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Tian-Hao Zhou, 1,* Jing-Zhi Su, 2,* Rui Qin, 3 Xi Chen, 4 Gao-Da Ju, 5 Sen Miao 3 1Shanghai First People’s Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai 200080, People’s Republic of China; 2Department of Anatomy and Neurobiology, School of Basic Medical Science, Central South University, Changsha, Hunan, People’s Republic of China; 3Department of Pathology, Affiliated Hospital of Jining Medical University, Jining 272000, People’s Republic of China; 4Department of Internal Medicine, Qilu Hospital of Shandong University, Jinan 250000, People’s Republic of China; 5Department of Oncology, Beijing Cancer Hospital, Peking University, Beijing 102206, People’s Republic of China*These authors contributed equally to this workCorrespondence: Sen MiaoDepartment of Pathology, Affiliated Hospital of Jining Medical University, Jining 272000, People’s Republic of ChinaEmail miaosen128@163.comGao-Da JuDepartment of Oncology, Beijing Cancer Hospital, Peking University, Beijing 102206, People’s Republic of ChinaEmail jgd0412@163.comPurpose: Hepatocellular carcinoma (HCC) is one of the most devastating diseases worldwide. Limited performance of clinicopathologic parameters as prognostic factors underscores more accurate and effective biomarkers for high-confidence prognosis that guide decision-making for optimal treatment of HCC. The aim of the present study was to establish a novel panel to improve prognosis prediction of HCC patients, with a particular interest in transcription factors (TFs).Materials and Methods: A TF-related prognosis model of liver cancer with data from ICGC-LIRP-JI cohort successively were processed by univariate and multivariate Cox regression analysis. Then, for evaluating the prognostic prediction value of the model, receiver operating characteristic (ROC) curve and survival analysis were performed both with internal data from the International Cancer Genome Consortium (ICGC) and external data from The Cancer Genome Atlas (TCGA). Furthermore, we verified the expression of three genes in HCC cell lines by Western blot and qPCR and protein expression level by IHC in liver cancer patients’ sample. Finally, we constructed a TF clinical characteristics nomogram to furtherly predict liver cancer patient survival probability with TCGA cohort.Results: By Cox regression analysis, a panel of 15 TFs (ZNF331, MYCN, AHRR, LEF1, ZNF780A, POU1F1, DLX5, ZNF775, PLSCR1, FOXK1, TAL2, ZNF558, SOX9, TCFL5, GSC) was identified to present with powerful predictive performance for overall survival of HCC patients based on internal ICGC cohort and external TCGA cohort. A nomogram that integrates these factors was established, allowing efficient prediction of survival probabilities and displaying higher clinical utility.Conclusion: The 15-TF panel is an independent prognostic factor for HCC, and 15 TF-based nomogram might provide implication an effective approach for HCC patient management and treatment.Keywords: hepatocellular carcinoma, prognosis, transcription factor, overall survival |
