Comparative and Functional Genomics最新文献

{"title":"Multiomics Integration Reveals Coexpression Regulatory Networks in Osteoporosis and Osteosarcoma","authors":"Dongming Fu, Ran Li, Bo Wang","doi":"10.1155/ijog/9619903","DOIUrl":"10.1155/ijog/9619903","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Osteoporosis and osteosarcoma impose a substantial and rising global health burden, yet their transcriptomic landscapes have not been systematically compared. We performed a multiomics characterization integrating bulk transcriptomics, coexpression network analysis, immune deconvolution, single-cell RNA sequencing, and diagnostic classifier construction.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Bulk transcriptomic profiling was performed using GSE56815 (circulating blood monocytes, <i>n</i> = 80; 40 low-BMD/40 high-BMD) and the TARGET-OS database (<i>n</i> = 88 osteosarcoma tumor samples; DNA methylation <i>n</i> = 86; copy number <i>n</i> = 81). Differential expression was assessed by Welch′s <i>t</i>-test with Benjamini–Hochberg correction. Coexpression modules were identified by hierarchical clustering of the Top 2000 most variable probes with dynamic tree cutting. A logistic regression classifier was constructed from the Top 20 differentially expressed probes using fivefold-stratified cross-validation. Single-cell RNA sequencing of an osteosarcoma tumor specimen (GEO accession GSM9030790) was processed with SVD-based dimensionality reduction and K-means clustering (<i>k</i> = 8; <i>n</i> = 3658 cells passing QC). In vitro validation was conducted by qRT-PCR in MC3T3-E1 osteoblast precursor cells and RAW264.7 osteoclast precursor cells.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Differential expression analysis identified 4347 probe sets at nominal <i>p</i> < 0.05, of which 604 remained significant after Benjamini–Hochberg correction. Eight coexpression modules were identified; module M0 showed a weak positive association with low-BMD status (<i>r</i> = 0.184), which did not reach statistical significance. Six hub genes—SP7/Osterix, RUNX2, BMP2, WNT5A, DKK1, and SOST—were examined by qRT-PCR: SP7, RUNX2, BMP2, and WNT5A showed significant downregulation (<i>p</i> < 0.01), whereas DKK1 showed significant upregulation (<i>p</i> < 0.01) and SOST showed a nonsignificant trend toward upregulation under osteoporosis-simulating conditions. The diagnostic classifier achieved a moderate AUC of 0.693 (fivefold CV range: 0.48–0.91), with the lower bound falling below the random classifier baseline (AUC = 0.5), indicating limited discriminative capacity in some data partitions. scRNA-seq resolved eight transcriptionally distinct cell clusters in the osteosarcoma specimen (3658 cells passing QC).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>This integrative multiomics study provides a comprehensive transcripto","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13445564/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148684453","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 Interplay Between Platelet Indices and Rheumatoid Arthritis: Genetic Evidence From Bidirectional Mendelian Randomization","authors":"Xu-Yan Shen,&nbsp;Jia-Ying Sun,&nbsp;Xin Wang,&nbsp;Shu-Shan Zhao","doi":"10.1155/ijog/4549330","DOIUrl":"https://doi.org/10.1155/ijog/4549330","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Objectives</h3>\u0000 \u0000 <p>This study is aimed at comprehensively understanding genetically causal associations between some platelet indices (PIs) and rheumatoid arthritis (RA).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Genetic summary statistics for the 4 types of PIs and RA were derived from Neale Lab, FinnGen, and MRC-IEU consortium. Two-sample Mendelian randomization (TSMR) analysis was utilized to infer the bidirectional causality with the implementation of the inverse-variance weighted (IVW), weighted median (WM), weighted mode, and MR-Egger methods. Sensitivity analysis with leave-one-out method was conducted to assess the robustness of the observed causal estimates.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>TSMR results indicated that the genetically-determined plateletcrit (PCT) had a causal association with the higher risk of RA (odds ratio [OR] = 1.13, 95% confidence interval [CI]: 1.01, 1.29, <i>p</i> = 0.012) and seropositive RA (OR = 1.03, 95% CI: 1.01, 1.21, <i>p</i> = 0.003). Both WM method and sensitivity analysis supported that the observed causal estimates were reliable. In the reverse MR analysis, genetic susceptibility leading to RA (Beta [se] = −0.012 [0.006], <i>p</i> = 0.037) was causally related to the decrease in mean platelet volume (MPV).</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusions</h3>\u0000 \u0000 <p>Our study reveals a positive causal association between PCT and the risk of RA, and that genetic susceptibility to RA is causally associated with a reduced MPV. This provides a reference for further studies on the exploration of mechanisms of platelets in the pathogenesis of RA.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ijog/4549330","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616715","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":"Ubiquitin-Proteasome System–Related Prognostic Model and Immune Landscape in Glioblastoma","authors":"Da Huo,&nbsp;Yue Feng,&nbsp;Zheng Yu,&nbsp;Wanting Xie,&nbsp;Hai Jin","doi":"10.1155/ijog/2045937","DOIUrl":"10.1155/ijog/2045937","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Glioblastoma (GBM) is a highly aggressive brain tumor with poor prognosis. This study is aimed at establishing an ubiquitin-proteasome system (UPS)–related prognostic model and investigating its link to immune infiltration and therapy response.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Materials and Methods</h3>\u0000 \u0000 <p>GBM datasets were obtained from public databases. Ubiquitin-proteasome system–related genes (UPSGs) were identified from literature. Consensus clustering defined UPS-based GBM subtypes. Differentially expressed genes (DEGs) were screened, and a prognostic model was constructed using univariate Cox, least absolute shrinkage and selection operator (LASSO), and stepwise regression. The model′s performance was validated using survival analysis and time-dependent receiver operating characteristic (ROC) curves. Immune infiltration was assessed using single-sample gene set enrichment analysis (ssGSEA), TIMER, and ESTIMATE. Drug sensitivity was assessed by correlating the half-maximal inhibitory concentration (IC₅₀) of candidate drugs with the risk score. Single-cell RNA sequencing data were used to characterize UPSG expression across distinct cell subpopulations in GBM. For in vitro validation, key UPSGs were silenced in GBM cell lines, and cell proliferation, migration, and invasion were measured using Cell Counting Kit-8 (CCK-8), wound healing, and Transwell assays, respectively.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>Two UPS-related GBM subtypes were identified. Six genes (<i>IGFBP6</i>, <i>CTSD</i>, <i>SPAG4</i>, <i>ZNF560</i>, <i>COL22A1</i>, and <i>HOXC13</i>) formed the prognostic model, where high Riskscore indicated poor survival. High Riskscore correlated with greater immune infiltration, including CD8⁺ T cells and macrophages. IC₅₀ values of 24 drugs were significantly associated with Riskscore. Single-cell analysis revealed seven GBM subpopulations; notably, <i>COL22A1</i> was enriched in MES-like cells, and CTSD in macrophages. <i>IGFBP6</i> promoted GBM cell proliferation, migration, and invasion.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>This study establishes a UPS-based prognostic model for GBM that links immune infiltration and drug sensitivity, providing potential biomarkers and therapeutic targets for GBM.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13390022/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148561786","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":"Urinary Microbiota in Female Patients With Dry and Wet Overactive Bladder (OAB)","authors":"Xiao Chen,&nbsp;Hui Zou,&nbsp;Jia Wang,&nbsp;Yiran Sun,&nbsp;Mingxia Zhang,&nbsp;Hao Hu,&nbsp;Kexin Xu","doi":"10.1155/ijog/5936606","DOIUrl":"10.1155/ijog/5936606","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Purpose</h3>\u0000 \u0000 <p>This study is aimed at comparing the urinary microbiota between OAB patients and healthy controls and at assessing whether urinary incontinence status affects the urinary microbiota in OAB patients.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>From April 2021 to October 2022, female patients admitted to our hospital in Beijing with an Overactive Bladder Symptom Score (OABSS) greater than 3 were selected. Healthy female individuals were chosen as the control group. Urine specimens were collected and subjected to 16S rRNA sequencing. The data obtained were analyzed using bioinformatics techniques.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>The average age, OABSS score, and alpha diversity index in the OAB group were significantly higher than those in the control group. At the genus level, the most abundant genus in the OABWet group was Gardnerella (19.69%), and the most abundant genus in the OABDry group was Lactobacillus (12.26%). In the control group, the most abundant genus was also Lactobacillus (19.90%). The OABWet group had significantly higher relative abundances of Gardnerella and Ciliophora compared to the OABDry group, and Ciliophora was more abundant in the control group. Veillonella was less abundant in the OABDry group, while Prevotella and Pseudomonas were less abundant in the control group. Gardnerella was also less abundant in the OABDry group than in the control group.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>The urinary microbiota of female OAB patients showed lower species richness and lacked dominant bacterial communities compared to the healthy control group. In wet-type OAB patients, there was not only a decreased abundance of Lactobacillus but also a high abundance of Gardnerella in the urine.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13383003/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148602511","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 Transcriptomic Profiling Reveals Cellular Heterogeneity and Identifies Novel Therapeutic Targets in Osteosarcoma","authors":"Hui Li,&nbsp;Changjiang Sun,&nbsp;Minjie Yang","doi":"10.1155/ijog/4040246","DOIUrl":"https://doi.org/10.1155/ijog/4040246","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;Osteosarcoma is the most prevalent primary malignant bone tumor predominantly affecting children and adolescents, yet prognosis for metastatic disease remains dismal. Understanding the cellular complexity within the tumor microenvironment is essential for developing targeted therapeutic strategies.&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 comprehensive single-cell RNA sequencing analysis on an osteosarcoma tissue sample (GSM4952363) using the Seurat pipeline (v4.3.0). Following rigorous quality control (200–6000 genes per cell, &lt; 15% mitochondrial reads), cells were filtered for downstream analysis. Dimensionality reduction (PCA and UMAP) and unsupervised clustering (Louvain algorithm, resolution = 0.8) identified seven distinct cellular clusters. Differential expression analysis (Wilcoxon rank-sum test, |log₂FC| &gt; 0.25, adjusted &lt;i&gt;p&lt;/i&gt; &lt; 0.05) identified cluster-specific markers, while Gene Ontology and KEGG pathway enrichment analyses (clusterProfiler, adjusted &lt;i&gt;p&lt;/i&gt; &lt; 0.05) revealed functional programs. Four candidate genes (F11, ACRP2, LEPR, and POSTN) were selected for validation by quantitative real-time PCR (mRNA level) and ELISA (protein level) in MG-63 osteosarcoma cells compared to hFOB 1.19 normal osteoblasts.&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;Single-cell transcriptomic profiling identified seven distinct cellular clusters within the osteosarcoma microenvironment, including macrophages (Cluster 0, 28.0%), osteoblasts (Cluster 1, 24.2%), fibroblasts (Cluster 2, Fibro_COMP, 14.7%), proliferating cells (Cluster 3, 12.3%), osteoclasts (Cluster 4, 11.6%), monocytes (Cluster 5, 6.3%), and T cells (Cluster 6, 2.9%). Functional enrichment analysis highlighted activation of PI3K-Akt signaling, focal adhesion, and extracellular matrix organization as core pathways. qRT-PCR validation (mRNA level) demonstrated that F11 was significantly downregulated (0.31 ± 0.04 vs. 1.00 ± 0.07, 69% reduction, &lt;i&gt;p&lt;/i&gt; &lt; 0.001), whereas ACRP2 (2.87 ± 0.33-fold), LEPR (4.52 ± 0.48-fold), and POSTN (6.23 ± 0.57-fold) were significantly upregulated (all &lt;i&gt;p&lt;/i&gt; &lt; 0.001). ELISA validation (protein level) confirmed consistent trends: F11 protein decreased by 65% (0.35 ± 0.05 vs. 1.00 ± 0.08, &lt;i&gt;p&lt;/i&gt; &lt; 0.001), whereas ACRP2 (2.64 ± 0.29-fold), LEPR (4.18 ± 0.44-fold), and POSTN (5.89 ± 0.53-fold) protein levels were elevated (all &lt;i&gt;p&lt;/i&gt; &lt; 0.001). Among the four candidates, POSTN exhibited the most pronounced changes at both mRNA and protein levels, suggesting its involvement in osteosarcoma matrix remodeling.&lt;/p&gt;\u0000 &lt;/section&gt;\u0000 \u0000 &lt;section&gt;\u0000 ","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ijog/4040246","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148467639","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":"Arg1 and Tgfb1 Identify a Partially Shared Macrophage Polarization Program in Postinfarction Cardiac Repair: A Single-Cell Transcriptomic Analysis","authors":"Baobao Liu,&nbsp;Fuli Liang","doi":"10.1155/ijog/5757408","DOIUrl":"https://doi.org/10.1155/ijog/5757408","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;Postinfarction cardiac repair is orchestrated by macrophages that undergo sequential polarization from proinflammatory M1 to reparative M2 states. These macrophage subsets regulate inflammation resolution, angiogenesis, and fibrotic remodeling in the infarcted myocardium. Tumor-associated macrophages (TAMs) in breast cancer share transcriptional features with cardiac M2 macrophages, providing an opportunity for cross-disease comparison of shared immune programs. Understanding the molecular programs common to reparative macrophages across pathological contexts may identify candidate regulatory nodes for future therapeutic investigation.&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;Single-cell RNA sequencing data were obtained from GEO: GSE136088 (murine post-AMI cardiac macrophages), GSE176078 and GSE167036 (human primary breast cancer). Analyses included quality control, UMAP/t-SNE dimensionality reduction, graph-based clustering, diffusion pseudotime trajectory reconstruction, ligand-receptor communication analysis, principal component analysis, and cross-dataset correlation analysis. All analyses were performed in Python using scanpy with cross-species ortholog mapping in R (biomaRt, 14,892 one-to-one mouse-human orthologs) with downstream analyses in Python (scanpy).&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;Post-QC datasets (GSE136088: ~14,900 cells; GSE176078: 100,064 cells; and GSE167036: 49,141 cells) were analyzed. UMAP and t-SNE resolved multiple macrophage and immune subpopulations across all three datasets. Diffusion pseudotime analysis reconstructed a sequential activation trajectory with an M1/M2 fate bifurcation. A 7-gene coexpression module (Spp1, Mif, Tgfb1, Arg1, Il10, Tnf, and Mrc1) was identified in cardiac macrophages and showed partially conserved expression patterns in breast cancer macrophage populations (GSE176078: Pearson r = 0.72; GSE167036: 6 of 7 genes detected, ARG1 not detected). Ligand-receptor communication analysis identified MIF-ACKR3, SPP1-CD44, and TGFB1-TGFBR2 as shared ligand-receptor pairs mediating macrophage-stromal communication in both cardiac and tumor microenvironments. Cross-dataset Pearson correlation of the 7-gene module between cardiac macrophages and breast cancer macrophage populations reached &lt;i&gt;r&lt;/i&gt; = 0.72 (GSE176078) and &lt;i&gt;r&lt;/i&gt; = −0.07 (GSE167036, n.s.).&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 single-cell transcriptomic analysis characterizes macrophage polarization programs in postinfarction cardiac repair and evaluates their partial conservation","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ijog/5757408","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148467638","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":"Overexpression of lncRNA ATP11A-AS1 in Colorectal Cancer and Its Association With H. pylori Infection","authors":"Nima Hagholshahri,&nbsp;Nashwah Jabbar Kadhim Muttwaqi,&nbsp;Reza Safaralizadeh,&nbsp;Mohammadali Hosseinpour Feizi,&nbsp;Ali Rajabi","doi":"10.1155/ijog/9351063","DOIUrl":"10.1155/ijog/9351063","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Colorectal cancer (CRC), the third most common cancer globally, is a leading cause of cancer-related mortality. <i>Helicobacter pylori</i> infection, a risk factor for CRC, promotes carcinogenesis via inflammation and microbiota disruption. Long noncoding RNAs (lncRNAs), such as ATP11A-AS1, regulate key processes in CRC progression, including proliferation and invasion. However, the role of ATP11A-AS1 in CRC and its association with <i>H. pylori</i> infection remain underexplored. This study quantified ATP11A-AS1 expression in CRC tumors compared with adjacent nontumor tissues and investigated its relationship with <i>H. pylori</i> infection and clinicopathological features.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p>Total RNA was extracted from 100 paired tumor and adjacent nontumor tissues collected from CRC patients (53 males, 47 females, mean age: 56 ± 6.72). Following cDNA synthesis, ATP11A-AS1 expression was measured via qRT-PCR. Associations with clinicopathological factors (age, gender, tumor site, histology, stage, lymph node metastasis, <i>H. pylori</i> status, family history of CRC, alcohol consumption, and diabetes) were analyzed using Mann–Whitney tests. Multivariate binary logistic regression, adjusted for age, sex, and tumor site, was applied to clinicopathological outcomes.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p><i>ATP11A-AS1</i> expression was significantly upregulated in tumor tissues compared with nontumor tissues (<i>p</i> &lt; 0.0001). Among 59 <i>H. pylori</i>-positive patients, expression was higher (<i>p</i> = 0.036). No significant associations were found with other features. Multivariate analysis confirmed no independent associations (all <i>p</i> &gt; 0.05). ROC curve analysis yielded an AUC of 0.69, with sensitivity of 63% and specificity of 68%, indicating that <i>ATP11A-AS1</i> is a weak biomarker for CRC detection.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>ATP11A-AS1 is overexpressed in CRC and associated with <i>H. pylori</i> infection, suggesting a potential role in CRC pathogenesis. Further studies are needed to elucidate its molecular mechanisms and clinical significance in <i>H. pylori</i>-related CRC.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13365877/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148446180","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":"Genomic Identification and Comprehensive Characterization of the CAMTA Transcription Factor Family in Brassica rapa L.","authors":"Jannatul Afrin,&nbsp;Maria Chowdhory,&nbsp;Subroto Das Jyoti,&nbsp;Jaber Bin Azim,&nbsp;Nikunjo Chakroborty,&nbsp;Arif Hasan Khan Robin","doi":"10.1155/ijog/1676159","DOIUrl":"10.1155/ijog/1676159","url":null,"abstract":"<p>Calmodulin-binding transcription activators (<i>CAMTAs</i>) are a key family of transcription factors that regulate gene expression involved in plant development and mediate plant responses to a wide range of biotic and abiotic stresses. Although <i>CAMTA</i> genes have been surveyed in several crop species, their comprehensive characterization in <i>Brassica rapa</i> remains limited. In this study, we performed a genome-wide analysis and identified 12 <i>BrCAMTA</i> genes, dispersed across 10 chromosomes of the <i>B. rapa</i> genome. Phylogenetic analysis of 84 CAMTA proteins from seven plant species grouped them into four major clades, with BrCAMTAs exhibiting conserved exon–intron structures and motif compositions within their respective phylogenetic groups. Domain analysis revealed that the major CG-1 domain is conserved across all the genes. Protein–protein interaction analysis indicated that the predicted interacting proteins contain diverse domains and exhibit a variety of functions. Promoter analysis revealed a wide array of cis-regulatory elements linked to hormone signaling, stress responses, and developmental regulation. RNA-seq–based expression profiling revealed tissue-specific expression patterns, with <i>BrCAMTA</i> genes showing high expression in roots, stems, and callus tissues. Notably, <i>BrCAMTA6</i>, <i>BrCAMTA8</i>, and <i>BrCAMTA11</i> exhibited significantly higher expression across all examined tissues. Collectively, these findings highlight the structural conservation and functional diversity of <i>CAMTA</i> genes in <i>B. rapa</i> and provide a valuable framework for future validation studies that are aimed at understanding their roles in growth, development, and environmental adaptation.</p>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13355293/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148420810","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 RNA Sequencing Reveals EMT and Stemness Signatures in the Gastric Cancer Microenvironment: Implications for Sevoflurane Anesthesia in Perioperative Management","authors":"Lijun Huang,&nbsp;Ya Shao,&nbsp;Cixian Hu,&nbsp;Feifei Gu,&nbsp;Liming Zhao","doi":"10.1155/ijog/6575874","DOIUrl":"https://doi.org/10.1155/ijog/6575874","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;Gastric cancer (GC) remains one of the most prevalent malignancies worldwide, with cancer stem cells (CSCs) emerging as critical drivers of tumor progression, therapeutic resistance, and recurrence. Sevoflurane, a commonly used volatile anesthetic in surgical oncology, has been implicated in modulating cancer cell biology through various molecular mechanisms. However, the effects of sevoflurane exposure on GC stem cell gene expression profiles and the tumor microenvironment remain poorly understood at single-cell resolution. Therefore, this study is aimed at characterizing the transcriptional landscape of the GC microenvironment and directly assessing sevoflurane effects on GC cell phenotype through in vitro experiments using single-cell RNA sequencing (scRNA-seq) technology.&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;scRNA-seq data from the publicly available GEO dataset GSE134520, comprising 13 gastric antral mucosa biopsies from 9 patients across premalignant and early malignant disease stages, were reanalyzed. Comprehensive quality control, normalization, batch effect correction (Harmony), and dimensionality reduction were performed using the Seurat package. Cell type annotation was conducted through marker gene expression analysis and reference-based approaches. Differential expression analysis was performed to identify genes significantly altered during GC progression, with particular focus on pathways known to be modulated by volatile anesthetics including sevoflurane. Cell–cell communication analysis, trajectory inference, gene regulatory network analysis, and pathway enrichment analysis were conducted to investigate molecular mechanisms relevant to GC progression and anesthetic-cancer interactions. Specific focus was placed on epithelial–mesenchymal transition (EMT), stemness signatures, and immune cell interactions. In addition, flow cytometry was performed in AGS GC cells to validate sevoflurane-associated changes in CD44 and E-cadherin expression.&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;Through comprehensive single-cell analysis, we identified 14 distinct cellular populations including tumor epithelial cells, cancer-associated fibroblasts (CAFs), CD4+ T cells, CD8+ T cells, macrophages, B cells, NK cells, endothelial cells, and normal epithelial cells within the GC microenvironment. Quality control metrics demonstrated high-quality data with appropriate filtering parameters. Principal component analysis revealed that approximately 15 principal components captured 80% of the variance. Differential expression analysis identified key genes including AREG, MUC5AC, S100P, CLDN18, and GPX2 significantly upr","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/ijog/6575874","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148466673","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":"Genotype and Cardiac Rhabdomyoma Phenotype Analyses in Tuberous Sclerosis Complex Diseases","authors":"Xiang Chen,&nbsp;Ruen Yao,&nbsp;Wangtao Sheng,&nbsp;Jiaqi Li,&nbsp;Yiwei Chen,&nbsp;Lijun Fu,&nbsp;Fei Bei","doi":"10.1155/ijog/2204583","DOIUrl":"10.1155/ijog/2204583","url":null,"abstract":"<div>\u0000 \u0000 <section>\u0000 \u0000 <h3> Background</h3>\u0000 \u0000 <p>Cardiac rhabdomyomas (CRs) are one of the typical phenotypes of tuberous sclerosis complex (TSC) diseases. Patients with CR could present various phenotypes and different severities. <i>TSC1</i> and <i>TSC2</i> are candidate genes for TSC. The genotype and phenotype relationship of the CR phenotype in TSC is unknown.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Methods</h3>\u0000 \u0000 <p><i>TSC1</i> and <i>TSC2</i> pathogenic and likely pathogenic variants from the HGMD, ClinVar, and LOVD databases were identified (last date: 2024.12.01). After critical exclusion criteria and pathogenicity reanalyses, statistical analyses were performed for enrichment evaluation.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Results</h3>\u0000 \u0000 <p>In this study, 1250 variants of <i>TSC1</i> were finally included, and 26 variants (20.8%) were reported to cause the CR phenotype. In Exon 15, 5.1% of them were CR-related (<i>p</i> &lt; 0.001), and 7.1% of them were enriched in Exon 18 (<i>p</i> = 0.008). After adjusting by the Benjamini–Hochberg FDR method, the FDR-adjusted <i>p</i> values were still significant for Exon 15 (<i>p</i> = 0.006) and Exon 18 (<i>p</i> = 0.028). Considering the size of each exon, there is no significant enrichment by Poisson model analysis. We included 2690 variants of <i>TSC2</i>; 117 variants (4.3%) were reported to cause the CR phenotype. In Exon 41, 12% of variants were CR-related (<i>p</i> = 0.003). The FDR-adjusted <i>p</i> value was not significant for Exon 41 (<i>p</i> = 0.11). Considering the size of each exon, in Poisson model analysis, there are significant enrichments detected in Exon 37 (IRR = 2.72 [1.42, 5.19], <i>p</i> = 0.003), Exon 38 (IRR = 3.26 [1.65, 6.44], <i>p</i> = 0.001), and Exon 41 (IRR = 5.79 [3.11, 10.77], <i>p</i> &lt; 0.001). After adjusting by the Benjamini–Hochberg FDR method, significant differences remained in these three exons.</p>\u0000 </section>\u0000 \u0000 <section>\u0000 \u0000 <h3> Conclusion</h3>\u0000 \u0000 <p>CR phenotypes demonstrated partial enrichment in specific exons, highlighting the importance of exon-level interpretation during genetic counseling.</p>\u0000 </section>\u0000 </div>","PeriodicalId":55239,"journal":{"name":"Comparative and Functional Genomics","volume":"2026 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13324241/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148375598","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}