Int J Biol Sci 2022; 18(1):360-373. doi:10.7150/ijbs.66913 This issue

Research Paper

Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients

Dongjie Chen1*, Jixing Liu1,2*, Longjun Zang1, Tijun Xiao3, Xianlin Zhang4, Zheng Li4, Hongwei Zhu1✉, Wenzhe Gao1✉, Xiao Yu1✉

1. Department of Hepatopancreatobiliary Surgery, The Third Xiangya Hospital, Central South University, Changsha, Hunan, P.R. China.
2. Department of Nephrology, Institute of Nephrology, 2nd Affiliated Hospital of Hainan Medical University, Haikou, Hainan, P.R. China.
3. Department of General Surgery, Shaoyang University Affiliated Second Hospital, Shaoyang University, Shaoyang, Hunan, P.R. China.
4. Department of General Surgery, Affiliated Renhe Hospital of China Three Gorges University, Yichang, Hubei, P.R. China.
*These authors contributed equally to this work.

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Citation:
Chen D, Liu J, Zang L, Xiao T, Zhang X, Li Z, Zhu H, Gao W, Yu X. Integrated Machine Learning and Bioinformatic Analyses Constructed a Novel Stemness-Related Classifier to Predict Prognosis and Immunotherapy Responses for Hepatocellular Carcinoma Patients. Int J Biol Sci 2022; 18(1):360-373. doi:10.7150/ijbs.66913. Available from https://www.ijbs.com/v18p0360.htm

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Abstract

Graphic abstract

Immunotherapy has made great progress in hepatocellular carcinoma (HCC), yet there is still a lack of biomarkers for predicting response to it. Cancer stem cells (CSCs) are the primary cause of the tumorigenesis, metastasis, and multi-drug resistance of HCC. This study aimed to propose a novel CSCs-related cluster of HCC to predict patients' response to immunotherapy. Based on RNA-seq datasets from The Cancer Genome Atlas (TCGA) and Progenitor Cell Biology Consortium (PCBC), one-class logistic regression (OCLR) algorithm was applied to compute the stemness index (mRNAsi) of HCC patients. Unsupervised consensus clustering was performed to categorize HCC patients into two stemness subtypes which further proved to be a predictor of tumor immune microenvironment (TIME) status, immunogenomic expressions and sensitivity to neoadjuvant therapies. Finally, four machine learning algorithms (LASSO, RF, SVM-RFE and XGboost) were applied to distinguish different stemness subtypes. Thus, a five-hub-gene based classifier was constructed in TCGA and ICGC HCC datasets to predict patients' stemness subtype in a more convenient and applicable way, and this novel stemness-based classification system could facilitate the prognostic prediction and guide clinical strategies of immunotherapy and targeted therapy in HCC.

Keywords: Cancer stem cell, Immunotherapy, Machine learning, Hepatocellular carcinoma