Hallmark-guided subtypes of hepatocellular carcinoma for the identification of immune-related gene classifiers in the prediction of prognosis, treatment efficacy, and drug candidates

Abstract

Hepatocellular carcinoma (HCC), accounting for ~90% of all primary liver cancer, is a prevalent malignancy worldwide. The intratumor heterogeneity of its causative etiology, histology, molecular landscape, and immune phenotype makes it difficult to precisely recognize individuals with high mortality risk or tumor-intrinsic treatment resistance, especially immunotherapy. Herein, we comprehensively evaluated the activities of cancer hallmark gene sets and their correlations with the prognosis of HCC patients using gene set variation analysis (GSVA) and identified two HCC subtypes with distinct prognostic outcomes. Based on these subtypes, seven immune-related genes (TMPRSS6, SPP1, S100A9, EPO, BIRC5, PLXNA1, and CDK4) were used to construct a novel prognostic gene signature [hallmark-guided subtypes-based immunologic signature (HGSIS)] via multiple statistical approaches. The HGSIS-integrated nomogram suggested an enhanced predictive performance. Interestingly, oncogenic hallmark pathways were significantly enriched in the high-risk group and positively associated with the risk score. Distinct mutational landscapes and immune profiles were observed between different risk groups. Moreover, immunophenoscore (IPS) and tumor immune dysfunction and exclusion (TIDE) analysis showed different sensitivities of HGSIS risk groups for immune therapy efficacy, and the pRRophetic algorithm indicated distinguishable responses for targeted/chemotherapies in different groups. KIF2C was picked out as the key target concerning HGSIS, and the top 10 small molecules were predicted to bind to the active site of KIF2C via molecular docking, which might be further used for candidate drug discovery of HCC. Taken together, our study offers novel insights for clinically significant subtype recognition, and the proposed signature may be a helpful guide for clinicians to improve the treatment regimens.

Identification of an immune-related 6-lncRNA panel with a good performance for prognostic prediction in hepatocellular carcinoma by integrated bioinformatics analysis

“…This is also why studying immune mechanisms in HCC and developing related diagnostic models have received increasing attention. [36,37] By checking the immune characteristics of HCC patients, that is the molecular and cellular characterization of TIME, individualized risk stratification of HCC patients can be achieved, and patients with significant therapeutic effects on immunotherapy can be identified, thereby guiding clinical decision-making and realizing the purpose of precise treatment. [38][39][40] In the present study, our 6-lncRNA panel demonstrated excellent stratification ability for immune infiltrating cells such as natural regulatory T cells, B cells, follicular helper T cells, mucosal-associated invariant T, macrophages and NK cells from HCC patients.…”

Characterization of cellular senescence patterns predicts the prognosis and therapeutic response of hepatocellular carcinoma

“…The oneclass logistic regression (OCLR)-based mRNAsi and mDNAsi for evaluating the differentiation degree from bulk sequencing were obtained as previously described (Malta et al, 2018). For survival-related analysis, 336 of 370 patients with the survival time of >1 month were included (Cai et al, 2020;Guo et al, 2022), which were divided into the training and validation datasets with the ratio of 2:1 (222 vs. 114). The transcriptome profiles and survival data of 238 HCC patients from the International Cancer Genome Consortium (ICGC) database (ICGC-LIRI-JP) were acquired to serve as the external validation set.…”

“…The transcriptome profiles and survival data of 238 HCC patients from the International Cancer Genome Consortium (ICGC) database (ICGC-LIRI-JP) were acquired to serve as the external validation set. All data were normalized and processed as previously reported (Guo et al, 2022;Tang et al, 2022). 1,259 cellular senescence-related genes (CSGs) including 525 positive CSGs and 734 negative CSGs were retrieved from a recently published paper (Wang et al, 2022).…”

“…Subsequently, Kaplan-Meier survival analysis and log-rank test were used to identify the prognostic DECSGs for OS. To enhance the stability, we adopted the "multi-split" approach as we reported with some modifications (Tang et al, 2020;Guo et al, 2022;Zhang et al, 2022). Next, the prognostic DECSGs were utilized to perform unsupervised clustering for the patients with complete survival information, and the R package "ConsensusClusterPlus" was used to explore novel CSG patterns of HCC (Wilkerson and Hayes, 2010;Wang et al, 2021;Guo et al, 2022).…”

A novel gene-based model for prognosis prediction of head and neck squamous cell carcinoma

“…Accurate prognostic prediction plays a crucial role in guiding decision-making for individual cases [4]. However, traditional prognostic indicators such as tumor stages or grades exhibit limited precision in prognostication [5].…”