A Five-Gene Signature Associated With DNA Damage Repair Molecular Subtype Predict Overall Survival for Hepatocellular Carcinoma

Identification of two heterogeneous subtypes of hepatocellular carcinoma with distinct pathway activities and clinical outcomes based on gene set variation analysis

Background

High heterogeneity is an essential feature of malignant tumors. This study aims to reveal the drivers of hepatocellular carcinoma heterogeneity for prognostic stratification and to guide individualized treatment.

Methods

Omics data and clinical data for two HCC cohorts were derived from the Cancer Genome Atlas (TCGA) and the International Cancer Genome Atlas (ICGC), respectively. CNV data and methylation data were downloaded from the GSCA database. GSVA was used to estimate the transcriptional activity of KEGG pathways, and consensus clustering was used to categorize the HCC samples. The pRRophetic package was used to predict the sensitivity of samples to anticancer drugs. TIMER, MCPcounter, quanTIseq, and TIDE algorithms were used to assess the components of TME. LASSO and COX analyses were used to establish a prognostic gene signature. The biological role played by genes in HCC cells was confirmed by in vitro experiments.

Results

We classified HCC tissues into two categories based on the activity of prognostic pathways. Among them, the transcriptional profile of cluster A HCC is similar to that of normal tissue, dominated by cancer-suppressive metabolic pathways, and has a better prognosis. In contrast, cluster B HCC is dominated by high proliferative activity and has significant genetic heterogeneity. Meanwhile, cluster B HCC is often poorly differentiated, has a high rate of serum AFP positivity, is prone to microvascular invasion, and has shorter overall survival. In addition, we found that mutations, copy number variations, and aberrant methylation were also crucial drivers of the differences in heterogeneity between the two HCC subtypes. Meanwhile, the TME of the two HCC subtypes is also significantly different, which offers the possibility of precision immunotherapy for HCC patients. Finally, based on the prognostic value of molecular subtypes, we developed a gene signature that could accurately predict patients' OS. The riskscore quantified by the signature could evaluate the heterogeneity of HCC and guide clinical treatment. Finally, we confirmed through in vitro experiments that RFPL4B could promote the progression of Huh7 cells.

Conclusion

The molecular subtypes we identified effectively exposed the heterogeneity of HCC, which is important for discovering new effective therapeutic targets.

Emerging role of microRNAs as regulators of protein kinase C substrate MARCKS and MARCKSL1 in cancer

MicroRNAs (miRNAs) have emerged as pivotal regulators of gene expression, playing essential roles in diverse cellular processes, including the development and progression of cancer. Among the numerous proteins influenced by miRNAs, the MARCKS/MARCKSL1 protein, a key regulator of cellular cytoskeletal dynamics and membrane-cytosol communication, has garnered significant attention due to its multifaceted involvement in various cancer-related processes, including cell migration, invasion, metastasis, and drug resistance. Motivated by the encouraging early clinical success of peptides targeting MARCKS in several pathological conditions, this review article delves into the intricate interplay between miRNAs and the MARCKS protein in cancer. Herein, we have highlighted the latest findings on specific miRNAs that modulate MARCKS/MARCKSL1 expression, providing a comprehensive overview of their roles in different cancer types. We have underscored the need for in-depth investigations into the therapeutic feasibility of targeting the miRNA-MARCKS axis in cancer, taking cues from the successes witnessed in related fields. Unlocking the full potential of miRNA-mediated MARCKS regulation could pave the way for innovative and effective therapeutic interventions against various cancer types.

DNA damage repair-related gene signature for identifying the immune status and predicting the prognosis of hepatocellular carcinoma

The heterogeneity of hepatocellular carcinoma (HCC) poses a challenge for accurate prognosis prediction. DNA damage repair genes (DDRGs) have an impact on a wide range of malignancies. However, the relevance of these genes in HCC prognosis has received little attention. In this study, we aimed to develop a prognostic signature to identify novel therapy options for HCC. We acquired mRNA expression profiles and clinical data for HCC patients from The Cancer Genome Atlas (TCGA) database. A polygenic prognostic model for HCC was constructed using selection operator Cox analysis and least absolute shrinkage. The model was validated using International Cancer Genome Consortium (ICGC) data. Overall survival (OS) between the high-risk and low-risk groups was compared using Kaplan‒Meier analysis. Independent predictors of OS were identified through both univariate and multivariate Cox analyses. To determine immune cell infiltration scores and activity in immune-related pathways, a single-sample gene set enrichment analysis was performed. The protein and mRNA expression levels of the prognostic genes between HCC and normal liver tissues were also examined by immunohistochemistry (IHC), immunofluorescence (IF) and quantitative real-time PCR (qRT-PCR). A novel ten-gene signature (CHD1L, HDAC1, KPNA2, MUTYH, PPP2R5B, NEIL3, POLR2L, RAD54B, RUVBL1 and SPP1) was established for HCC prognosis prediction. Patients in the high-risk group had worse OS than those in the low-risk group. Receiver operating characteristic curve analysis confirmed the predictive ability of this prognostic gene signature. Multivariate Cox analysis showed that the risk score was an independent predictor of OS. Functional analysis revealed a strong association with cell cycle and antigen binding pathways, and the risk score was highly correlated with tumor grade, tumor stage, and types of immune infiltrate. High expression levels of the prognostic genes were significantly correlated with increased sensitivity of cancer cells to antitumor drugs. IHC, IF and qRT-PCR all indicated that the prognostic genes were highly expressed in HCC relative to normal liver tissue, consistent with the results of bioinformatics analysis. Ten DDRGs were utilized to create a new signature for identifying the immunological state of HCC and predicting prognosis. In addition, blocking these genes could represent a promising treatment.

SPP1 as a risk factor for patients with acute on chronic liver failure undergoing liver transplantation

BACKGROUND

Acute on chronic liver failure (ACLF) is characterized by systemic inflammation and significant mortality, calling for accurate assessment due to the diverse prognosis of liver transplantation (LT).

METHODS

8 patients with ACLF and 4 normal controls (NC) underwent peripheral blood mononuclear cells (PBMCs) transcriptomics, whereas 9 patients with ACLF and 3 NC had hepatic CD45⁺ T cells transcriptomics. Thecandidateindicatorfoundinthetranscriptomicswas confirmedbya retrospective cohort (n = 137) and one prospective cohort (n = 68).

RESULTS

Transcriptomics revealed significant differentially expression genes (DEGs) and bioprocesses related to the PBMCs and hepatic CD45⁺ T cells. Secreted phosphoprotein 1 (SPP1) was identified as a potential indicator for ACLF patients receiving LT, which was supported by evidence from the cross-sectional cohorts. As the condition of ACLF got worse, so did SPP1 levels, which were associated with liver failure and coagulation failure. SPP1 levels prior to LT were considerably greater in non-survivors of ACLF within 90 days than that in survivors. In the derivation cohort and validation cohort, ACLF patients with elevated SPP1 levels had significantly shorter cumulative survival durations than those with low SPP1 levels, P = 0.02 and P < 0.001, respectively. The SPP1-MELD and SPP1-chronic liver failure consortium (CLIF-C) ACLF scores had comparatively larger areas under the receiver operating characteristic curves (AUCs) than MELD (P = 0.0388) and CLIF-C ACLF (P = 0.045).

CONCLUSIONS

The circulating SPP1 showed promise as a predictor for ACLF patients receiving LT, which demonstrated the need for tracking the clinical outcome of LT.

Identification of Cuproptosis-Related Subtypes, Establishment of a Prognostic Signature and Characterization of the Tumor Microenvironment in Gastric Cancer

Purpose

Cuproptosis is a newly identified form of programmed cell death. We aimed to comprehensively discuss the correlation of cuproptosis with gastric cancer (GC) using bioinformatic methods.

Patients and Methods

This study selected GC bulk and single-cell RNA sequencing profiles from public databases. Based on the enrichment pattern of cuproptosis-related gene sets (CRGSs), GC patients were classified into different cuproptosis subtypes. A series of systematic analyses was performed to investigate the correlation of cuproptosis subtype with biological function and immune cell infiltration. In addition, we established a CRGS risk score signature to quantify GC patients' risk level, and analyzed the signature's relationship with clinical features, tumor microenvironment (TME) and treatment responses. Genes used for the construction of the risk score model were also detected in GC tumor and normal tissues by real-time quantitative polymerase chain reaction (RT-qPCR).

Results

First, analysis of scRNA-seq data revealed the alterations in CRGS enrichment scores for patients with GC and precancerous diseases. Then, based on large GC patient cohorts, two cuproptosis subtypes with significant differences in survival, biological function and TME were identified. Furthermore, we established a CRGS risk score signature. High-risk patients on the CRGS risk score signature with worse overall survival were characterized by higher immune and stromal contents in the TME, more advanced clinicopathological features, and better sensitivity to a wider range of anti-tumor drugs. Low-risk patients were correlated with higher tumor purity, and demonstrated more favorable clinical outcomes and higher sensitivity to immunotherapy.

Conclusion

The current work elucidated that cuproptosis plays an important role in the regulation of TME landscapes in GC. Two cuproptosis subtypes with distinct TME characteristics were identified. In addition, the establishment of a CRGS risk score signature could provide novel insights into accurate prediction and personalized treatment for GC patients.

Identification and verification of the prognostic value of CUL7 in colon adenocarcinoma

CUL7, a gene composed of 26 exons associated with cullin 7 protein, is also an E3 ligase that is closely related to cell senescence, apoptosis, and cell transformation and also plays an important role in human cancer. However, there is no systematic pan-cancer analysis has been performed to explore its role in prognosis and immune prediction. In this study, the expression of CUL7 in colon adenocarcinoma (COAD) was investigated to determine its prognosis value. First, based on the Cancer Genome Atlas (TCGA), Genotypic-Tissue Expression Project(GTEx), Cancer Cell Line Encyclopedias(CCLE), and TISIDB database, the potential role of CUL7 in different tumors was explored. Subsequently, the expression of CUL7 in COAD was explored and verified by Immunohistochemistry (IHC). Furthermore, the mutation frequency of CUL7 in COAD was analyzed, and the prognostic value of CUL7 in COAD was discussed. In addition, the nomogram was constructed, and its prognostic value was verified by follow-up data from Jiangmen Central Hospital. Finally, PPI network analysis explored the potential biological function of CUL7 in COAD. The results show that CUL7 is upregulated in most tumors, which is significantly associated with poor survival. At the same time, CUL7 is correlated with the clinical stage and immune landscape of various tumors. In colorectal cancer, CUL7 was overexpressed in tumor tissues by IHC with a mutation frequency of about 4%. CUL7 is an independent prognostic factor for colorectal cancer. The nomogram constructed has effective predictive performance, and external databases proved the prognostic value of CUL7. In addition, PPI network analysis showed that CUL7 was closely related to FBXW8, and further pathway enrichment analysis showed that CUL7 was mainly involved in ubiquitin-mediated proteolysis. Therefore, our study provides a comprehensive understanding of the potential role of CUL7 in different tumors, and CUL7 might be a prognostic marker for COAD.