Identification of cancer stem cell characteristics in liver hepatocellular carcinoma by WGCNA analysis of transcriptome stemness index.
Cancer stem cells (CSCs) are characterized by self-renewal and -differential potential as compared to common cancer cells and play an important role in the development and therapeutic resistance of liver hepatocellular carcinoma (LIHC). However, the specific pathogenesis of LIHC stem cells is still unclear, and the genes involved in the stemness of LIHC stem cells are currently unknown. In this study, we investigated novel biomarkers associated with LIHC and explored the expression characteristics of stem cell-related genes in LIHC. We found that mRNA expression-based stemness index (mRNAsi) was significantly overexpressed in liver cancer tissues. Further, mRNAsi expression in LIHC increased with the tumor pathological grade, with grade 4 tumors harboring the greatest stem cell features. Upon establishing mRNAsi scores based on mRNA expression of every gene, we found an association with poor overall survival in LIHC. Moreover, modules of interest were determined based on weighted gene co-expression network analysis (WGCNA) inclusion criteria, and three significant modules (red, green, and brown) and 21 key genes (DCN, ECM1, HAND2, PTGIS, SFRP1, SRPX, COLEC10, GRP182, ADAMTS7, CD200, CDH11, COL8A1, FAP, LZTS1, MAP1B, NAV1, NOTCH3, OLFML2A, PRR16, TMEM119, and VCAN) were identified. Functional analysis of these 21 genes demonstrated their enrichment in pathways involved in angiogenesis, negative regulation of DNA-binding transcription factor activity, apoptosis, and autophagy. Causal relationship with proteins indicated that the Wnt, Notch, and Hypoxia pathways are closely related to LIHC tumorigenesis. To our knowledge, this is the first report of a novel CSC biomarker, mRNAsi, to predict the prognosis of LIHC. Further, we identified 21 key genes through mRNA expression network analysis, which could be potential therapeutic targets to inhibit the stemness of cancer cells in LIHC.
Bai KH
，He SY
，Shu LL
，Wang WD
，Lin SY
，Zhang QY
，Li L
，Cheng L
，Dai YJ
... -
《Cancer Medicine》
Predictive value of a stemness-based classifier for prognosis and immunotherapy response of hepatocellular carcinoma based on bioinformatics and machine-learning strategies.
Significant advancements have been made in hepatocellular carcinoma (HCC) therapeutics, such as immunotherapy for treating patients with HCC. However, there is a lack of reliable biomarkers for predicting the response of patients to therapy, which continues to be challenging. Cancer stem cells (CSCs) are involved in the oncogenesis, drug resistance, and invasion, as well as metastasis of HCC cells. Therefore, in this study, we aimed to create an mRNA expression-based stemness index (mRNAsi) model to predict the response of patients with HCC to immunotherapy.
We retrieved gene expression and clinical data of patients with HCC from the GSE14520 dataset and the Cancer Genome Atlas (TCGA) database. Next, we used the "one-class logistic regression (OCLR)" algorithm to obtain the mRNAsi of patients with HCC. We performed "unsupervised consensus clustering" to classify patients with HCC based on the mRNAsi scores and stemness subtypes. The relationships between the mRNAsi model, clinicopathological features, and genetic profiles of patients were compared using various bioinformatic methods. We screened for differentially expressed genes to establish a stemness-based classifier for predicting the patient's prognosis. Next, we determined the effect of risk scores on the tumor immune microenvironment (TIME) and the response of patients to immune checkpoint blockade (ICB). Finally, we used qRT-PCR to investigate gene expression in patients with HCC.
We screened CSC-related genes using various bioinformatics tools in patients from the TCGA-LIHC cohort. We constructed a stemness classifier based on a nine-gene (PPARGC1A, FTCD, CFHR3, MAGEA6, CXCL8, CABYR, EPO, HMMR, and UCK2) signature for predicting the patient's prognosis and response to ICBs. Further, the model was validated in an independent GSE14520 dataset and performed well. Our model could predict the status of TIME, immunogenomic expressions, congenic pathway, and response to chemotherapy drugs. Furthermore, a significant increase in the proportion of infiltrating macrophages, Treg cells, and immune checkpoints was observed in patients in the high-risk group. In addition, tumor cells in patients with high mRNAsi scores could escape immune surveillance. Finally, we observed that the constructed model had a good expression in the clinical samples. The HCC tumor size and UCK2 genes expression were significantly alleviated and decreased, respectively, by treatments of anti-PD1 antibody. We also found knockdown UCK2 changed expressions of immune genes in HCC cell lines.
The novel stemness-related model could predict the prognosis of patients and aid in creating personalized immuno- and targeted therapy for patients in HCC.
Chen E
，Zou Z
，Wang R
，Liu J
，Peng Z
，Gan Z
，Lin Z
，Liu J
... -
《Frontiers in Immunology》
ResearchGate
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