Integrative analysis of the characteristics of lipid metabolism-related genes as prognostic prediction markers for hepatocellular carcinoma

Diagnostic and prognostic role of LINC01767 in hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is a primary contributor to cancer-related mortality on a global scale. However, the underlying molecular mechanisms are still poorly understood. Long noncoding RNAs are emerging markers for HCC diagnosis, prognosis, and therapeutic target. No study of LINC01767 in HCC was published.

AIM

To conduct a multi-omics analysis to explore the roles of LINC01767 in HCC for the first time.

METHODS

DESeq2 Package was used to analyze different gene expressions. Receiver operating characteristic curves assessed the diagnostic performance. Kaplan-Meier univariate and Cox multivariate analyses were used to perform survival analysis. The least absolute shrinkage and selection operator (LASSO)-Cox was used to identify the prediction model. Subsequent to the validation of LINC01767 expression in HCC fresh frozen tissues through quantitative real time polymerase chain reaction, next generation sequencing was performed following LINC01767 over expression (GSE243371), and Gene Ontology/Kyoto Encyclopedia of Genes and Genomes/Gene Set Enrichment Analysis/ingenuity pathway analysis was carried out. In vitro experiment in Huh7 cell was carried out.

RESULTS

LINC01767 was down-regulated in HCC with a log fold change = 1.575 and was positively correlated with the cancer stemness. LINC01767 was a good diagnostic marker with area under the curve (AUC) [0.801, 95% confidence interval (CI): 0.751-0.852, P = 0.0106] and an independent predictor for overall survival (OS) with hazard ratio = 1.899 (95%CI: 1.01-3.58, P = 0.048). LINC01767 nomogram model showed a satisfied performance. The top-ranked regulatory network analysis of LINC01767 showed the regulation of genes participating various pathways. LASSO regression identified the 9-genes model showing a more satisfied performance than 5-genes model to predict the OS with AUC > 0.75. LINC01767 was down-expressed obviously in tumor than para-tumor tissues in our cohort as well as in cancer cell line; the over expression of LINC01767 inhibit cell proliferation and clone formation of Huh7 in vitro.

CONCLUSION

LINC01767 was an important tumor suppressor gene in HCC with good diagnostic and prognostic performance.

A Four Amino Acid Metabolism-Associated Genes (AMGs) Signature for Predicting Overall Survival Outcomes and Immunotherapeutic Efficacy in Hepatocellular Carcinoma

Metabolites are important indicators of cancer and mutations in genes involved in amino acid metabolism may influence tumorigenesis. Immunotherapy is an effective cancer treatment option; however, its relationship with amino acid metabolism has not been reported. In this study, RNA-seq data for 371 liver cancer patients were acquired from TCGA and used as the training set. Data for 231 liver cancer patients were obtained from ICGC and used as the validation set to establish a gene signature for predicting liver cancer overall survival outcomes and immunotherapeutic responses. Four reliable groups based on 132 amino acid metabolism-related DEGs were obtained by consistent clustering of 371 HCC patients and a four-gene signature for prediction of liver cancer survival outcomes was developed. Our data show that in different clinical groups, the overall survival outcomes in the high-risk group were markedly low relative to the low-risk group. Univariate and multivariate analyses revealed that the characteristics of the 4-gene signature were independent prognostic factors for liver cancer. The ROC curve revealed that the risk characteristic is an efficient predictor for 1-, 2-, and 3-year HCC survival outcomes. The GSVA and KEGG pathway analyses revealed that high-risk score tumors were associated with all aspects of the degree of malignancy in liver cancer. There were more mutant genes and greater immune infiltrations in the high-risk groups. Assessment of the three immunotherapeutic cohorts established that low-risk score patients significantly benefited from immunotherapy. Then, we established a prognostic nomogram based on the TCGA cohort. In conclusion, the 4-gene signature is a reliable diagnostic marker and predictor for immunotherapeutic efficacy.

Identification of a novel oxidative stress-related prognostic model in lung adenocarcinoma

Background: Oxidative stress (OxS) participates in a variety of biological processes, and is considered to be related to the occurrence and progression of many tumors; however, the potential diagnostic value of OxS in lung cancer remains unclear. Methods: The clinicopathological and transcriptome data for lung adenocarcinoma (LUAD) were collected from TCGA and GEO database. LASSO regression was used to construct a prognostic risk model. The prognostic significance of the OxS-related genes was explored using a Kaplan-Meier plotter database. The prediction performance of the risk model was shown in both the TCGA and GSE68465 cohorts. The qRT-PCR was performed to explore the expression of genes. CCK-8, Edu and transwell assays were conducted to analyze the role of CAT on cell proliferation migration and invasion in lung cancer. Immune infiltration was evaluated by CIBERSORT and mutational landscape was displayed in the TCGA database. Moreover, the relationship between risk score with drug sensitivity was investigated by pRRophetic. Results: We identified a prognosis related risk model based on a four OxS gene signature in LUAD, including CYP2D6, FM O 3, CAT, and GAPDH. The survival analysis and ROC curve indicated good predictive power of the model in both the TCGA and GEO cohorts. LUAD patients in the high-risk group had a shorter OS compared to the low-risk group. QRT-PCR result showed that the expression of four genes was consistent with previous analysis in cell lines. Moreover, overexpression of CAT could decrease the proliferation, invasion and migration of lung cancer cells. The Cox regression analysis showed that the risk score could be used as an independent prognostic factor for OS. LUAD patients in the high-risk score group exhibited a higher tumor mutation burden and risk score were closely related to tumor associated immune cell infiltration, as well as the expression of immune checkpoint molecules. Both the high- and low-risk groups have significant differences in sensitivity to some common chemotherapy drugs, such as Paclitaxel, Docetaxel, and Vinblastine, which may contribute to clinical treatment decisions. Conclusion: We established a robust OxS-related prognostic model, which may contribute to individualized immunotherapeutic strategies in LUAD.

SLC11A1 associated with tumor microenvironment is a potential biomarker of prognosis and immunotherapy efficacy for colorectal cancer

Colorectal cancer (CRC) is one of the most lethal cancers of the digestive system. The tumor microenvironment (TME) plays a central role in the initiation and development of CRC. However, little is known about the modulation mechanism of the TME in CRC. In our study, we attempted to identify a biomarker related to the TME modulation that could serve as a potential prognostic biomarker for CRC. We identified differentially expressed genes between the ImmuneScore high/low and StromalScore high/low groups. Using univariate COX regression analysis and hub gene analysis (cytoHubba), SLC11A1 was identified as the only candidate gene for subsequent analysis. CIBERSORT, EPIC, MCPcounter, and immunogenic cell death were performed to evaluate the effect of SLC11A1 on the TME. We also collected samples and performed Real-time quantitative PCR to verify the expression levels of SLC11A1 in CRC and adjacent normal tissues. The IMvigor210 cohort, TIDE score, and immunophenoscore (IPS) were used to analyze the association between SLC11A1 and immunotherapy efficacy. SLC11A1 was highly expressed in CRC tissues compared with its expression in normal colorectal tissues and was associated with poor prognosis and advanced clinicopathological stages. Gene set enrichment analysis showed that TGF-β pathways, JAK-STAT pathways, and angiogenesis were significantly enriched in the high-SLC11A1 group. Single-cell analysis validated the correlation between SLC11A1 and the TME. Using CIBERSORT, EPIC, and MCPcounter algorithms, we found that there was more macrophage and fibroblast infiltration in the SLC11A1 high-expression group. Meanwhile, high-SLC11A1 patients had lower IPS scores, higher TIDE scores, and fewer immunotherapy benefits than those of low-SLC11A1 patients. In conclusion, SLC11A1 plays a crucial role in the TME and could serve as a potential biomarker for poor prognosis and immunotherapy efficacy in CRC.

Identification and Validation of a Nine-Gene Amino Acid Metabolism-Related Risk Signature in HCC

Background: Hepatocellular carcinoma (HCC) is the world’s second most deadly cancer, and metabolic reprogramming is its distinguishing feature. Among metabolite profiling, variation in amino acid metabolism supports tumor proliferation and metastasis to the most extent, yet a systematic study on the role of amino acid metabolism-related genes in HCC is still lacking. An effective amino acid metabolism-related prediction signature is urgently needed to assess the prognosis of HCC patients for individualized treatment.

Materials and Methods: RNA-seq data of HCC from the TCGA-LIHC and GSE14520 (GPL3921) datasets were defined as the training set and validation set, respectively. Amino acid metabolic genes were extracted from the Molecular Signature Database. Univariate Cox and LASSO regression analyses were performed to build a predictive risk signature. K-M curves, ROC curves, and univariate and multivariate Cox regression were conducted to evaluate the predictive value of this risk signature. Functional enrichment was analyzed by GSEA and CIBERSORTx software.

Results: A nine-gene amino acid metabolism-related risk signature including B3GAT3, B4GALT2, CYB5R3, GNPDA1, GOT2, HEXB, HMGCS2, PLOD2, and SEPHS1 was constructed to predict the overall survival (OS) of HCC patients. Patients were separated into high-risk and low-risk groups based on risk scores and low-risk patients had lower risk scores and longer survival time. Univariate and multivariate Cox regression verified that this signature was an independent risk factor for HCC. ROC curves showed that this risk signature can effectively predict the 1-, 2-, 3- and 5-year survival times of patients with HCC. Additionally, prognostic nomograms were established based on the training set and validation set. These genes were closely correlated with the immune regulation.

Conclusion: Our study identified a nine-gene amino acid metabolism-related risk signature and built predictive nomograms for OS in HCC. These findings will help us to personalize the treatment of liver cancer patients.

Estrogen Receptor-α Suppresses Liver Carcinogenesis and Establishes Sex-Specific Gene Expression

Estrogen protects females from hepatocellular carcinoma (HCC). To determine whether this protection is mediated by classic estrogen receptors, we tested HCC susceptibility in estrogen receptor-deficient mice. In contrast to a previous study, we found that diethylnitrosamine induces hepatocarcinogenesis to a significantly greater extent when females lack Esr1, which encodes Estrogen Receptor-α. Relative to wild-type littermates, Esr1 knockout females developed 9-fold more tumors. Deficiency of Esr2, which encodes Estrogen Receptor-β, did not affect liver carcinogenesis in females. Using microarrays and QPCR to examine estrogen receptor effects on hepatic gene expression patterns, we found that germline Esr1 deficiency resulted in the masculinization of gene expression in the female liver. Six of the most dysregulated genes have previously been implicated in HCC. In contrast, Esr1 deletion specifically in hepatocytes of Esr1 conditional null female mice (in which Cre was expressed from the albumin promoter) resulted in the maintenance of female-specific liver gene expression. Wild-type adult females lacking ovarian estrogen due to ovariectomy, which is known to make females susceptible to HCC, also maintained female-specific expression in the liver of females. These studies indicate that Esr1 mediates liver cancer risk, and its control of sex-specific liver gene expression involves cells other than hepatocytes.