on m&lt;sup&gt;6&lt;/sup&gt;A-related genes was developed, which exhibits strong efficacy in predicting the prognosis of osteosarcoma. Additionally, a robust clinical nomogram was generate","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12906242/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146201584","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Construction of a Mitochondria-Related Gene Diagnostic Model Based on Integrated Multiomics Data and Functional Validation of ANK2 as a Key Regulator in Colorectal Cancer","authors":"Xiangyu Ding,&nbsp;Huanhuan Wu,&nbsp;Jiyuan Yang,&nbsp;Han Song,&nbsp;Jianhui Guo,&nbsp;Xudong Wang,&nbsp;Xiaopeng Zhang","doi":"10.1155/ijog/9306920","DOIUrl":"10.1155/ijog/9306920","url":null,"abstract":"<p>Colorectal cancer (CRC) is one of the most common malignancies of the digestive tract globally, characterized by high incidence, difficulty in early diagnosis, and poor prognosis. Traditional screening methods have limitations in sensitivity and specificity, thus necessitating the development of novel, efficient molecular diagnostic approaches. Recent studies have highlighted the crucial role of mitochondrial dysfunction in the initiation and progression of various cancers, suggesting that mitochondria-related genes (MRGs) could serve as promising diagnostic targets for CRC. In this study, we integrated transcriptomic data from 1174 samples across The Cancer Genome Atlas (TCGA) and multiple Gene Expression Omnibus (GEO) public datasets (GSE21510, GSE44076, and GSE9348) and combined it with MRG data from the MitoCarta3.0 database for a systematic analysis of differentially expressed genes (DEGs). Using LASSO regression and SVM-RFE, two machine learning algorithms, we identified eight key MRGs (ABCG2, ANK2, MACC1, PMAIP1, SLC22A5, SLC25A34, ACAT1, and PDK4) and constructed an early diagnostic model for CRC. Receiver operating characteristic (ROC) curve analysis confirmed the diagnostic efficacy of the model. Gene interaction networks were constructed using GeneMANIA, demonstrating the potential synergistic roles of these genes in regulating cellular metabolism, drug efflux, and immune modulation. CIBERSORT immune cell infiltration analysis revealed significant correlations between these genes and various immune cell subtypes, including T cells, macrophages, and dendritic cells. Further integration of single-cell RNA sequencing data (GSE245552) identified the specific expression patterns of the diagnostic model genes across different cell types. Additionally, we conducted an in-depth investigation of the ANK2 gene. Immunohistochemistry (HPA database), qRT-PCR, and western blotting confirmed the significantly low expression of ANK2 in CRC tissues and cell lines. Moreover, TUNEL and angiogenesis assays showed that overexpression of ANK2 significantly promoted cell apoptosis and inhibited angiogenesis, suggesting that ANK2 may function as a key tumor suppressor in CRC. In conclusion, this study proposes and validates a CRC diagnostic model based on differentially expressed mitochondrial genes. We systematically explored the molecular mechanisms and immune microenvironment correlations of the model and confirmed the biological effects through single-cell and molecular biology experiments. Notably, we highlight the potential regulatory role of ANK2 in the progression of CRC. This research provides theoretical support and new directions for early screening, diagnostic biomarker identification, and targeted therapy strategies for CRC.</p>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12860396/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146105332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}