Identification of a two-gene prognostic model associated with cytolytic activity for colon cancer.

BACKGROUND: Increasing evidence has shown that cytolytic activity (CYT) is a new immunotherapy biomarker that characterises the antitumour immune activity of cytotoxic T cells and macrophages. In this study, we established a prognostic model associated with CYT. METHODS: A prognostic model based on CYT-related genes was developed. Furthermore, aberrant expression of genes of the model in colon cancer (CC) was identified by reverse transcription-quantitative polymerase chain reaction (RT-qPCR) and immunohistochemistry (IHC) assays. Next, the correlation between the model and T-cell infiltration in the CC microenvironment was analysed. The Tumour Immune Dysfunction and Exclusion (TIDE) algorithm and subclass mapping were used to predict clinical responses to immune checkpoint inhibitors. RESULTS: In total, 280 of the 1418 genes were differentially expressed based on CYT. A prognostic model (including HOXC8 and MS4A2) was developed based on CYT-related genes. The model was validated using the testing set, the whole set and a Gene Expression Omnibus (GEO) cohort (GSE41258). Gene set enrichment analysis (GSEA) and other analyses showed that the levels of immune infiltration and antitumour immune activation in low-risk-score tumours were greater than those in high-risk-score tumours. CC patients with a low-risk-score showed more promise in the response to anti-immune checkpoint therapy. CONCLUSIONS: Overall, our model may precisely predict the overall survival of CC and reflect the strength of antitumour immune activity in the CC microenvironment. Furthermore, the model may be a predictive factor for the response to immunotherapy.

Typing and modeling of hepatocellular carcinoma based on disulfidptosis-related amino acid metabolism genes for predicting prognosis and guiding individualized treatment.

Introduction: Hepatocellular carcinoma (HCC) is the most common type of cancer worldwide and is a major public health problem in the 21st century. Disulfidopathy, a novel cystine-associated programmed cell death, plays complex roles in various tumors. However, the relationship between disulfidoptosis and prognosis in patients with HCC remains unclear. This study aimed to explore the relationship between disulfideptosis and the prognosis of liver cancer and to develop a prognostic model based on amino acid metabolism and disulfideptosis genes. Methods: We downloaded the clinicopathological information and gene expression data of patients with HCC from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and classified them into different molecular subtypes based on the expression patterns of disulfidoptosis-associated amino acid metabolism genes (DRAGs). Patients were then classified into different gene subtypes using the differential genes between the molecular subtypes, and the predictive value of staging was assessed using survival and clinicopathological analyses. Subsequently, risk prognosis models were constructed based on Cox regression analysis to assess patient prognosis, receiver operating characteristic (ROC) curves, somatic mutations, microsatellite instability, tumor microenvironment, and sensitivity to antitumor therapeutic agents. Results: Patients were classified into two subtypes based on differential DRAGs gene expression, with cluster B having a better survival outcome than cluster A. Three gene subtypes were identified based on the differential genes between the two DRAGs molecular subtypes. The patients in cluster B had the best prognosis, whereas those in cluster C had the worst prognosis. The heat map showed better consistency in the patient subtypes obtained using both typing methods. We screened six valuable genes and constructed a prognostic signature. By scoring, we found that patients in the low-risk group had a better prognosis, higher immune scores, and more abundant immune-related pathways compared to the high-risk group, which was consistent with the tumor subtype results. Discussion: In conclusion, we developed a prognostic signature of disulfidptosis-related amino acid metabolism genes to assist clinicians in predicting the survival of patients with HCC and provide a reference value for targeted therapy and immunotherapy for HCC.

Based on disulfidptosis-related glycolytic genes to construct a signature for predicting prognosis and immune infiltration analysis of hepatocellular carcinoma.

Background: Hepatocellular carcinoma (HCC) comprises several distinct molecular subtypes with varying prognostic implications. However, a comprehensive analysis of a prognostic signature for HCC based on molecular subtypes related to disulfidptosis and glycolysis, as well as associated metabolomics and the immune microenvironment, is yet to be fully explored. Methods: Based on the differences in the expression of disulfide-related glycolytic genes (DRGGs), patients with HCC were divided into different subtypes by consensus clustering. Establish and verify a risk prognosis signature. Finally, the expression level of the key gene SLCO1B1 in the signature was evaluated using immunohistochemistry (IHC) and quantitative real-time PCR (qRT-PCR) in HCC. The association between this gene and immune cells was explored using multiplex immunofluorescence. The biological functions of the cell counting kit-8, wound healing, and colony formation assays were studied. Results: Different subtypes of patients have specific clinicopathological features, prognosis and immune microenvironment. We identified seven valuable genes and constructed a risk-prognosis signature. Analysis of the risk score revealed that compared to the high-risk group, the low-risk group had a better prognosis, higher immune scores, and more abundant immune-related pathways, consistent with the tumor subtypes. Furthermore, IHC and qRT-PCR analyses showed decreased expression of SLCO1B1 in HCC tissues. Functional experiments revealed that SLCO1B1 overexpression inhibited the proliferation, migration, and invasion of HCC cells. Conclusion: We developed a prognostic signature that can assist clinicians in predicting the overall survival of patients with HCC and provides a reference value for targeted therapy.

Immune infiltration and a ferroptosis-related gene signature for predicting the prognosis of patients with cholangiocarcinoma.

Cholangiocarcinoma (CHOL) is a digestive tract tumor with high malignancy and poor prognosis and is extremely challenging to treat. At present, induced cell death holds great promise in tumor therapy. Ferroptosis is a recently proposed pattern of programmed cell death, and numerous studies have shown that it is intimately involved in tumors. However, the roles of differentially expressed ferroptosis-related genes (DEFRGs) in CHOL have not been investigated. Our study was based on The Cancer Genome Atlas (TCGA) database, and DEFRGs were obtained to construct a prognostic riskScore model of CHOL by univariate and multivariate Cox regression analyses. Subsequently, the model was evaluated by nomogram construction, survival analysis, receiver operating characteristic (ROC) analysis, and exploration of the immune microenvironment. The mRNA and protein expression levels of each gene in the model were validated by the Gene Expression Omnibus (GEO) database, quantitative real-time PCR (qRT-PCR) and immunohistochemistry (IHC) staining. Our study found that the construction of a nomogram confirmed the predictive value of the model for overall survival (OS), and it was confirmed to have high diagnostic value by ROC analysis. Our experimental results were almost consistent with our bioinformatics results. In conclusion, we found that the prognostic model showed extremely high diagnostic and prognostic value and could predict the possibility of immunotherapy, thus providing a new direction for individualized treatment of patients with CHOL.

SLC17A2 Expression Correlates with Prognosis and Immune Infiltrates in Hepatocellular Carcinoma.

BACKGROUND: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors with a dismal prognosis, according to updated statistics. The solute carrier family 17 member 2 (SLC17A2) has not been studied in liver cancer. Therefore, we evaluated the role of SLC17A2 in HCC by bioinformatics analysis. OBJECTIVE: The objective of the study was to explore the value of SLC17A2 in the prognosis and diagnosis of hepatocellular carcinoma. METHODS: The expression level of SLC17A2 in HCC and the clinicopathological data were analyzed based on The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases and validated by immunohistochemical staining. In addition, the Kaplan-Meier plotter database and receiver operating characteristic (ROC) curve analysis were used to explore the prognostic and diagnostic significance. Some online databases were used to analyze the relationship between immune cell infiltration and analyze the relationship between immune cell infiltration and SLC17A2 in HCC. RESULTS: Multivariate Cox regression analysis showed that SLC17A2 expression was low in HCC (P < 0.05) and closely related to the clinical stage of HCC. In addition, SLC17A2 had a certain diagnostic value in HCC according to ROC curve analysis. Further biological analyses showed that SLC17A2 can regulate fatty acid metabolism, amino acid metabolism and cytochrome P450- related metabolism. Notably, we found that SLC17A2 expression was closely correlated with the infiltration of most immune cells in HCC. CONCLUSION: SLC17A2 expression is low in HCC and correlates with immune infiltration, so it may serve as an independent prognostic factor for HCC.

The prognostic and immunological effects of ZBTB7C across cancers: friend or foe?

As an important transcription factor, zinc-finger and BTB domain-containing 7B (ZBTB7C) plays an important role in a variety of tumors. However, its relationship with human immunity is unclear. This article aims to study its differential expression and survival across cancers and explore the relationships between its differential expression and the tumor microenvironment and immune cell infiltration. In this study, we used R software to process The Cancer Genome Atlas (TCGA) data and explored the expression pattern and prognostic value of ZBTB7C across cancers. Next, we comprehensively explained the important role of ZBTB7C in several tumor types in terms of tumor mutational burden (TMB), microsatellite instability (MSI) and immune cell infiltration. In general, the expression level of ZBTB7C in tumor tissues was lower than that in normal tissues. Highly expressed ZBTB7C was beneficial to the survival of patients with colon adenocarcinoma (COAD), lymphoid neoplasm diffuses large B cell lymphoma (DLBC), esophageal carcinoma (ESCA) and mesothelioma (MESO). Multivariate analysis showed that the expression of ZBTB7C was an independent prognostic factor in COAD and MESO. In COAD, the expression of ZBTB7C was positively correlated with both TMB and MSI. In colorectal cancer (CRC), there was a significant positive correlation between ZBTB7C expression and immune cell infiltration, especially the infiltration of mast cells and B cells. In conclusion, ZBTB7C can be used as a potential therapeutic target across cancers and is related to immune cell infiltration.

Transcriptome and Differential Methylation Integration Analysis Identified Important Differential Methylation Annotation Genes and Functional Epigenetic Modules Related to Vitiligo.

Vitiligo is an pigmentation disorder caused by a variety of pathogenic factors; its main pathophysiological conditions include oxidative stress, immune activation, and genetic background. Additionally, DNA methylation is often associated with the pathogenesis of vitiligo; however, the underlying mechanism remains unknown. In the present study, we used the Human Methylation 850K BeadChip platform to detect DNA methylation changes in the vitiligo melanocytes. We then integrated the results with the transcriptome data of vitiligo melanocytes and lesions to analyse the correlation between differentially methylated levels and differentially expressed genes. The results showed that there was a significant negative correlation between methylation levels and differentially expressed genes. Subsequently, we enriched GO and KEGG based on methylated differentially expressed genes (MDEGs) using R package ClusterProfiler, and the results were closely related to the pathogenesis of vitiligo. In addition, we also constructed a PPI network of MDEGs and excavated three important functional epigenetic modules, involving a total of 12 (BCL2L1, CDK1, ECT2, HELLS, HSP90AA1, KIF23, MC1R, MLANA, PBK, PTGS2, SOX10, and TYRP1) genes. These genes affect melanocyte melanogenesis, cellular oxidative stress and other important biological processes. Our comprehensive analysis results support the significant contribution of the status of DNA methylation modification to vitiligo, which will help us to better understand the molecular mechanism of vitiligo and explore new therapeutic strategies.

Zinc finger and BTB domain-containing 7C (ZBTB7C) expression as an independent prognostic factor for colorectal cancer and its relevant molecular mechanisms.

Currently, colorectal cancer (CRC) predictions are based on an early diagnosis and the tumor-node-metastasis (TNM) stage, but the outcomes of patients with the same cancer type are difficult to predict. Novel molecular tests for the early diagnosis and stratification of CRC patients must be devised. After our initial bioinformatics screen, we examined zinc finger and BTB domain-containing 7C (ZBTB7C). To date, few studies have investigated ZBTB7C in CRC, necessitating further analyses of its expression and regulatory mechanism in CRC. ZBTB7C mRNA and protein expression was detected in CRC and corresponding non-CRC tissues. We evaluated the relationship between clinical prognosis and ZBTB7C protein levels using Cox regression analysis and Kaplan-Meier curves. A receiver operating characteristic (ROC) curve was generated to verify the diagnostic performance of ZBTB7C levels in CRC. Several bioinformatics techniques were applied to analyze the potential molecular mechanism of ZBTB7C. Low mRNA and protein levels of ZBTB7C were detected in tumor tissues from CRC patients. The survival curve predicted a poor prognosis for CRC patients exhibiting low ZBTB7C expression (P=0.001). According to the univariate Cox regression analysis, older age, a high TNM stage and low ZBTB7C expression were responsible for poor outcomes in CRC patients. The multivariate analysis further revealed ZBTB7C as an independent prognostic factor for CRC (P=0.015). The area under the curve of ZBTB7C expression for CRC diagnosis was 0.970 (95% confidence interval, 0.9447-0.9946; P < 0.0001). According to in silico analyses, genes coexpressed with ZBTB7C are associated mainly with the Ras and Wnt signaling pathways. Overall, ZBTB7C is downregulated in CRC and represents an early diagnostic marker and independent prognostic factor for CRC. ZBTB7C may be functionally mediated by different pathways or targeting miRNAs.