Cancer biomarkers : section A of Disease markers最新文献

{"title":"A causal association between Epstein-Barr virus infection and lung cancer: A two-sample Mendelian Randomization study.","authors":"Jia Wang, Kai Wang, Zhishang Wang","doi":"10.1177/18758592261463308","DOIUrl":"10.1177/18758592261463308","url":null,"abstract":"<p><p>BackgroundThe association between Epstein-Barr virus (EBV) and lung cancer risk remains controversial. Here, we used a two-sample Mendelian randomization (MR) analysis to test the causal relationship between EBV and lung cancer.MethodsData regarding lung cancer (outcomes) were collected from the Finnish database; the Genome-Wide Association Study (GWAS) summary-level dataset for EBV was obtained from the Open Forum Infect Dis. The inverse-variance weighted (IVW) method was used as a primary analytical approach; weighted median, MR-Egger, and weighted mode methods were used to ensure the robustness of the data. The MR-Egger regression assessed horizontal pleiotropy, and the MR pleiotropy residual sum and outlier (MR-PRESSO) method identified potential outliers. Cochran's Q test evaluated heterogeneity among instrumental variables (IVs).ResultsIVW analysis indicated several significant causal effects. Genetically elevated levels of EBV ZEBRA and EBNA-1 antibodies increased the risk of SQC (OR=1.26 and OR=1.33, respectively). Increased EBNA-1 antibodies also raised the risk of overall lung cancer and small cell lung cancer. Conversely, higher VCA p18 antibody levels were associated with a decreased risk of lung adenocarcinoma (OR=0.70). Sensitivity analyses suggested these findings were robust, with no significant evidence of horizontal pleiotropy or heterogeneity.ConclusionOur data suggests a causal effect between EBV and the progression of lung cancer.</p>","PeriodicalId":520578,"journal":{"name":"Cancer biomarkers : section A of Disease markers","volume":"43 ","pages":"18758592261463308"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13333053/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148383522","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":"Gene expression profiling of ovarian cancer reveals candidate diagnostic and prognostic biomarkers: An integrated bioinformatics approach.","authors":"Michael Woode, Mark Akuamoah Appah, Emmanuel Kpalakuso Alhassan, Timothy Makwo, Du-Bois Asante, Daniel Sakyi Agyirifo","doi":"10.1177/18758592261468586","DOIUrl":"10.1177/18758592261468586","url":null,"abstract":"<p><p>BackgroundOvarian cancer remains one of the most lethal gynecological malignancies, largely due to delayed diagnosis and limited effectiveness of current biomarkers. Identifying candidate biomarkers through integrated gene expression analysis may enhance understanding of OC biology and support future diagnostic and prognostic investigations.MethodsFour microarray datasets were retrieved from the Gene Expression Omnibus and analyzed using the limma package in R to identify differentially expressed genes (DEGs) through a bidirectional filter (|log₂FC| ≥ 1.2). Overlapping DEGs across all four datasets were subjected to Gene Ontology and KEGG pathway enrichment analyses. Protein-protein interaction networks were constructed using the STRING database, and hub genes were identified by consensus across three centrality algorithms in Cytoscape. Prognostic significance was evaluated using Kaplan-Meier survival analysis via GEPIA2; diagnostic performance was assessed using Youden Index-based ROC analysis; and external validation was performed using an independent dataset.ResultsA total of 209 overlapping DEGs were identified, yielding 35 hub genes. Functional enrichment analysis implicated cell cycle dysregulation, metabolic reprogramming, and extracellular matrix remodeling as central biological processes. TRIP13, FGF13, and LYVE1 were significantly associated with overall survival, while ALDH1A1, GATA6, and WNT5A were associated with disease-free survival. KDR demonstrated the highest diagnostic accuracy, and external validation confirmed the generalizability of the diagnostic findings.ConclusionThis study identifies a panel of candidate diagnostic and prognostic biomarkers in ovarian cancer, providing a molecular framework for future experimental validation and translational investigation.</p>","PeriodicalId":520578,"journal":{"name":"Cancer biomarkers : section A of Disease markers","volume":"43 ","pages":"18758592261468586"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13369421/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148440732","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":"A novel diagnostic model for early-stage high-grade serous ovarian cancer based on serum tRNA-derived fragments.","authors":"Xueyuan Zhao, Weijia Wen, Yan Jia, Li Yuan, Linna Chen, Songlin Liu, Haolin Fan, Hongye Jiang, Chaoyun Pan, Chunyu Zhang, Shuzhong Yao","doi":"10.1177/18758592261463300","DOIUrl":"10.1177/18758592261463300","url":null,"abstract":"<p><p>Early detection of ovarian cancer, particularly high-grade serous ovarian cancer (HGSOC), remains challenging due to the lack of reliable biomarkers. This study investigated the potential of tRNA-derived small RNAs (tsRNAs) as non-invasive diagnostic biomarkers using liquid biopsy. Using qRT-PCR, we analyzed the relative Ct values of selected tsRNAs in plasma samples from healthy controls and patients with early- and late-stage HGSOC. A logistic regression model constructed using the relative Ct value of the selected tsRNA demonstrated high sensitivity and specificity for distinguishing ovarian cancer patients from healthy controls, including those with early-stage disease. The model was validated in a multi-center cohort, and analysis confirmed the dysregulation of this tsRNA in tumor tissue compared to normal tissue. These findings suggest that this single plasma tsRNA may serve as a promising biomarker for early HGSOC detection, supporting its potential clinical utility pending further mechanistic studies.</p>","PeriodicalId":520578,"journal":{"name":"Cancer biomarkers : section A of Disease markers","volume":"43 ","pages":"18758592261463300"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13354906/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148428433","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":"Evaluation of radiomics and radiologic signatures for diagnosis and clinical outcomes in pancreatic adenocarcinoma and pancreatic neuroendocrine: A pilot study.","authors":"Robert Cg Martin, Marc W Fromer, Jacob Wh Gordon, Charles R Scoggins, Michael E Egger, Kelly M McMasters, Prejesh Philips","doi":"10.1177/18758592261455545","DOIUrl":"10.1177/18758592261455545","url":null,"abstract":"<p><p>BackgroundBiopsy is the only definitive method for diagnosing pancreatic ductal adenocarcinoma(PDAC) in the preoperative setting. With the increasing use of neoadjuvant therapy, alternative diagnostic modalities would aid in cases where fine-needle aspiration(FNA) is inconclusive or challenging. We hypothesized that radiomic signatures generated from computed tomography(CT) scans can help diagnose PDAC with high fidelity and radiologic/radiomics features have utility in predicting clinical outcomes.MethodsPatients with PDAC who underwent oncologic pancreatectomy from 2017-2019 were selected. Preoperative CT scans were reviewed by two body radiologists and two pancreatic surgeons. Tumors were segmented using two radiomics software platforms(Platform A&B).ResultsTwenty-five patients were included (n = 20 PDAC,n = 5 pancreatic neuroendocrine tumors(pNET)). Neoadjuvant chemotherapy was administered in 80% of PDAC patients, and 60% underwent pancreaticoduodenectomy. Platform A extracted 455 independent radiomics features and Platform B extracted 2078 radiomics features corresponding to shape, texture, filter, and intensity. One principal component from the shape feature group showed significant discrimination between PDAC and pNET, within both Platform A and B(area under the curve(AUC) = 0.81,p = 0.038 and AUC=0.89,p = 0.017, respectively). Additionally, a gray-level co-occurrence matrix sum entropy feature identified with Platform B differentiated malignant pancreatic tissue from normal parenchyma and PDAC from normal gland(p < 0.05).ConclusionRadiologic/radiomic features are reliable tools for PDAC diagnosis and associate with clinical outcomes. An improved understanding of PDAC radiomics may have implications for prognosis, margin status, and treatment response assessments.</p>","PeriodicalId":520578,"journal":{"name":"Cancer biomarkers : section A of Disease markers","volume":"43 ","pages":"18758592261455545"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13379644/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148475626","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 clinical assessment of miR-34a and miR-135b panel as promising diagnostic biomarkers predict the risk of colorectal cancer: A case-control study.","authors":"Roya Karami, Elaheh Najafi, Anahita Babaei, Erfan Shahdoust, Ali Shaghaghi, Nasrin Amiri-Dashatan, Behrooz Motlagh, Mehdi Koushki, Hadi Khodabandehloo","doi":"10.1177/18758592261429266","DOIUrl":"10.1177/18758592261429266","url":null,"abstract":"<p><p>BackgroundmiRNAs are of interest due to have been involved in cancers such as colorectal cancer (CRC).ObjectiveTo explore the expression levels of miRNA-34a and miRNA-135b and whether they can be potential biomarker for early diagnosis of CRC.MethodsFifteen pairs newly diagnosed colorectal cancer and normal involved in the measurement of tissue and serum levels of miRNAs 34a and 135b and their target genes of SIRT1 and DAPK-1. We assessed the predictive value of these miRNAs utilizing the receiver operating characteristic curve.ResultsThe findings have shown that the miRNA-34a expression was significantly downregulated and miRNA-135b was upregulated in CRC tissues in comparison to their expression in adjacent normal tissue. This was additionally observed in the serum of the same CRC cases when compared to individuals without CRC. Furthermore, we found a significant increase in the level of SIRT-1 and a significant reduction in the expression level of DAPK-1 in CRC tissues compared to the adjacent normal tissue. The value of AUC was considerably high for the timely detection of CRC.ConclusionsWe found these miRNAs acting as potential non-invasive biomarkers for the early detection of CRC and in assessing the CRC risk within the general population.</p>","PeriodicalId":520578,"journal":{"name":"Cancer biomarkers : section A of Disease markers","volume":"43 ","pages":"18758592261429266"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13254432/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148214038","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 impact of HER2 low positivity in hormone receptor-positive, HER2-negative breast cancer treated with neoadjuvant chemotherapy.","authors":"Mustafa Ersoy","doi":"10.1177/18758592261427515","DOIUrl":"10.1177/18758592261427515","url":null,"abstract":"<p><p>AimsThis study aimed to investigate whether the efficacy of neoadjuvant chemotherapy differs between HER2-low and HER2-zero subgroups in hormone receptor-positive, HER2-negative breast cancer.Materials and MethodsA total of 61 patients who received neoadjuvant chemotherapy between 2015 and 2025 were retrospectively analyzed. HER2 status was determined from pre-treatment core biopsy specimens and classified as HER2-low (IHC 1 + or 2 + and FISH-negative) or HER2-zero (IHC 0). The pathological complete response (pCR) rates were compared between the two groups.ResultsAmong 61 patients, 28 had HER2-low and 33 had HER2-zero tumors. The pCR rate was 7.1% in the HER2-low group and 27.3% in the HER2-zero group (p = 0.042). A post-hoc power analysis based on these results showed a moderate power (0.59) to detect the observed difference.ConclusionsHER2-low expression may negatively influence chemosensitivity in hormone receptor-positive breast cancer treated with neoadjuvant chemotherapy. Further studies are warranted to evaluate the potential role of trastuzumab deruxtecan (T-DXd) and other antibody-drug conjugates in this newly defined subgroup.</p>","PeriodicalId":520578,"journal":{"name":"Cancer biomarkers : section A of Disease markers","volume":"43 ","pages":"18758592261427515"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13055881/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147286861","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":"Detection of methylated SEPT9 DNA in peripheral blood for diagnosis of colorectal cancer.","authors":"Mengjie Lin, Hong Chen, Caifeng Jiang","doi":"10.1177/18758592261449027","DOIUrl":"10.1177/18758592261449027","url":null,"abstract":"<p><p>BackgroundColorectal cancer (CRC) remains a leading cause of cancer-related death worldwide. Although colonoscopy is effective screening method, its widespread adoption is hampered by poor compliance. Development of patient-friendly screening methods that are minimally invasive is of paramount importance to improve CRC screening participation.MethodsA single-center case-control study was conducted. Blood samples were collected from 119 participants (79 CRC patients, 20 patients with colorectal adenomas, and 20 healthy colonoscopy-negative controls) during May 2023 and March 2025. The methylated SEPT9 (mSEPT9) DNA in peripheral blood was analyzed by droplet digital polymerase chain reaction (ddPCR).ResultsThe sensitivity and specificity of mSEPT9 detection in peripheral blood for diagnosis of all stages of CRC patients was 68.35% and 95.00%, respectively. The positive rate were similar between left-sided CRC and right-sided CRC (65.31% vs 72.41%，p ＞ 0.05). mSEPT9 detection in all healthy people with normal colonoscopy were negative. When mSEPT9 detection was combined with CEA and CA199, positive rate could be raised to 82.67%.ConclusionsBlood-based mSEPT9 detection by ddPCR method is a sensitive and specific method for non-invasive diagnosis of CRC. Combination with CEA and CA19-9 could improve its performance. It may serve as a viable alternative for individuals unwilling or unable to undergo invasive procedures.</p>","PeriodicalId":520578,"journal":{"name":"Cancer biomarkers : section A of Disease markers","volume":"43 ","pages":"18758592261449027"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13167279/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147848881","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":"Development and verification of cell cycle-related gene markers for prognosis in bladder cancer.","authors":"Hao Li, Dandan Zhu","doi":"10.1177/18758592261449024","DOIUrl":"10.1177/18758592261449024","url":null,"abstract":"<p><p>BackgroundThe abnormal expression patterns of genes associated with cell cycle control are essential contributors to the initiation and progression of bladder cancer (BC).MethodsIn this research, transcriptomic profiles sourced from The Cancer Genome Atlas (TCGA) were examined to pinpoint cell cycle-related genes with significant expression differences between early-stage (I-II) and advanced-stage (III-IV) BC. The identified BC samples were then randomly split into training and validation groups. A prognostic model containing nine key genes was subsequently constructed from the training dataset using the least absolute shrinkage and selection operator (LASSO) regression algorithm, and its predictive reliability was independently confirmed in the validation dataset. Based on individual risk scores derived from this gene signature, patients were separated into high- and low-risk groups. Functional enrichment analyses (GO and KEGG), immune cell infiltration (CIBERSORT), and gene set variation analysis (GSVA) were performed to explore the biological functions and immune landscape associated with the risk signature.ResultsSurvival analysis revealed substantially worse overall survival (OS) in patients categorized as high-risk. The prognostic efficacy of the constructed gene signature was further corroborated by receiver operating characteristic (ROC) curve analyses. Functional enrichment analyses revealed that these genes were chiefly associated with critical biological processes such as cellular differentiation, proliferation, and migration, involving key signaling pathways like PI3K-Akt signaling, positive modulation of the MAPK cascade, and cell-matrix interactions. Immune infiltration analysis showed that tumors from patients in the high-risk group had increased infiltration by M2 macrophages. Furthermore, GSVA results indicated that stromal and angiogenic functions were enriched in the high-risk group.ConclusionsThese results highlight the potential clinical utility of the newly developed cell cycle-associated gene signature, providing effective biomarkers to facilitate personalized therapeutic decision-making for BC patients.</p>","PeriodicalId":520578,"journal":{"name":"Cancer biomarkers : section A of Disease markers","volume":"43 ","pages":"18758592261449024"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13191138/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147978603","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":"Stage-specific biomarkers in triple-negative breast cancer: Preliminary findings from bioinformatics approaches.","authors":"Faeze Shahraki, Azadeh Meshkini, Elham Nazari","doi":"10.1177/18758592261454886","DOIUrl":"10.1177/18758592261454886","url":null,"abstract":"<p><p>BackgroundIntegrated bioinformatics approaches were used to identify stage-specific candidate genes and potential drug targets in triple-negative breast cancer (TNBC).MethodsMicroarray (164 early-stage, 33 advanced-stage, and 53 normal samples) and RNA-seq (113 normal, 163 early-stage, and 30 advanced-stage TNBC samples) datasets were analyzed. Differentially expressed genes (DEGs) were identified, followed by co-expression analysis using Weighted Gene Co-expression Network Analysis (WGCNA) and protein-protein interaction analysis using the STRING database. miRNA co-regulation was evaluated using multiMiR and TCGA correlation analyses. Candidate genes were validated using UALCAN and immunohistochemistry data. Molecular docking assessed potential therapeutic agents.ResultsNovel stage-specific candidate biomarkers were identified, including <i>DNAJC6</i>, <i>SKP2</i>, <i>MOCOS</i>, and <i>NCAPD2</i> in early-stage TNBC, and <i>F11R</i>, <i>FOXO6</i>, <i>PPP4C</i>, and <i>TMEM51</i> in advanced-stage TNBC. UALCAN analysis confirmed the dysregulation of these genes across 23 additional malignancies. STRING-based network analysis revealed stage-specific protein-protein interactions, including SKP2-SKP1 in early-stage and F11R-TJP1 in advanced-stage TNBC. miRNA co-regulation distinguished early-stage TNBC through PI3K-AKT-related pathways and advanced-stage TNBC through tumor progression-associated pathways. Docking-based drug repurposing highlighted conventional agents (e.g., doxorubicin) and potential novel candidates (e.g., sunitinib).ConclusionThis study identifies novel stage-specific gene candidates and suggests repurposable drugs for TNBC, supporting progression-specific targeted therapeutic strategies.</p>","PeriodicalId":520578,"journal":{"name":"Cancer biomarkers : section A of Disease markers","volume":"43 ","pages":"18758592261454886"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13219939/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148045313","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":"In silico analysis suggests ELL2 as a survival-associated cross-tissue biomarker in gastric cancer.","authors":"Kiarash Zare, Zahra Salehi, Ali Reza Morovat, Ali Aghajani, Pardis Mohammadi Pour, Ali Ghanbariasad, Mohammad Mehdi Naghizadeh","doi":"10.1177/18758592261463305","DOIUrl":"10.1177/18758592261463305","url":null,"abstract":"<p><p>BackgroundThe main aim of this study is to identify prognostic biomarkers through integrating bioinformatics analysis in gastric cancer, which is a significant global health challenge.MethodsGene expression datasets related to blood, tissue, and saliva in gastric cancer were downloaded from the Gene Expression Omnibus (GEO) database and analyzed. The bioinformatics approaches included the identification of differentially expressed genes (DEGs) and enrichment analysis, as well as Kaplan-Meier Plotter survival analysis. The DEGs were also validated through The Cancer Genome Atlas (TCGA) database. Additionally, DEGs-associated lncRNAs and microRNAs were identified. Subsequently, Tumor and Immune System Interaction Database (TISIDB) was utilized to examine the correlation of the genes of interest with immune and molecular subtypes.ResultsTwenty-six common DEGs were identified across blood, tissue, and saliva samples. Among them, 17 genes showed significant expression based on TCGA data. RAB23, LOX, ELL2, ELK3, CENPF, CD44, ANP32E, AKR1C2, and SMAD5 displayed significant association with patient survival. Particularly, ELL2 exhibit decreased expression in all specimens. The results indicated that ELL2 has a significant correlation with the immune system. The ELL2 gene regulates immune cell functions in gastric cancer, potentially influencing cancer immune responses, and tumor progression.ConclusionELL2 downregulated expression and its correlation with survival across blood, tissue, and saliva samples using bioinformatics analysis underscores the necessity of more investigation to fully comprehend its function in cancer immunology.</p>","PeriodicalId":520578,"journal":{"name":"Cancer biomarkers : section A of Disease markers","volume":"43 ","pages":"18758592261463305"},"PeriodicalIF":1.9,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13305397/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148304584","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}