Evidence mapPaperPMID 42599115Full record

ArticleCancer reports (Hoboken, N.J.)2026

Therapeutic and Predicted Significance of PKLR in Patients With Cholangiocarcinoma.

Biyan Gong, Xuejun Xu, Tubing Xu, Dong Zhang, Li Yang, Cailing Li, Tingjun Li, Yuandeng Luo

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Article in Cancer reports (Hoboken, N.J.), 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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8 authors.

Biyan GongDepartment of General Surgery, Armed Police Corps Hospital, Chongqing, China.
Xuejun XuDepartment of Hepatobiliary Surgery, General Hospital of Xinjiang Military Command, Xinjiang, China.
Tubing XuDepartment of Thyroid and Breast Surgery, General Hospital of Xinjiang Military Command, Xinjiang, China.
Dong ZhangDepartment of Hepatobiliary Surgery, General Hospital of Xinjiang Military Command, Xinjiang, China.
Li YangDepartment of Hepatobiliary Surgery, General Hospital of Xinjiang Military Command, Xinjiang, China.
Cailing LiDepartment of Nursing, General Hospital of Xinjiang Military Command, Xinjiang, China.
Tingjun LiDepartment of Hepatobiliary Surgery, General Hospital of Xinjiang Military Command, Xinjiang, China.
Yuandeng LuoDepartment of Hepatobiliary Surgery, General Hospital of Xinjiang Military Command, Xinjiang, China.ORCID https://orcid.org/0000-0002-2554-5360

Funding

Natural Science Foundation of Chongqing General Project CSTB2023NSCQ-MSX1066Natural Science Foundation of Xinjiang Uygur Autonomous Region General Project for Tingjun Li
6 · The paper itself

Abstract

backgroundCholangiocarcinoma (CCA) is a highly heterogeneous malignancy with a poor prognosis. However, the molecular mechanisms underlying CCA and its treatment options remain unclear and limited.

aimsThis study aims to identify novel prognostic biomarkers and potential therapeutic targets for CCA through transcriptomics and whole-exome sequencing analysis. METHODS AND

resultsWe analyzed the relationship between overall survival and various risk factors in 255 CCA samples from a published dataset and 36 samples from The Cancer Genome Atlas (TCGA) database. Transcriptomics analysis was used to identify genes whose expression plays a crucial role in CCA development and progression. Targeted drug databases were used to examine which gene expression signatures represent potential therapeutic targets for CCA. Mutated genes were analyzed to explore potential mechanisms. Standard statistical methods were applied to analyze associations between clinical data and candidate targets. A nomogram was constructed to predict the prognostic impact of the optimal candidate. Cell experiments were used to demonstrate the role of PKLR in CCA growth and metastasis. We identified FOLH1, PKLR, PTH1R, GPR114, KCNH2, LCN2, MMP10, PORCN, RIPK3, and SRPK3 as potential therapeutic targets for CCA, with PKLR being the most promising candidate based on the computational analyses. PKLR was lowly expressed in CCA tissues. PKLR expression was closely associated with ARID1A and KRAS mutations. PKLR mRNA levels were significantly correlated with tumor size (p = 0.024) and metastasis (p = 0.014). Low PKLR expression in CCA was positively associated with poor survival. A nomogram accurately predicted the prognosis of CCA patients based on PKLR expression levels. Silencing PKLR expression promotes growth and metastasis of CCA in vitro.

conclusionOur findings indicate that PKLR, an independent protective factor in CCA, is a potential prognostic biomarker and a candidate for further investigation as a therapeutic target. PKLR expression is significantly associated with ARID1A and KRAS mutations.

Indexed as

Bile Duct NeoplasmsBiomarkers, TumorCholangiocarcinomaCell Line, TumorExome SequencingFemaleGene Expression ProfilingGene Expression Regulation, NeoplasticHumansMaleMiddle AgedMutationNomogramsPrognosisBiomarkers, Tumorcholangiocarcinomanomogramoverall survivalPKLRprognosis

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PMID42599115

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.