Cancer Informatics最新文献

{"title":"Identification of Potential Hub Proteins as Theragnostic Targets in Hepatocellular Carcinoma through Comprehensive Quantitative Tissue Proteomics Analysis.","authors":"Quratul Abedin, Kulsoom Bibi, Alex von Kriegsheim, Zehra Hashim, Amber Ilyas","doi":"10.1177/11769351251336923","DOIUrl":"https://doi.org/10.1177/11769351251336923","url":null,"abstract":"<p><strong>Objective: </strong>Hepatocellular carcinoma (HCC) is the most common primary liver cancer mainly caused by hepatitis viral infection. Early stage diagnosis is still challenging due to its asymptomatic behavior so there is an urgent need for effective biomarkers. This study aimed to identify effective diagnostic biomarker or therapeutic target for HCC.</p><p><strong>Method: </strong>Label-free quantitative mass spectrometry was performed to analyze protein expression in HCC and control tissues. Protein-protein interaction (PPI) analysis was done using the STRING database and hub proteins were identified by Cytohubba. The survival analysis and expressions profiling of hub proteins were performed by using GEPIA. Functional and pathway enrichment analysis were carried out using Gene Ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG).</p><p><strong>Results: </strong>A total of 1539 proteins were identified, of which 116 were differentially expressed proteins (DEPs). PPI network analysis revealed 10 hub proteins; EGFR, GAPDH, HSP90AA1, MMP9, PTPRC, CD44, ANXA5, PECAM1, MMP2, and CDK1. Among these, GAPDH, MMP9, ANXA5, HSP90AA1, and CDK1 were significantly associated with low survival rate (<i>p</i> ⩽ .05). Moreover, MMP9 and CDK1 were showed significantly increased expression in tumor tissues as compared to control (<i>p</i> ⩽ .05). The GO analysis based on biological process, cellular components and molecular function indicated that DEPs were enriched in stress response, vesicle and extracellular space, protein binding and enzyme activity. The KEGG pathway analysis showed that the thyroid hormone synthesis pathway is the most enriched.</p><p><strong>Conclusion: </strong>The hub proteins GAPDH, HSP90AA1, MMP9, ANXA5, and CDK1 demonstrated significant prognostic potential, could be used as promising theragnostic biomarkers for HCC.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"24 ","pages":"11769351251336923"},"PeriodicalIF":2.4,"publicationDate":"2025-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12078984/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144080975","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":"Machine Learning Approach and Bioinformatics Analysis Discovered Key Genomic Signatures for Hepatitis B Virus-Associated Hepatocyte Remodeling and Hepatocellular Carcinoma.","authors":"Adane Adugna, Gashaw Azanaw Amare, Mohammed Jemal","doi":"10.1177/11769351251333847","DOIUrl":"https://doi.org/10.1177/11769351251333847","url":null,"abstract":"<p><p>Hepatitis B virus (HBV) causes liver cancer, which is the third most common cause of cancer-related death worldwide. Chronic inflammation via HBV in the host hepatocytes causes hepatocyte remodeling (hepatocyte transformation and immortalization) and hepatocellular carcinoma (HCC). Recognizing cancer stages accurately to optimize early screening and diagnosis is a primary concern in the outlook of HBV-induced hepatocyte remodeling and liver cancer. Genomic signatures play important roles in addressing this issue. Recently, machine learning (ML) models and bioinformatics analysis have become very important in discovering novel genomic signatures for the early diagnosis, treatment, and prognosis of HBV-induced hepatic cell remodeling and HCC. We discuss the recent literature on the ML approach and bioinformatics analysis revealed novel genomic signatures for diagnosing and forecasting HBV-associated hepatocyte remodeling and HCC. Various genomic signatures, including various microRNAs and their associated genes, long noncoding RNAs (lncRNAs), and small nucleolar RNAs (snoRNAs), have been discovered to be involved in the upregulation and downregulation of HBV-HCC. Moreover, these genetic biomarkers also affect different biological processes, such as proliferation, migration, circulation, assault, dissemination, antiapoptosis, mitogenesis, transformation, and angiogenesis in HBV-infected hepatocytes.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"24 ","pages":"11769351251333847"},"PeriodicalIF":2.4,"publicationDate":"2025-04-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12033511/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144001294","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":"DNA Methylation Biomarker Discovery for Colorectal Cancer Diagnosis Assistance Through Integrated Analysis.","authors":"Yi-Hsuan Tsai, Yi-Husan Lai, Shu-Jen Chen, Yi-Chiao Cheng, Tun-Wen Pai","doi":"10.1177/11769351251324545","DOIUrl":"https://doi.org/10.1177/11769351251324545","url":null,"abstract":"<p><strong>Objective: </strong>This study aimed to identify biomarkers for colorectal cancer (CRC) with representative gene functions and high classification accuracy in tissue and blood samples.</p><p><strong>Methods: </strong>We integrated CRC DNA methylation profiles from The Cancer Genome Atlas and comorbidity patterns of CRC to select biomarker candidates. We clustered these candidates near the promoter regions into multiple functional groups based on their functional annotations. To validate the selected biomarkers, we applied 3 machine learning techniques to construct models and compare their prediction performances.</p><p><strong>Results: </strong>The 10 screened genes showed significant methylation differences in both tissue and blood samples. Our test results showed that 3-gene combinations achieved outstanding classification performance. Selecting 3 representative biomarkers from different genetic functional clusters, the combination of <i>ADHFE1</i>, <i>ADAMTS5</i>, and <i>MIR129-2</i> exhibited the best performance across the 3 prediction models, achieving a Matthews correlation coefficient > .85 and an F1-score of .9.</p><p><strong>Conclusions: </strong>Using integrated DNA methylation analysis, we identified 3 CRC-related biomarkers with remarkable classification performance. These biomarkers can be used to design a practical clinical toolkit for CRC diagnosis assistance and may also serve as candidate biomarkers for further clinical experiments through liquid biopsies.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"24 ","pages":"11769351251324545"},"PeriodicalIF":2.4,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12033546/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144062685","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":"Twenty Year History of Cancer Informatics (CiX) - A Long and Established Legacy of Quality Research and Scientific Advances in the Field of Oncology.","authors":"Jimmy T Efird","doi":"10.1177/11769351251329712","DOIUrl":"10.1177/11769351251329712","url":null,"abstract":"<p><p>Over a 20 year period, the journal Cancer Informatics has played an important role defining and forging a bridge between bioinformations and translational cancer research. The main focus of the journal has been to advance the prevention, diagnosis, and treatment of cancer. This involves the specialized intersection of genomics, molecular biology, data science, computer programing, statistics, communication theory, and the clinical sciences to answer important questions in the field of cancer research.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"24 ","pages":"11769351251329712"},"PeriodicalIF":2.4,"publicationDate":"2025-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11938436/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143721704","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 Analysis Highlights Anti-apoptotic Subpopulation Promoting Malignant Progression and Predicting Prognosis in Bladder Cancer.","authors":"Linhuan Chen, Yangyang Hao, Tianzhang Zhai, Fan Yang, Shuqiu Chen, Xue Lin, Jian Li","doi":"10.1177/11769351251323569","DOIUrl":"10.1177/11769351251323569","url":null,"abstract":"<p><strong>Backgrounds: </strong>Bladder cancer (BLCA) has a high degree of intratumor heterogeneity, which significantly affects patient prognosis. We performed single-cell analysis of BLCA tumors and organoids to elucidate the underlying mechanisms.</p><p><strong>Methods: </strong>Single-cell RNA sequencing (scRNA-seq) data of BLCA samples were analyzed using Seurat, harmony, and infercnv for quality control, batch correction, and identification of malignant epithelial cells. Gene set enrichment analysis (GSEA), cell trajectory analysis, cell cycle analysis, and single-cell regulatory network inference and clustering (SCENIC) analysis explored the functional heterogeneity between malignant epithelial cell subpopulations. Cellchat was used to infer intercellular communication patterns. Co-expression analysis identified co-expression modules of the anti-apoptotic subpopulation. A prognostic model was constructed using hub genes and Cox regression, and nomogram analysis was performed. The tumor immune dysfunction and exclusion (TIDE) algorithm was applied to predict immunotherapy response.</p><p><strong>Results: </strong>Organoids recapitulated the cellular and mutational landscape of the parent tumor. BLCA progression was characterized by mesenchymal features, epithelial-mesenchymal transition (EMT), immune microenvironment remodeling, and metabolic reprograming. An anti-apoptotic tumor subpopulation was identified, characterized by aberrant gene expression, transcriptional instability, and a high mutational burden. Key regulators of this subpopulation included CEBPB, EGR1, ELF3, and EZH2. This subpopulation interacted with immune and stromal cells through signaling pathways such as FGF, CXCL, and VEGF to promote tumor progression. Myofibroblast cancer-associated fibroblasts (mCAFs) and inflammatory cancer-associated fibroblasts (iCAFs) differentially contributed to metastasis. Protein-protein interaction (PPI) network analysis identified functional modules related to apoptosis, proliferation, and metabolism in the anti-apoptotic subpopulation. A 5-gene risk model was developed to predict patient prognosis, which was significantly associated with immune checkpoint gene expression, suggesting potential implications for immunotherapy.</p><p><strong>Conclusions: </strong>We identified a distinct anti-apoptotic tumor subpopulation as a key driver of tumor progression with prognostic significance, laying the foundation for the development of new therapeutic strategies to improve patient outcomes.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"24 ","pages":"11769351251323569"},"PeriodicalIF":2.4,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11866393/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143524653","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":"An Immunogenic Cell Death-Related Gene Signature Predicts the Prognosis and Immune Infiltration of Cervical Cancer.","authors":"Fangfang Sun, Yuanyuan Sun, Hui Tian","doi":"10.1177/11769351251323239","DOIUrl":"10.1177/11769351251323239","url":null,"abstract":"<p><strong>Objectives: </strong>Immunogenic cell death (ICD) has been demonstrated to play a critical role in the development and progression of malignant tumors by modulating the anti-tumor immune response. However, its function in cervical cancer (CC) remains largely unexplored. In this study, we aimed to construct an ICD-related gene signature to predict patient prognosis and immune cell infiltration in CC.</p><p><strong>Methods: </strong>The gene expression profiles and clinical data of CC were downloaded from The Cancer Genome Alas (TCGA) and Gene Expression Omnibus (GEO) datasets, serving as the training and testing groups, respectively. An ICD-related gene signature was developed using the LASSO-Cox model. The expression levels of the associated ICD-related genes were evaluated using single-cell data, CC cell lines, and clinical samples in vitro.</p><p><strong>Results: </strong>Two ICD-associated subtypes (cluster 1 and cluster 2) were identified through consensus clustering. Patients classified into cluster 2 demonstrated higher levels of immune cell infiltration and exhibited a more favorable prognosis. Subsequently, an ICD-related gene signature comprising 3 genes (IL1B, IFNG, and FOXP3) was established for CC. Based on the median risk score, patients in both training and testing cohorts were segregated into high-risk and low-risk groups. Further analyses indicated that the estimated risk score functioned as an independent prognostic factor for CC and influenced immune cell abundance within the tumor microenvironment. The up-regulation of the identified ICD-related genes was further validated in CC cell lines and collected clinical samples.</p><p><strong>Conclusion: </strong>In summary, the stratification based on ICD-related genes demonstrated strong efficacy in predicting patient prognosis and immune cell infiltration, which also provides valuable new perspectives for the diagnosis and prognosis of CC.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"24 ","pages":"11769351251323239"},"PeriodicalIF":2.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851768/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143504494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Integrated Bioinformatic Analyses Reveal Thioredoxin as a Putative Marker of Cancer Stem Cells and Prognosis in Prostate Cancer.","authors":"Shigeru Sugiki, Tetsuhiro Horie, Kenshiro Kunii, Takuya Sakamoto, Yuka Nakamura, Ippei Chikazawa, Nobuyo Morita, Yasuhito Ishigaki, Katsuhito Miyazawa","doi":"10.1177/11769351251319872","DOIUrl":"10.1177/11769351251319872","url":null,"abstract":"<p><strong>Objectives: </strong>Prostate cancer stem cells (CSCs) play an important role in cancer cell survival, proliferation, metastasis, and recurrence; thus, removing CSCs is important for complete cancer removal. However, the mechanisms underlying CSC functions remain largely unknown, making it difficult to develop new anticancer drugs targeting CSCs. Herein, we aimed to identify novel factors that regulate stemness and predict prognosis.</p><p><strong>Methods: </strong>We reanalyzed 2 single-cell RNA sequencing data of prostate cancer (PCa) tissues using Seurat. We used gene set enrichment analysis (GSEA) to estimate CSCs and identified common upregulated genes in CSCs between these datasets. To investigate whether its expression levels change over CSC differentiation, we performed a trajectory analysis using monocle 3. In addition, GSEA helped us understand how the identified genes regulate stemness. Finally, to assess their clinical significance, we used the Cancer Genome Atlas database to evaluate their impact on prognosis.</p><p><strong>Results: </strong>The expression of thioredoxin (<i>TXN</i>), a redox enzyme, was approximately 1.2 times higher in prostate CSCs than in PCa cells (<i>P</i> < 1 × 10^-10), and <i>TXN</i> expression decreased over CSC differentiation. In addition, GSEA suggested that intracellular signaling pathways, including MYC, may be involved in stemness regulation by <i>TXN</i>. Furthermore, <i>TXN</i> expression correlated with poor prognosis (P < .05) in PCa patients with high stemness.</p><p><strong>Conclusions: </strong>Despite the limited sample size in our study and the need for further in vitro and in vivo experiments to demonstrate whether TXN functionally regulates prostate CSCs, our findings suggest that TXN may serve as a novel therapeutic target against CSCs. Moreover, TXN expression in CSCs could be a useful marker for predicting the prognosis of PCa patients.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"24 ","pages":"11769351251319872"},"PeriodicalIF":2.4,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11851766/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143504509","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 Molecular Subtypes and Prognostic Features of Breast Cancer Based on TGF-β Signaling-related Genes.","authors":"Jia Qu, Mei-Huan Wang, Yue-Hua Gao, Hua-Wei Zhang","doi":"10.1177/11769351251316398","DOIUrl":"10.1177/11769351251316398","url":null,"abstract":"<p><strong>Objectives: </strong>The TGF-β signaling pathway is widely acknowledged for its role in various aspects of cancer progression, including cellular invasion, epithelial-mesenchymal transition, and immunosuppression. Immune checkpoint inhibitors (ICIs) and pharmacological agents that target TGF-β offer significant potential as therapeutic options for cancer. However, the specific role of TGF-β in prognostic assessment and treatment strategies for breast cancer (BC) remains unclear.</p><p><strong>Methods: </strong>The Cancer Genome Atlas (TCGA) database was utilized to develop a predictive model incorporating five TGF-β signaling-related genes (TSRGs). The GSE161529 dataset from the Gene Expression Omnibus was employed to conduct single-cell analyses aimed at further elucidating the characteristics of these TSRGs. Additionally, an unsupervised clustering algorithm was applied to categorize BC patients into two distinct groups based on the five TSRGs, with a focus on immune response and overall survival (OS). Further investigations were conducted to explore variations in pharmacotherapy and the tumor microenvironment across different patient cohorts and clusters.</p><p><strong>Results: </strong>The predictive model for BC identified five TSRGs: FUT8, IFNG, ID3, KLF10, and PARD6A. Single-cell analysis revealed that IFNG is predominantly expressed in CD8+ T cells. Consensus clustering effectively categorized BC patients into two distinct clusters, with cluster B demonstrating a longer OS and a more favorable prognosis. Immunological assessments indicated a higher presence of immune checkpoints and immune cells in cluster B, suggesting a greater likelihood of responsiveness to ICIs.</p><p><strong>Conclusion: </strong>The findings of this study highlight the potential of the TGF-β signaling pathway for prognostic classification and the development of personalized treatment strategies for BC patients, thereby enhancing our understanding of its significance in BC prognosis.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"24 ","pages":"11769351251316398"},"PeriodicalIF":2.4,"publicationDate":"2025-02-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11789128/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143123797","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":"Cathepsin L in Lung Adenocarcinoma: Prognostic Significance and Immunotherapy Response Through a Multi Omics Perspective.","authors":"Jianming Lu, Jiaqi Liang, Gang Xiao, Zitao He, Guifang Yu, Le Zhang, Chao Cai, Gao Yi, Jianjiang Xie","doi":"10.1177/11769351241307492","DOIUrl":"10.1177/11769351241307492","url":null,"abstract":"<p><strong>Objectives: </strong>Lung adenocarcinoma (LUAD), a predominant form of lung cancer, is characterized by a high rate of metastasis and recurrence, leading to a poor prognosis for LUAD patients. This study aimed to identify and rigorously validate a highly precise biomarker, Cathepsin L (CTSL), for the prognostic prediction of lung adenocarcinoma.</p><p><strong>Methods: </strong>We employed a multicenter and omics-based approach, analyzing RNA sequencing data and mutation information from public databases such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). The DepMap portal with Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR/Cas9) technology was used to assess the functional impact of CTSL. Immunohistochemistry (IHC) was conducted on a local cohort to validate the prognostic significance of CTSL at the protein expression level.</p><p><strong>Results: </strong>Our findings revealed a significant correlation between elevated CTSL expression and advanced disease stage in LUAD patients. Kaplan-Meier survival analysis and Cox regression modeling revealed that high CTSL expression is associated with poor overall survival. The in vitro studies corroborated these findings, revealing notable suppression of tumor proliferation following CTSL knockout in cell lines, particularly in LUAD. Functional enrichment revealed that CTSL activated pathways associated with tumor progression, such as angiogenesis and Transforming growth factor beta (TGF-beta) signaling, and inhibited pathways such as apoptosis and DNA repair. Mutation analysis revealed distinct variations in the CTSL expression groups.</p><p><strong>Conclusion: </strong>This study highlights the crucial role of CTSL as a prognostic biomarker in LUAD. This combined multicenter and omics-based analysis provides comprehensive insights into the biological role of CTSL, supporting its potential as a target for therapeutic intervention and a marker for prognosis in patients with LUAD.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"23 ","pages":"11769351241307492"},"PeriodicalIF":2.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11648051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142839637","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":"Utilizing an In-silico Approach to Pinpoint Potential Biomarkers for Enhanced Early Detection of Colorectal Cancer.","authors":"Alireza Gharebaghi, Saeid Afshar, Leili Tapak, Hossein Ranjbar, Massoud Saidijam, Irina Dinu","doi":"10.1177/11769351241307163","DOIUrl":"10.1177/11769351241307163","url":null,"abstract":"<p><strong>Objectives: </strong>Colorectal cancer (CRC) is a prevalent disease characterized by significant dysregulation of gene expression. Non-invasive tests that utilize microRNAs (miRNAs) have shown promise for early CRC detection. This study aims to determine the association between miRNAs and key genes in CRC.</p><p><strong>Methods: </strong>Two datasets (GSE106817 and GSE23878) were extracted from the NCBI Gene Expression Omnibus database. Penalized logistic regression (PLR) and artificial neural networks (ANN) were used to identify relevant miRNAs and evaluate the classification accuracy of the selected miRNAs. The findings were validated through bipartite miRNA-mRNA interactions.</p><p><strong>Results: </strong>Our analysis identified 3 miRNAs: miR-1228, miR-6765-5p, and miR-6787-5p, achieving a total accuracy of over 90%. Based on the results of the mRNA-miRNA interaction network, CDK1 and MAD2L1 were identified as target genes of miR-6787-5p.</p><p><strong>Conclusions: </strong>Our results suggest that the identified miRNAs and target genes could serve as non-invasive biomarkers for diagnosing colorectal cancer, pending laboratory confirmation.</p>","PeriodicalId":35418,"journal":{"name":"Cancer Informatics","volume":"23 ","pages":"11769351241307163"},"PeriodicalIF":2.4,"publicationDate":"2024-12-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11648020/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142839639","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}