A chemoresistance-associated proliferation gene signature identified by single-cell and machine learning analysis predicts prognosis in high-grade serous ovarian carcinoma.

Yanmin Zhang, Sihuang Wu, Huizhu Lin, Ling Li

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Article in Translational oncology, 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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Yanmin ZhangDepartment of Gynecology and Obstetrics, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450014, China; Henan Gynecological Diseases (Gynecology Oncology) Clinical Research Center, The Second Affiliated Hospital of Zhengzhou University, Zhengzhou 450014, China.

Sihuang WuCell-Gene Therapy Translational Medicine Research Centre, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China; Biotherapy Center, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China. Electronic address: wush85@mail.sysu.edu.cn.

Huizhu LinCell-Gene Therapy Translational Medicine Research Centre, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China; Biotherapy Center, The Third Affiliated Hospital, Sun Yat-Sen University, Guangzhou, 510630, China. Electronic address: linhzh7@mail.sysu.edu.cn.

Ling LiDepartment of Gynecology and Obstetrics, High-Risk Perinatal Medicine Center, The Third Affiliated Hospital of Sun Yat-Sen University, Guangzhou, 510630, China. Electronic address: liling48@mail.sysu.edu.cn.

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6 · The paper itself

Abstract

backgroundHigh-grade serous ovarian carcinoma (HGSOC) features extensive intratumoral heterogeneity, frequent chemoresistance and poor prognosis. Tumor proliferation kinetics reflected by tumor doubling time (TDT) are linked to therapeutic response, yet molecular drivers of chemoresistance-associated proliferation remain incompletely defined.

methodsWe integrated scRNA-seq (GSE154600) with bulk data (TCGA-OV, GTEx). Differential analyses of refractory/resistant versus sensitive tumors and tumor versus normal tissues, intersected with TDT-associated genes, identified 105 chemoresistance-associated proliferation genes. Consensus clustering and immune analyses were performed. A 117-algorithm machine-learning framework constructed a prognostic signature trained on TCGA-OV and validated in GSE26193. Survival was assessed by Kaplan-Meier Plotter. Hub genes were validated by qPCR and Western blot in paired sensitive (A2780, TYKnu) and cisplatin-resistant (A2780-DDP, TYKnu-DDP) cell lines.

resultsThe genes enriched in cell-cycle and p53 signaling. Consensus clustering defined C1 and C2 subtypes with distinct immune microenvironments; C2 showed upregulation of DNA replication and cell-cycle programs. A nine-gene signature (BIRC5, CENPH, CKAP2, PAK1IP1, PBK, SDF2L1, TEAD4, TPM3, UBE2T) was established. High CENPH, CKAP2, PBK, TEAD4, TPM3 and UBE2T associated with inferior overall survival, while SDF2L1 was protective. These genes were predominantly expressed in malignant epithelial cells, SPP1+/TREM2+ macrophages and CAFs. qPCR confirmed upregulation of six genes in TYKnu-DDP cells; Western blot validated elevated SURVIVIN (BIRC5), CENPH, CKAP2, PAK1IP1 and PBK in resistant lines.

conclusionsThis single-cell landscape of chemoresistance-associated proliferation genes delineates a nine-gene prognostic signature for HGSOC and confirms hub-gene overexpression in cisplatin-resistant cells, nominating therapeutic targets.

Indexed as

Chemoresistance-associated proliferation genesHigh‑grade serous ovarian carcinomaMachine learningPrognostic signatureSingle‑cell transcriptomics

Identifiers

PMID42696959

DOI10.1016/j.tranon.2026.103018

PMCPMC13679619

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