A Novel Signature Based on mTORC1 Pathway in Hepatocellular Carcinoma

Copper metabolism-related risk score identifies hepatocellular carcinoma subtypes and SLC27A5 as a potential regulator of cuproptosis

AIMS

Dysregulated copper metabolism has been noticed in many types of cancer including hepatocellular carcinoma (HCC); however, a comprehensive understanding about this dysregulation still remains unclear in HCC.

METHODS

A set of bioinformatic tools was integrated to analyze the expression and prognostic significance of copper metabolism-related genes. A related risk score, termed as CMscore, was developed via univariate Cox regression, least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression. Pathway enrichment analyses and tumor immune cell infiltration were further investigated in CMscore stratified HCC patients. Weighted correlation network analysis (WGCNA) was used to identify potential regulator of cuproptosis.

RESULTS

Copper metabolism was dysregulated in HCC. HCC patients in the high-CMscore group showed a significantly lower overall survival (OS) and enriched in most cancer-related pathways. Besides, HCC patients with high CMscore had higher expression of pro-tumor immune infiltrates and immune checkpoints. Moreover, cancer patients with high CMscore from two large cohorts exhibited significantly prolonged survival time after immunotherapy. WGCNA and subsequently correlation analysis revealed that SLC27A5 might be a potential regulator of cuproptosis in HCC. In vitro experiments revealed that SLC27A5 inhibited cell proliferation and migration of HCC cells and could upregulate FDX1, the key regulator of cuproptosis.

SIGNIFICANCE

The CMscore is helpful in clustering HCC patients with distinct prognosis, gene mutation signatures, and sensitivity to immunotherapy. SLC27A5 might serve as a potential target in the induction of cuproptosis in HCC.

Prognostic value of lactate transporter SLC16A1 and SLC16A3 as oncoimmunological biomarkers associating tumor metabolism and immune evasion in glioma

BACKGROUND

Hypoxic microenvironment is immunosuppressive and protumorigenic, and elevated lactate is an intermediary in the modulation of immune responses. However, as critical lactate transporters, the role of SLC16A1 and SLC16A3 in immune infiltration and evasion of glioma is not fully elucidated.

METHODS

Gene expression in low- and high-grade glioma (LGG and GBM) was evaluated with TCGA database. The TISIDB, TIMER and CIBERSORT databases were utilized for the analysis of the correlation between SLC16A1 or SLC16A3 and immunocyte infiltration as well as immune checkpoints.

RESULTS

Compared with normal tissues, a significant increase of both SLC16A1 and SLC16A3 was found in LGG and GBM, and closely related to the poor prognosis only in LGG. Cancer SEA indicated that SLC16A1 was involved in hypoxia while SLC16A3 contributed to metastasis and inflammation in glioma. The SLC16A3 expression was significantly correlated with neutrophil activation by GO analysis. TISCH showed the distribution of SLC16A1 on glioma cells and SLC16A3 on immune cells, which was correlated to tumor-associated macrophages and neutrophils that are immunosuppressive. SLC16A1 and SLC16A3 were identified to tightly interacted with diverse immune checkpoints (especially PD1, PD-L1, PD-L2, Tim-3) and immunosuppressive factors (TGF-β and IL-10) in glioma. Furthermore, SLC16A3 had a positive correlation to activation markers of tumor-associated neutrophils and chemokines such as CCL2, CCL22, CXCR2, CXCR4 in LGG and CCL7, CCL20 CXCL8 in GBM, which could enhance infiltration of immunosuppressive cells to the tumor microenvironment.

CONCLUSION

In general, our results suggest that SLC16A1 and SLC16A3 act as a bridge between tumor metabolism and immunity by promoting immunosuppressive cell infiltration, which contributes to immune evasion and a worse prognosis in glioma. Targeting SLC16A1 and SLC16A3 may provide novel therapeutic strategy for immunotherapy in glioma.

Construction of a prognostic risk model based on apoptosis-related genes to assess tumor immune microenvironment and predict prognosis in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a serious malignant disease with high incidence, high mortality and poor prognosis. This study aimed to establish a novel signature based on apoptosis-related genes (ARGs) to predict the prognosis of HCC.

METHODS

Expression data of HCC from TCGA database and the list of 160 ARGs from MSigDB were downloaded. The genes included in apoptosis-related signature were selected by univariate Cox regression analysis and lasso Cox regression analysis. Subsequently, a prognostic risk model for scoring patients was developed, and then separates patients into two groups. Kaplan-Meier and receiver operating characteristic analysis were performed to evaluate the prognostic value of the model in TCGA, GEO and ICGC databases. The characteristics of immune cell infiltration between two groups of HCC were investigated. Finally, a nomogram was plotted to visualize the prognosis prediction.

RESULTS

Nine genes (CDC25B, DAP3, ETF1, GSR, LGALS3, MGMT, PPP2R5B, SQSTM1 and VDAC2) were included in the prognostic risk model. Survival was lower in the high-risk group. Surprisingly, the high-risk group was significantly more in immune cell infiltration and with higher immunoscore and stromalscore than in the low-risk group. In addition, the risk score was an independent prognostic factor for HCC.

CONCLUSIONS

Prognostic signature comprising nine ARGs could be used as a potential prognostic factor for HCC. It also provides an important idea for further understanding the immunotherapy of HCC.

Establishment and Validation of an MTORC1 Signaling-Related Gene Signature to Predict Overall Survival in Patients with Hepatocellular Carcinoma

Background

Accurate and effective biomarkers for the prognosis of patients with hepatocellular carcinoma (HCC) are poorly identified. A network-based gene signature may serve as a valuable biomarker to improve the accuracy of risk discrimination in patients.

Methods

The expression levels of cancer hallmarks were determined by Cox regression analysis. Various bioinformatic methods, such as GSEA, WGCNA, and LASSO, and statistical approaches were applied to generate an MTORC1 signaling-related gene signature (MSRS). Moreover, a decision tree and nomogram were constructed to aid in the quantification of risk levels for each HCC patient.

Results

Active MTORC1 signaling was found to be the most vital predictor of overall survival in HCC patients in the training cohort. MSRS was established and proved to hold the capacity to stratify HCC patients with poor outcomes in two validated datasets. Analysis of the patient MSRS levels and patient survival data suggested that the MSRS can be a valuable risk factor in two validated datasets and the integrated cohort. Finally, we constructed a decision tree which allowed to distinguish subclasses of patients at high risk and a nomogram which could accurately predict the survival of individuals.

Conclusions

The present study may contribute to the improvement of current prognostic systems for patients with HCC.