Evidence map›Paper›PMID 41582369›Full record

ArticleCurrent topics in medicinal chemistry2026

Discovery of Anoikis-correlated Biomarkers for Ovarian Cancer Through Integrated Transcriptome and Single-cell RNA Sequencing Analyses.

Xiaoping Jiang, Donghua An, Sumei Fan, Chenlian Quan, Hongling Zhu, Meiqin Zhang

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Article in Current topics in medicinal chemistry, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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6 authors.

Xiaoping JiangDepartment of Gynaecology, Armed Police Corps Hospital of Shanghai, Shanghai, 201103, China.ORCID 0009-0005-3109-1405
Donghua AnDepartment of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.ORCID 0009-0007-0630-2754
Sumei FanDepartment of Gynaecology, Fudan University Shanghai Cancer Center, Minhang Branch, Shanghai, 200240, China.ORCID 0009-0007-3361-8455
Chenlian QuanDepartment of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.ORCID 0009-0001-3138-5682
Hongling ZhuDepartment of Gynaecology, Armed Police Corps Hospital of Shanghai, Shanghai, 201103, China.ORCID 0009-0000-2007-972X
Meiqin ZhangDepartment of Gynecologic Oncology, Fudan University Shanghai Cancer Center, Shanghai, 200032, China.ORCID 0000-0003-3230-6250

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6 · The paper itself

Abstract

introductionOvarian cancer (OC) is a heterogeneous cancer with a high death rate and poor prognosis. Identifying precise and reliable prognostic biomarkers is crucial for the treatment of OC.

methodsAnoikis-related DEGs were screened from the differentially expressed genes (DEGs) between the OC group and the control group and then subjected to functional enrichment analysis. A protein-protein interaction (PPI) network was developed to obtain candidate genes. Then, LASSO regression and support vector machine-recursive feature elimination (SVM-RFE) analysis were employed to select biomarkers, followed by conducting gene set enrichment analysis (GSEA). Correlations between the biomarkers and immune infiltration, drug sensitivity, and immunotherapy response were assessed by the Spearman method. The expression of the biomarkers in cells was measured by scRNA-seq analysis.

resultsWe obtained 142 anoikis-related DEGs, which were mainly enriched in apoptosis-relevant pathways. A total of 16 candidate genes were acquired from the PPI network. Then, STAT3 and BCL2L1 were selected via LASSO regression and SVM-RFE analysis as two biomarkers for OC. BCL2L1 was closely associated with the infiltration of 5 immune cell types and 32 drugs, while STAT3 exhibited notable correlation with the infiltration of 6 immune cell types and 13 drugs. The Tumor Immune Dysfunction and Exclusion (TIDE) score was positively correlated with the two biomarkers. Moreover, STAT3 and BCL2L1 were expressed in most cells, with a high expression of STAT3 in endothelial cells. DISCUSSION: This study integrated bulk and single-cell transcriptomics to identify STAT3 and BCL2L1 as two anoikis-related biomarkers linked to the immune infiltration and drug sensitivity of OC, showing potential value for patient stratification and therapy. These findings suggested that targeting the STAT3/BCL-xL axis and combinational immunotherapy might be an effective strategy for OC treatment, which, however, should be further verified by functional and clinical experiments.

conclusionThis study identified two anoikis-related biomarkers for OC, contributing to the clinical diagnosis of OC and its treatment.

Indexed as

AnoikisBiomarkers, TumorOvarian NeoplasmsSequence Analysis, RNASingle-Cell AnalysisTranscriptomeFemaleHumansSingle-Cell Gene Expression AnalysisSTAT3 Transcription FactorBiomarkers, TumorSTAT3 Transcription FactorAnoikisBiomarkerLASSO regressionOvarian cancerscRNA-seqSVM-RFE

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PMID41582369

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