Comparative and Functional Genomics最新文献

{"title":"Hypothesis-Generating Single-Cell Analysis of the Endometrial Cancer Immune Microenvironment With Complementary Multiomics Evidence","authors":"Xiaolan Ouyang,&nbsp;Xiaoli Guo,&nbsp;Xiaoxi Yang,&nbsp;Juan Wen","doi":"10.1155/ijog/5125648","DOIUrl":"10.1155/ijog/5125648","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Endometrial cancer (EC) has a heterogeneous immune microenvironment, and response-associated biology remains incompletely characterized. We used the retained EC single-cell dataset to describe immune-cell states and complemented it with TCGA-UCEC, independent gut microbiome cohorts, and RL95-2 proof-of-concept experiments. All responder versus nonresponder scRNA-seq comparisons are descriptive and hypothesis generating because only one sample was available per clinical response group.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Single-cell RNA sequencing (scRNA-seq) data from EC patients undergoing anti–PD-1 immunotherapy (GEO: GSE251923; one responder, GSM7990051, and one nonresponder, GSM7990052; 17,546 cells retained after quality control) were analyzed using Scanpy, Leiden clustering, and over-representation analysis with GO biological process, KEGG, and MSigDB Hallmark gene sets using Enrichr with Benjamini–Hochberg correction. Bulk RNA-seq from TCGA-UCEC (554 tumor and 35 normal samples) was used for complementary transcriptomic analysis. Gut microbiome 16S rRNA datasets (SRA: PRJNA897842 and PRJNA833670) were analyzed as independent cohorts, and RL95-2 qRT-PCR experiments were interpreted as proof-of-concept.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Ten major cell populations were identified in the EC immune microenvironment. In this single responder/nonresponder sample pair, the nonresponder sample showed patterns consistent with higher M2-associated macrophage marker expression, increased T-cell checkpoint/exhaustion marker expression, and lower cytotoxic activity scores. These observations are descriptive and cannot establish patient-level significance. TCGA-UCEC analyses and the independent microbiome and RL95-2 experiments provide complementary, hypothesis-generating evidence; no unverified classifier-performance estimate is reported.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This study provides a hypothesis-generating description of immune patterns in one EC responder and one nonresponder sample, complemented by TCGA-UCEC, independent microbiome datasets, and RL95-2 proof-of-concept experiments. The findings nominate candidate immune, hormonal, and microbiome-associated pathways for further investigation but do not establish patient-level response differences, causality, or clinically validated biomarkers.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13677134/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149220410","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 Multiomics Integration Reveals Cell Trajectories and Regulatory Networks in Breast Cancer Progression","authors":"Xiangyu Jin,&nbsp;Chengyin Ma,&nbsp;Hongqiang Xin,&nbsp;Yi Liu,&nbsp;Changjiang Sun","doi":"10.1155/ijog/9324717","DOIUrl":"https://doi.org/10.1155/ijog/9324717","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;Breast cancer is a molecularly heterogeneous disease encompassing distinct subtypes with divergent prognoses and therapeutic responses. Despite extensive bulk genomic characterization, the single-cell transcriptional trajectories, chromatin regulatory landscapes, and intercellular communication networks that drive breast cancer progression from luminal to mesenchymal states remain incompletely understood. Emerging evidence implicates gut microbiome dysbiosis as a systemic modulator of breast cancer risk, immune regulation, and treatment response; however, its coordinated characterization alongside high-resolution single-cell profiling has not been undertaken. Integrating single-cell RNA sequencing (scRNA-seq) with chromatin accessibility profiling (scATAC-seq), cell-cell communication inference, and gut microbiome 16S rRNA analysis offers a comprehensive multilayered approach to dissecting both the intratumoral regulatory logic and the systemic microbial landscape of breast cancer progression.&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;We performed integrated multiomics analysis of 42,037 high-quality single cells from breast cancer patient samples obtained from the GEO database (GSE161529), encompassing tumor and adjacent normal tissue. scRNA-seq data were processed using Seurat v4.3.0 and Scanpy v1.9.1. Dimensionality reduction, Leiden clustering, differential expression analysis, pseudotime trajectory inference (Monocle3, scVelo, Slingshot), SCENIC regulon analysis, chromatin accessibility profiling, cell-cell communication inference (CellChat), and functional enrichment analysis were applied. Key marker genes were validated by quantitative RT-PCR in MCF-7 and MDA-MB-231 cell lines. In parallel, gut microbiome 16S rRNA amplicon sequencing data from 599 matched breast cancer–healthy control pairs (266 genera, four BioProjects) were analyzed for alpha/beta diversity, differential genus abundance (Mann–Whitney &lt;i&gt;U&lt;/i&gt; with Benjamini–Hochberg correction), genus co-occurrence networks, and random forest breast cancer classification.&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;Unbiased clustering identified 12 observed cell populations spanning luminal (Luminal A, Luminal B), basal, HER2+, cancer-associated fibroblast (CAF), tumor-associated macrophage (TAM), CD8 T cell, regulatory T cell (Treg), NK cell, B cell, endothelial, and pericyte compartments. Quality control confirmed robust data quality with median 2400–3100 genes per cell and mitochondrial content below 20%. Pseudotime trajectory analysis revealed a continuous epithelial-to-mesenchymal transition (EMT) spectrum with three divergent lineages: EMT, senescence","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ijog/9324717","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205356","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":"Integrative Single-Cell and Bulk Transcriptomics Identifies a Proliferative Malignant State and Supports a Functional Contribution of TROAP in Osteosarcoma","authors":"Peilei Jiao,&nbsp;Xiuqi Chen,&nbsp;He Wei,&nbsp;Zhihua Sun,&nbsp;Miao Lu,&nbsp;Ye Bai,&nbsp;Wenjin Zhang,&nbsp;Dexin Wang","doi":"10.1155/ijog/1407947","DOIUrl":"https://doi.org/10.1155/ijog/1407947","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Osteosarcoma contains heterogeneous malignant-cell states whose clinical relevance is difficult to infer from small single-cell cohorts.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We reanalyzed six treatment-naive osteosarcomas from GSE162454, identified CNV-supported malignant osteoblastic cells, and characterized a recurrent proliferative/mitotic state using patient-paired pseudobulk analysis and program-selection sensitivity tests. A 50-gene module was evaluated for association with metastatic occurrence in 53 pretreatment tumors from GSE21257. Candidate ranking was audited across transparent weighting scenarios, and TROAP was depleted with two independent siRNAs in HOS and U2OS cells.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Of 52,489 input barcodes, 39,063 singlets were retained, including 10,456 CNV-supported malignant osteoblastic cells. M6 comprised 725 cells and was detected in all six tumors. It ranked first in six of eight program-selection scenarios but fell to fifth when G2M checkpoint and E2F targets were jointly excluded, supporting its interpretation as a proliferative/mitotic state. Its weighted module was higher in metastatic tumors (<i>p</i> = 0.0147; apparent AUC = 0.704), but the association was attenuated after macrophage adjustment (OR = 1.646, 95% CI 0.649–4.170; <i>p</i> = 0.2937), whereas the macrophage score remained inversely associated with metastasis (OR = 0.251, 95% CI 0.081–0.773; <i>p</i> = 0.0161). TROAP was strongly enriched in M6 and supported by paired pseudobulk analysis, but it was not the top-ranked candidate under every weighting scenario. TROAP depletion reduced colony formation, overall wound closure, and Transwell migration and invasion in both cell lines.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>A recurrent proliferative/mitotic malignant state is detectable across the six osteosarcomas and yields a bulk expression program associated with metastatic occurrence, although this association is sensitive to macrophage composition. TROAP is supported as a functional contributor to osteosarcoma cell phenotypes rather than an established driver of the state or an independently validated clinical marker.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ijog/1407947","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149205934","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":"The Cellular and Immune Context of YTHDC1 in Hepatocellular Carcinoma: A Single-Cell and Multicohort Study","authors":"Siqi Luo,&nbsp;Diguang Wen,&nbsp;Jingyi Zhang,&nbsp;Zuojin Liu,&nbsp;Jiao Lu,&nbsp;Jianchuan Deng,&nbsp;Hanqing Zeng,&nbsp;Shifeng Lou,&nbsp;Yue Li,&nbsp;Lin Lv","doi":"10.1155/ijog/6542992","DOIUrl":"https://doi.org/10.1155/ijog/6542992","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>YTHDC1 is a nuclear N6-methyladenosine reader involved in RNA processing. Its abundance, cellular distribution, and relationship with the immune microenvironment in hepatocellular carcinoma (HCC) remain incompletely characterized.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>We integrated paired tumor–adjacent transcriptomic and proteomic cohorts, three single-cell RNA-sequencing datasets containing 249,080 quality-controlled cells, four outcome cohorts, three immune checkpoint inhibitor (ICI) cohorts, paired clinical specimens, and cell-based YTHDC1-knockdown assays. Patient-level analyses covered hepatocyte programs, immune states, expression-supported ligand–receptor axes, survival, ICI response, and exploratory single-cell network perturbation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>YTHDC1 protein abundance was higher in 160 paired CPTAC samples (Hedges′ <i>g</i> = 1.21, 95% confidence interval [CI] 1.01–1.42), whereas RNA effects varied across cohorts. Single-cell analysis localized YTHDC1 across hepatic and immune compartments and identified patient-level associations with hepatocyte, lymphoid, and myeloid programs. SPP1–CD44 received the strongest cross-cohort expression support among prespecified hepatocyte-to-myeloid axes. A transferred hepatocyte state showed a pooled tumor–adjacent effect of <i>g</i> = 0.42 (95% CI 0.04–0.80), and higher YTHDC1 abundance was associated with poorer overall survival in study-aware univariable models (hazard ratio 1.20, 95% CI 1.06–1.36). In 37 ICI-treated patients, the pooled responder–nonresponder effect for YTHDC1 was small (<i>g</i> = 0.05, 95% CI −0.60 to 0.71). Exploratory virtual knockout showed modest cross-cohort agreement in network perturbation ranks. Paired clinical tissues supported increased YTHDC1 mRNA and protein abundance, and YTHDC1 silencing reduced proliferation-related readouts and THP-1 migration in two HCC cell lines.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>YTHDC1 is increased at the protein level in HCC and is situated within identifiable hepatocyte and immune programs. Functional assays support its contribution to HCC cell proliferation and tumor cell–dependent macrophage-like cell migration under the tested conditions.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ijog/6542992","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146679","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":"The Relationship of Peripheral Glycoprotein Nonmetastatic Melanoma Protein B and Parkinson′s Disease: Insights From a Mendelian Randomization Study","authors":"Yaoyi Zhang,&nbsp;En Huang,&nbsp;Nan Lin","doi":"10.1155/ijog/2340381","DOIUrl":"https://doi.org/10.1155/ijog/2340381","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Genome-wide association studies (GWASs) have revealed that glycoprotein nonmetastatic melanoma Protein B (GPNMB) was associated with Parkinson′s disease (PD). GPNMB has been found in plasma and cerebrospinal fluid, and its levels and the variation of single nucleotide polymorphisms (SNPs) have been linked to disease severity in PD patients. However, whether the altered expression of GPNMB is the cause or the result cannot be identified in the patient samples.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>This study applied a Mendelian randomization analysis to explore the causal relationship between GPNMB and PD. The blood GPNMB mRNA level from the expression quantitative trait loci (eQTL) dataset and the plasma GPNMB protein level from the protein quantitative trait loci (pQTL) acted as the exposure. PD′s GWAS dataset from the International Parkinson′s Disease Genomics Consortium including 33674 PD cases and 449056 normal controls was applied as the outcome.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>MR analysis with the inverse-variant weighted method (IVW, OR = 1.416, <i>p</i> = 1.39<i>e</i> − 06) and the weighted median methods (WM, OR = 1.430, <i>p</i> = 4.47<i>e</i> − 07) showed that the plasma GPNMB protein expression was linked to a higher risk of PD. We also discovered a significant association between the blood mRNA level of GPNMB and PD risk. The IVW (OR = 1.243, <i>p</i> = 3.64<i>e</i> − 09) and WM (OR = 1.237, <i>p</i> = 5.11<i>e</i> − 08) methods share similar results. Secondary MR analysis further reveals causality between GPNMB pQTL/eQTL and brain volume traits.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>The study demonstrated a significant association between plasma GPNMB protein (pQTL) and mRNA level (eQTL) with PD. Further secondary MR analysis revealed causality between GPNMB pQTL/eQTL and brain volume traits.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ijog/2340381","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149146354","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":"Targeting lncRNA PVT1/miR-145-5p/ABCC-1 Pathway Enhances Paclitaxel Sensitivity and Suppresses Melanoma Progression","authors":"Negar Pedram,&nbsp;Reza Safaralizadeh,&nbsp;Mohammad Ali Hosseinpour Feizi,&nbsp;Dariush Shanehbandi","doi":"10.1155/ijog/2246468","DOIUrl":"10.1155/ijog/2246468","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>As a highly aggressive type of skin cancer, melanoma cells show significant resistance against a variety of chemotherapeutics, such as paclitaxel, which limits the efficacy of the treatment. Studies have highlighted the potential role of noncoding RNA dysregulation in melanoma, affecting tumor progression and drug response, including lncRNA PVT1.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study investigated whether simultaneous modulation of the lncRNA PVT1/miR-145-5p/ABCC1 axis enhances paclitaxel sensitivity and suppresses malignant behaviors in a melanoma cell line.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Using SK-MEL-3 and A375 cells, we performed PVT1 knockdown using siPVT1, restored miR-145-5p function by transfection, and administered paclitaxel, either alone or in combination. Cell viability was assessed using the MTT assay; apoptosis and cell cycle distribution were analyzed by flow cytometry; and the expression of genes related to apoptosis, cell cycle regulation, migration, and drug resistance was quantified by qRT-PCR.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Combination therapy markedly reduced cell viability compared with single treatments. Cotargeting PVT1 and miR-145-5p significantly enhanced paclitaxel-induced apoptosis, evidenced by increased expression of proapoptotic and downregulation of antiapoptotic genes. The combination also induced a pronounced G1-phase arrest with decreased levels of CDK-4 and CDK-6. Furthermore, gene expression analysis showed downregulation of MMP-9 and ABCC1, along with upregulation of E-cadherin, suggesting reduced migratory potential and reversal of drug resistance.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Simultaneous PVT1 knockdown and miR-145-5p overexpression enhance the anticancer effects of paclitaxel by promoting apoptosis, inducing cell cycle arrest, and mitigating drug resistance in melanoma cells. Targeting the PVT1/miR-145-5p/ABCC1 regulatory network may provide a promising therapeutic strategy to overcome chemoresistance in melanoma.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13639832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149109071","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":"Metagenomic Tools as Predictive Approaches to Decipher Soil and Rhizosphere Microbial Communities and Their Functions for Sustainable Agriculture","authors":"Oluwaseun Emmanuel Shittu,&nbsp;Jing Ge,&nbsp;Olubukola Oluranti Babalola","doi":"10.1155/ijog/3098615","DOIUrl":"10.1155/ijog/3098615","url":null,"abstract":"<p>The increasing demand for resilient and sustainable food production necessitates a comprehensive understanding of rhizosphere microbial communities and their functional roles in enhancing plant health, nutrient acquisition, stress resilience, and crop productivity, thereby supporting food security and sustainable agriculture. However, soil microbiomes are composed of complex and unculturable communities of beneficial microorganisms that drive critical ecosystem functions, which traditional culture methods cannot capture. This limits the understanding of their roles and impacts on plant health and agricultural sustainability. Metagenomic tools have emerged as transformative approaches for characterizing microbial diversity in soil. Therefore, this review presents an overview of how metagenomic techniques can be harnessed to predict microbial community structures and their associated functional potentials, ultimately contributing to the development of resilient farming strategies. Next-generation sequencing and bioinformatics enable metagenomics to analyze microbial genetic material in soil and rhizospheres. This study provides insights into microbial diversity and profiles of key functional genes linked to nutrient availability and soil functions. We discuss the use of metagenomics tools to predict soil health and microbial functions for sustainable agroecosystems, emphasizing predictive models that help farmers optimize yields and minimize environmental impact. This review explores how metagenomics tools in agriculture can serve as a predictive model to transform our understanding of soil–microbe interactions. This advancement can facilitate the formulation of novel, more resilient, productive, and sustainable agricultural systems.</p>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13636661/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108997","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":"Identification of a Novel Subtype-Derived Prognostic Signature Based on Nucleotide Metabolism-Related Molecular Classification for Predicting Lung Adenocarcinoma Prognosis","authors":"Yue Shi,&nbsp;Shanshan Li,&nbsp;Lin Zhao,&nbsp;Yali Li,&nbsp;Fangyu Cai,&nbsp;Kun Wang,&nbsp;Naibo Xu,&nbsp;Benkun Liu,&nbsp;Fucheng Zhou,&nbsp;Yue Cui,&nbsp;Changguo Shi,&nbsp;Xue Bai,&nbsp;Jian Zhang","doi":"10.1155/ijog/4944325","DOIUrl":"10.1155/ijog/4944325","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;Lung adenocarcinoma (LUAD) is a major subtype of nonsmall cell lung cancer with substantial clinical heterogeneity. Nucleotide metabolism-related genes may contribute to tumor progression and immune microenvironment remodeling, but their prognostic value in LUAD remains incompletely defined.&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;Bulk transcriptomic and clinical data from TCGA-LUAD and GEO datasets were analyzed to define nucleotide metabolism-related molecular subtypes and construct a subtype-derived prognostic signature from subtype-associated differentially expressed genes. The TCGA-LUAD cohort was randomly divided into a training cohort and an internal validation cohort at a ratio of 4:1. Univariate Cox regression and LASSO regression analyses were performed exclusively in the training cohort to construct the prognostic signature, which was subsequently evaluated in the internal validation cohort and the independent external bulk-transcriptomic GSE30219 cohort. Consensus clustering, survival analysis, ROC analysis, immune infiltration analysis, mutation analysis, predicted drug sensitivity analysis, and exploratory single-cell transcriptomic characterization were also performed. In vitro assays were used to evaluate the functional relevance of IRX5 in A549 LUAD cells.&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 152 subtype-associated differentially expressed genes were identified from pairwise comparisons among the three nucleotide metabolism-related molecular subtypes, and a nine-gene subtype-derived prognostic signature consisting of SEMA3C, PTTG1, BARX1, CDCA5, TGFBI, MKI67, TSPAN7, GADD45G, and IRX5 was constructed. The signature stratified LUAD patients into high- and low-risk groups with distinct overall survival and remained an independent prognostic factor. The risk score was associated with TP53 mutation frequency, immune cell infiltration patterns, cytokine- and exhaustion-related scores, and predicted IC50 values for several anticancer agents. Single-cell analysis revealed cell-type–specific expression patterns of signature genes and potential intercellular communication features in the LUAD microenvironment. Functionally, IRX5 knockdown inhibited proliferation, migration, and invasion, promoted apoptosis, and induced G0/G1 cell-cycle arrest in A549 cells.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusion&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;This study identified a subtype-derived prognostic signature based on nucleotide metabolism-related molecular classification with potential value for LUAD risk stratification and provide","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13633477/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108849","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":"Beyond the Edit: Integrating CRISPR-Cas Genome Editing Into the Cereal Breeding Pipeline","authors":"Baber Ali,&nbsp;Aqsa Hafeez,&nbsp;Nijat Imin","doi":"10.1155/ijog/1457684","DOIUrl":"10.1155/ijog/1457684","url":null,"abstract":"<p>Cereal breeding must deliver complex trait combinations under a changing climate, and CRISPR-Cas editing is presented as the technology that will accelerate this. Existing reviews document the editing toolkit thoroughly, yet few follow an edited allele through the steps separating an edited plant from a released cultivar. This review addresses that gap by treating genome editing as one module within a cereal breeding pipeline and asking, at each interface, whether integration has been demonstrated or only proposed. Evidence was selected against three criteria, namely, that the work was performed in a cereal, that the outcome was measured rather than projected and that genotype and environment were stated. Applying this standard across wheat, rice, maize and barley yields a more qualified picture than current summaries suggest. Multiplex editing is the dominant strategy for polygenic traits, yet recovery of a complete multilocus genotype is limited by the least efficient guide rather than the average efficiency. Doubled haploid production shortens fixation only where several loci segregate independently, and its induction, identification and doubling steps impose losses that offset part of the saving. Marker-assisted selection acts on backcross progeny and on haploid inducers rather than among near-isogenic edited events, and genomic selection combined with editing rests on livestock simulations whose assumptions cereals do not meet. Transformation efficiency, licensing of regeneration technology and jurisdiction-specific approval remain binding constraints. Every module has been demonstrated in some cereal. The full sequence has not and closing that gap needs integration studies rather than further gains in editing precision.</p>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13628700/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149108721","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":"Revealing the Diagnostic Potential of Three RHO GTPase-Related Genes in Osteoarthritis Based on Single-Cell RNA Sequencing and GEO Dataset Analysis","authors":"Guoqiang Zou,&nbsp;Xufeng Wang,&nbsp;Jing Yu","doi":"10.1155/ijog/7632269","DOIUrl":"10.1155/ijog/7632269","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;Osteoarthritis (OA) is becoming increasingly common, and RHO GTPase family members have been implicated in inflammatory processes associated with OA. We therefore examined the diagnostic utility of RHO GTPase-related genes (RGPRGs) in OA.&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;The OA expression datasets were collected from public repositories, whereas RGPRGs were assembled from published literature. In the training cohort, we first performed differential expression analysis to screen for differentially expressed genes (DEGs) between OA and control samples. RHO GTPase-related DEGs were obtained by intersecting the DEG list with the RGPRG set, followed by feature selection with the Boruta and XGBoost algorithms. Candidate biomarkers were further evaluated by receiver operating characteristic (ROC) analysis and cross-dataset expression validation. The selected biomarkers were used to establish and validate an artificial neural network (ANN) model, and their biological associations were examined using gene set enrichment analysis (GSEA) and immune infiltration analysis. In GSE152805, key cell populations were identified and subsequently characterized by pseudotime, cell–cell communication, pathway activity, and metabolic analyses.&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;Analysis yielded 801 DEGs, 30 candidate genes, 14 candidate biomarkers, and 3 final biomarkers. The ANN achieved an area under the curve (AUC) of 0.940 in the training set and 0.833 in the validation set. Following Benjamini–Hochberg adjustment, VAV1, STMN2, and RND1 were associated with 34, 7, and 31 significant KEGG pathways, respectively, and five pathways were common to all three (adjusted &lt;i&gt;p&lt;/i&gt; &lt; 0.05 and |&lt;i&gt;N&lt;/i&gt;&lt;i&gt;E&lt;/i&gt;&lt;i&gt;S&lt;/i&gt;| &gt; 1). STMN2 also showed a significant positive association with natural killer (NK) cells (correlation coefficient (cor) = 0.604, &lt;i&gt;p&lt;/i&gt; &lt; 0.01). Fibroblasts were selected as the key cell population and displayed strong communication with endothelial cells. Along the fibroblast pseudotime trajectory, RND1 expression decreased initially, increased subsequently, and declined again at later stages. Single-cell Reactome analysis further indicated functional differences among fibroblast subpopulations involving cell interactions, immune signaling, transcriptional regulation, and metabolic processes.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 \u0000 &lt;h3&gt; Conclusion&lt;/h3&gt;\u0000 \u0000 &lt;p&gt;VAV1, STMN2, and RND1 emerged as reliable diagnostic biomarkers for OA and may provide molecular candidates for early detection and therapeutic investigation. Th","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13618371/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149074622","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}