Reduced GRAMD1C expression correlates to poor prognosis and immune infiltrates in kidney renal clear cell carcinoma

PAQR6 as a prognostic biomarker and potential therapeutic target in kidney renal clear cell carcinoma

Background

Progestin And AdipoQ Receptor Family Member VI (PAQR6) plays a significant role in the non-genomic effects of rapid steroid responses and is abnormally expressed in various tumors. However, its biological function in kidney renal clear cell carcinoma (KIRC) and its potential as a therapeutic target remain underexplored.

Methods

In this study, PAQR6 was identified as a critical oncogene by WGCNA algorithm and differential gene expression analysis using TCGA - KIRC and GSE15641 data. The differences in PAQR6 expression and its association with KIRC survival outcomes were investigated, and transcriptomic data were used to further elucidate PAQR6's biological functions. Moreover, XCELL and single - cell analysis assessed the correlation between PAQR6 expression and immune infiltration. TIDE algorithm was used to assess how well various patient cohorts responded to immune checkpoint therapy. Finally, the role of PAQR6 in the development of KIRC was verified through EdU, scratch assays, and Transwell assays.

Results

Our findings suggest that elevated expression of PAQR6 is linked to a poor prognosis for KIRC patients. Functional enrichment analysis demonstrated that PAQR6 is primarily involved in angiogenesis and pluripotent stem cell differentiation, which are crucial in mediating the development of KIRC. Additionally, we established a ceRNA network that is directly related to overall prognosis, further supporting the role of PAQR6 as a prognostic biomarker for KIRC.

Conclusion

Using both computational and experimental methods, this study leads the charge in discovering and verifying PAQR6 as a prognostic biomarker and possible therapeutic target for KIRC. In the future, to determine its molecular mechanism in KIRC carcinogenesis, more in vivo research will be carried out.

Examining genotype-phenotype associations of GRAM domain proteins using GWAS, PheWAS and literature review in cattle, human, pig, mouse and chicken

The GRAMD genes are involved in maintaining cholesterol homeostasis, apoptosis, cancer and production traits in livestock. A lipid-binding GRAM domain is implicated in lipid transport and metabolism. The functions of GRAMD proteins remain incompletely understood. The aim of the present study was therefore to investigate the associations between six GRAMD genes in cattle using data from the international genomic evaluation of the Interbull InterGenomics Centre and to evaluate genotype-phenotype associations in human, cattle, pig, mouse and, chicken. Genotyping of 55,013 bulls was performed using DNA microarrays and 11 SNPs were mapped to the five GRAMD genes. A phenome-wide association study (PheWAS) tested associations between the 11 SNPs and 36 traits. The integrated analysis of SNP effects, rankings, and clustering patterns revealed their potential for improving cattle productivity, health, and robustness, and established a baseline for the targeted improvement of cattle traits. This study lays the groundwork for functional experiments aimed at uncovering the mechanism of action of GRAMD genes and to evaluate the potential of using GRAMD sequence variants for selection programs in dairy cattle. The study presents an example of how the combination of GWAS and the PheWAS offers a promising toolbox for the systematic functional annotation of vertebrate genomes.

Cardioprotective role of oleanolic acid in patients with type 2 diabetes mellitus

Background

Patients with type 2 diabetes mellitus (T2DM) experience a decline in cardiac function, resulting in poor prognosis. Therefore, restoration of cardiac function and improvement of myocardial fibrosis is an important treatment goal for patients with T2DM.

Material and methods

The chemical structure of oleanolic acid(OA) was downloaded from PubChem and uploaded to PharmMapper. GeneCards and OMIM databases were searched for genes related to OA and disease and plotted into a Venn diagram. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using R software. Then, a mouse model of diabetes mellitus was established, and ELISA, echocardiographic analysis of cardiac function, TUNEL assay, and reactive oxygen species assay were performed.

Results

Network pharmacology analysis identified the related targets and potential molecular mechanisms underlying the effects of OA in T2DM. ELISA, echocardiographic analysis of cardiac function, and TUNEL assay results showed that OA inhibits apoptosis and improves apoptotic indexes in mice with T2DM-induced myocardial injury.

Conclusion

The results demonstrate the myocardial protective effect of OA in this mouse model.

The nonvesicular sterol transporter Aster-C plays a minor role in whole body cholesterol balance

Introduction

The Aster-C protein (encoded by the Gramd1c gene) is an endoplasmic reticulum (ER) resident protein that has been reported to transport cholesterol from the plasma membrane to the ER. Although there is a clear role for the closely-related Aster-B protein in cholesterol transport and downstream esterification in the adrenal gland, the specific role for Aster-C in cholesterol homeostasis is not well understood. Here, we have examined whole body cholesterol balance in mice globally lacking Aster-C under low or high dietary cholesterol conditions.

Method

Age-matched Gramd1c +/+ and Gramd1c -/- mice were fed either low (0.02%, wt/wt) or high (0.2%, wt/wt) dietarycholesterol and levels of sterol-derived metabolites were assessed in the feces, liver, and plasma.

Results

Compared to wild type controls (Gramd1c +/+) mice, mice lackingGramd1c (Gramd1c -/-) have no significant alterations in fecal, liver, or plasma cholesterol. Given the potential role for Aster C in modulating cholesterol metabolism in diverse tissues, we quantified levels of cholesterol metabolites such as bile acids, oxysterols, and steroid hormones. Compared to Gramd1c +/+ controls, Gramd1c -/- mice had modestly reduced levels of select bile acid species and elevated cortisol levels, only under low dietary cholesterol conditions. However, the vast majority of bile acids, oxysterols, and steroid hormones were unaltered in Gramd1c -/- mice. Bulk RNA sequencing in the liver showed that Gramd1c -/- mice did not exhibit alterations in sterol-sensitive genes, but instead showed altered expression of genes in major urinary protein and cytochrome P450 (CYP) families only under low dietary cholesterol conditions.

Discussion

Collectively, these data indicate nominal effects of Aster-C on whole body cholesterol transport and metabolism under divergent dietary cholesterol conditions. These results strongly suggest that Aster-C alone is not sufficient to control whole body cholesterol balance, but can modestly impact circulating cortisol and bile acid levels when dietary cholesterol is limited.

Knocking down GRAMD1C expression reduces 6-OHDA-induced apoptosis in PC12 cells

Aim

To explore the differential genes in Parkinson's disease (PD) through a preliminary GEO database, and to investigate the possible mechanisms.

Materials and Methods

The PD differentially expressed genes (DEGs) were analyzed by the microarray method. Then, these DEGs were applied to KEGG and GO analyses to predict the related signaling pathways and molecular functions. Comparison of GRAMD1C expression levels in the putamen of normal and Parkinson's patients by bioinformatic analysis. PC12 cells were cultured to construct a 6-hydroxydopamine (6-OHDA)-induced Parkinson's cell model. RT-qPCR was performed to detect the efficiency of GRAMD1C siRNA. MTT assay was conducted to examine the proliferation of cells. Then, the apoptosis of each group of cells was measured by flow cytometry. Western blot was carried out to determine the expression of apoptosis-related proteins.

Results

Through bioinformatics, GRAMD1C was confirmed to be one of the most significantly upregulated genes in PD. Furthermore, GRAMD1C was notably enhanced in the PD patients and 6-OHDA-induced PC12 cells. Besides, 6-OHDA stimulation significantly reduced PC12 cell proliferation, and it reverted with the GRAMD1C siRNA. Moreover, the flow cytometry results showed that knockdown of GRAMD1C could effectively reduce the high apoptosis rate of PC12 cells induced by 6-OHDA treatment. Similarly, western blot results found that 6-OHDA stimulation markedly increased the expression levels of Bax and Caspase 3Caspase 3 and decreased the Bcl-2 expression in PC12 cells, and GRAMD1C knockdown reversed these changes.

Conclusion

GRAMD1C is upregulated in PD, and may affect the PD process through the apoptotic pathway.

Unveiling the role of YARS1 in bladder cancer: A prognostic biomarker and therapeutic target

YARS is responsible for catalysing the binding of tyrosine to its cognate tRNA and plays a crucial role in basic biosynthesis. However, its biological functions in bladder cancer remains to be proven. We analysed variations in YARS1 expression and survival in bladder cancer using multiple data sets, including TCGA-BLCA, GSE13507 and bladder cancer-specific tissue microarrays. Furthermore, we explored the biological functions of YARS1 using transcriptome data. Our findings revealed a noteworthy correlation between YARS1 and immune infiltration in bladder cancer, as determined using the XCELL algorithm and single-cell analysis. In addition, we employed the TIDE algorithm to evaluate the responsiveness of different cohorts to immune checkpoint therapy. We investigated the regulatory associations between YARS1 and various aspects of bladder cancer, including senescence, ferroptosis and stemness. Finally, we established a ceRNA network that is directly linked to the overall prognosis, YARS1 can serve as a prognostic biomarker for bladder cancer; its interaction with MYC has implications for bladder cancer cell senescence, ferroptosis and stemness. Moreover, the identified ceRNA network has potential as a therapeutic target in bladder cancer.

The role of Cyclin Dependent Kinase Inhibitor 3 (CDKN3) in promoting human tumors: Literature review and pan-cancer analysis

Background

Although many experiments and clinical studies have proved the link between the expression of CDKN3 and human tumors, we have not been able to identify any bioinformatics study in which the extensive tumor-promoting effect of CDKN3 was systematically analyzed.

Objective

Explore the extensive tumor-promoting effects of CDKN3 and review the research progress of CDKN3 in cancer.

Methods

We systematically reviewed the literature on CDKN3 and tumors. We explored the potential tumor-promoting effects of CDKN3 on different tumors in the TCGA database and the GTEx database using multiple platforms and websites. We studied the expression level of CDKN3, survival, prognosis, diagnosis, genetic variation, immune infiltration, and enrichment analysis using databases such as TIMER 2.0, GEPIA2, cBioPortal, and STRING.

Results

We found that CDKN3 is highly expressed in most tumors. The expression of CDKN3 is closely related to the prognosis of some tumors. And CDKN3 may have diagnostic value. The conclusion of our literature review is roughly the same, but there are differences, which are worthy of further study. Moreover, CDKN3 may be related to immune cell infiltration in tumor tissues. The genetic alteration of LUAD, STAD, SARC, PCPG, and ESCA with "Amplification" as the main type. In addition, through enrichment analysis, we found that CDKN3 affects tumors mainly through the control of the cell cycle and mitosis.

Conclusion

CDKN3 is highly expressed in most tumor tissues and has a statistical correlation with survival prognosis. It has extensive tumor-promoting effects that may be related to mechanisms such as immune infiltration.

A novel immune-related gene signature for predicting immunotherapy outcomes and survival in clear cell renal cell carcinoma

Clear cell renal carcinoma (ccRCC) is one of the most common cancers worldwide. In this study, a new model of immune-related genes was developed to predict the overall survival and immunotherapy efficacy in patients with ccRCC. Immune-related genes were obtained from the ImmPort database. Clinical data and transcriptomics of ccRCC samples were downloaded from GSE29609 and The Cancer Genome Atlas. An immune-related gene-based prognostic model (IRGPM) was developed using the least absolute shrinkage and selection operator regression algorithm and multivariate Cox regression. The reliability of the developed models was evaluated by Kaplan-Meier survival curves and time-dependent receiver operating characteristic curves. Furthermore, we constructed a nomogram based on the IRGPM and multiple clinicopathological factors, along with a calibration curve to examine the predictive power of the nomogram. Overall, this study investigated the association of IRGPM with immunotherapeutic efficacy, immune checkpoints, and immune cell infiltration. Eleven IRGs based on 528 ccRCC samples significantly associated with survival were used to construct the IRGPM. Remarkably, the IRGPM, which consists of 11 hub genes (SAA1, IL4, PLAUR, PLXNB3, ANGPTL3, AMH, KLRC2, NR3C2, KL, CSF2, and SEMA3G), was found to predict the survival of ccRCC patients accurately. The calibration curve revealed that the nomogram developed with the IRGPM showed high predictive performance for the survival probability of ccRCC patients. Moreover, the IRGPM subgroups showed different levels of immune checkpoints and immune cell infiltration in patients with ccRCC. IRGPM might be a promising biomarker of immunotherapeutic responses in patients with ccRCC. Overall, the established IRGPM was valuable for predicting survival, reflecting the immunotherapy response and immune microenvironment in patients with ccRCC.

Development and Validation of a Novel Prognosis Model Based on a Panel of Three Immunogenic Cell Death-Related Genes for Non-Cirrhotic Hepatocellular Carcinoma

Purpose

The accurate prediction of non-cirrhotic hepatocellular carcinoma (NCHCC) risk facilitates improved surveillance strategy and decreases cancer-related mortality. This study aimed to explore the correlation between immunogenic cell death (ICD) and NCHCC prognosis using The Cancer Genome Atlas (TCGA) datasets, and the potential prognostic value of ICD-related genes in NCHCC.

Methods

Clinical and transcriptomic data of patients with NCHCC patients were retrieved from TCGA database. Weighted gene co-expression network analysis was performed to obtain the NCHCC phenotype-related module genes. Consensus clustering analysis was performed to classify the patients into two clusters based on intersection genes among differentially expressed genes (DEGs) between cancer and adjacent tissues, NCHCC phenotype-related genes, and ICD-related genes. NCHCC-derived tissue microarray was used to evaluate the correlation of the expression levels of key genes with NCHCC prognosis using immunohistochemical staining.

Results

Cox regression analyses were performed to construct a prognostic risk score model comprising three genes (TMC7, GRAMD1C, and GNPDA1) based on DEGs between two clusters. The model stratified patients with NCHCC into two risk groups. The overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group. Univariable and multivariable Cox regression analyses revealed that these signature genes are independent predictors of OS. Functional analysis revealed differential immune status between the two risk groups. Next, a nomogram was constructed, which demonstrated the potent distinguishing ability of the developed model based on receiver operating characteristic curves. In vitro functional validation revealed that the migration and invasion abilities of HepG2 and Huh7 cells were upregulated upon GRAMD1C knockdown but downregulated upon TMC7 knockdown.

Conclusion

This study developed a prognostic model comprising three genes, which can aid in predicting the survival of patients with NCHCC and guide the selection of drugs and molecular markers for NCHCC.

ETNK2 Low-Expression Predicts Poor Prognosis in Renal Cell Carcinoma with Immunosuppressive Tumor Microenvironment

Background

The ethanolamine kinase 2 (ETNK2) gene is implicated in carcinogenesis, but its expression and involvement in kidney renal clear cell carcinoma (KIRC) remain unknown.

Methods

Initially, we conducted a pan-cancer study in which we searched the Gene Expression Profiling Interactive Analysis, the UALCAN, and the Human Protein Atlas databases to determine the expression level of the ETNK2 gene in KIRC. The Kaplan-Meier curve was then used to calculate the overall survival (OS) of KIRC patients. We then used the differentially expressed genes (DEGs) and enrichment analysis to explain the mechanism of the ETNK2 gene. Finally, the immune cell infiltration analysis was performed.

Results

Although the ETNK2 gene expression was lower in KIRC tissues, the findings illustrated a link between the ETNK2 gene expression and a shorter OS time for KIRC patients. DEGs and enrichment analysis revealed that the ETNK2 gene in KIRC involved multiple metabolic pathways. Finally, the ETNK2 gene expression has been linked to several immune cell infiltrations.

Conclusions

According to the findings, the ETNK2 gene plays a crucial role in tumor growth. It can potentially serve as a negative prognostic biological marker for KIRC by modifying immune infiltrating cells.

Stem Cell-Associated Signatures Help to Predict Diagnosis and Prognosis in Ovarian Serous Cystadenocarcinoma

Ovarian serous cystadenocarcinoma (OV) is a fatal gynecologic cancer with a five-year survival rate of only 46%. Resistance to platinum-based chemotherapy is a prevalent factor in OV patients, leading to increased mortality. The platinum resistance in OV is driven by transcriptome heterogeneity and tumor heterogeneity. Studies have indicated that ovarian cancer stem cells (OCSCs), which are chemoresistant and help in disease recurrence, are enriched by platinum-based chemotherapy. Stem cells have a significant influence on the OV progression and prognosis of OV patients and are key pathology mediators of OV. However, the molecular mechanisms and targets of OV have not yet been fully understood. In this study, systematic research based on the TCGA-OV dataset was conducted for the identification and construction of key stem cell-related diagnostic and prognostic models for the development of multigene markers of OV. A six-gene diagnostic and prognostic model (C19orf33, CBX2, CSMD1, INSRR, PRLR, and SLC38A4) was developed based on the differentially expressed stem cell-related gene model, which can act as a potent diagnostic biomarker and can characterize the clinicopathological properties of OV. The key genes related to stem cells were identified by screening the genes differentially expressed in OV and control samples. The mRNA-miRNA-TF molecular network for the six-gene model was constructed, and the potential biological significance of this molecular model and its impact on the infiltration of immune cells in the OV tumor microenvironment were elucidated. The differences in immune infiltration and stem cell-related biological processes were determined using gene set variation analysis (GSVA) and single-sample gene set enrichment analysis (ssGSEA) for the selection of molecular treatment options and providing a reference for elucidating the posttranscriptional regulatory mechanisms in OV.

The cholesterol transport protein GRAMD1C regulates autophagy initiation and mitochondrial bioenergetics

During autophagy, cytosolic cargo is sequestered into double-membrane vesicles called autophagosomes. The contributions of specific lipids, such as cholesterol, to the membranes that form the autophagosome, remain to be fully characterized. Here, we demonstrate that short term cholesterol depletion leads to a rapid induction of autophagy and a corresponding increase in autophagy initiation events. We further show that the ER-localized cholesterol transport protein GRAMD1C functions as a negative regulator of starvation-induced autophagy and that both its cholesterol transport VASt domain and membrane binding GRAM domain are required for GRAMD1C-mediated suppression of autophagy initiation. Similar to its yeast orthologue, GRAMD1C associates with mitochondria through its GRAM domain. Cells lacking GRAMD1C or its VASt domain show increased mitochondrial cholesterol levels and mitochondrial oxidative phosphorylation, suggesting that GRAMD1C may facilitate cholesterol transfer at ER-mitochondria contact sites. Finally, we demonstrate that expression of GRAMD family proteins is linked to clear cell renal carcinoma survival, highlighting the pathophysiological relevance of cholesterol transport proteins.

Upregulation of EMID1 Accelerates to a Favorable Prognosis and Immune Infiltration in Lung Adenocarcinoma

Lung cancer is a difficult-to-treat cancer. Lung adenocarcinoma (LUAD) is the main subtype of lung cancer. Although there are many ways to treat lung cancer, the survival rate of patients is low. Therefore, novel molecules need to be identified to diagnose and treat LUAD. This study utilized The Cancer Genome Atlas (TCGA) LUAD data to analyze and validate the value of EMID1 as a LUAD diagnostic surface marker and overall survival prognostic marker. Differential expression analysis formally confirmed that decreased EMID1 expression was significantly associated with advanced stage and metastasis of lung cancer. Kaplan-Meier survival analysis showed that the patients with low EMID expression are dismal. The relationship between clinicopathological features and EMID1 was scored using Wilcoxon signed-rank test and R (v.3.5.1) logistic regression and suggested that patients with low EMID1 expression had a worse prognosis than patients with high EMID1 expression. (Gene Ontology) GO, Kyoto Encyclopedia of Genes and Genomes(KEGG), and gene set enrichment analysis (GSEA) were performed to investigate the potential mechanism of EMID1 expression on the prognosis of LUAD and suggested that Notch signaling pathway may be an important biological pathway for EMID1 to play a role in LUAD. Further, combined with univariate and multivariate Cox regression analysis, it was speculated that high and low levels of EMID1 expression and the logistic regression analysis of related clinical variables had significant clinical significance to verify the underlying mechanism of LUAD focus and prognosis. EMID1 plays an important role in the immune milieu of LUAD. Meanwhile, the correlation between tumor-infiltrating immune cells and genes was assessed using CIBERSORT, and it was found that the level of B cell infiltration was positively correlated with the expression of EMID1, all of which were validated in the GEO and GEPIA databases. In all, this study helps to understand the immune microenvironment of LUAD and improve the survival of patients with LUAD. Thus, EMID1 may be a novel immune-related prognostic marker of LUAD.

A cholesterogenic gene signature for predicting the prognosis of young breast cancer patients

Purpose

We aimed to establish a cholesterogenic gene signature to predict the prognosis of young breast cancer (BC) patients and then verified it using cell line experiments.

Methods

In the bioinformatic section, transcriptional data and corresponding clinical data of young BC patients (age ≤ 45 years) were downloaded from The Cancer Genome Atlas (TCGA) database for training set. Differentially expressed genes (DEGs) were compared between tumour tissue (n = 183) and normal tissue (n = 30). By using univariate Cox regression and multi COX regression, a five-cholesterogenic-gene signature was established to predict prognosis. Subgroup analysis and external validations of GSE131769 from the Gene Expression Omnibus (GEO) were performed to verify the signature. Subsequently, in experiment part, cell experiments were performed to further verify the biological roles of the five cholesterogenic genes in BC.

Results

In the bioinformatic section, a total of 97 upregulated genes and 124 downregulated cholesterogenic genes were screened as DEGs in the TCGA for training the model. A risk scoring signature contained five cholesterogenic genes (risk score = -1.169 × GRAMD1C -0.992 × NFKBIA + 0.432 × INHBA + 0.261 × CD24 -0.839 × ACSS2) was established, which could differentiate the prognosis of young BC patients between high-risk and low-risk group (<0.001). The prediction value of chelesterogenic gene signature in excellent with AUC was 0.810 in TCGA dataset. Then the prediction value of the signature was verified in GSE131769 with P = 0.033. In experiment part, although the downregulation of CD24, GRAMD1C and ACSS2 did not significantly affect cell viability, NFKBIA downregulation promoted the viability, colony forming ability and invasion capability of BC cells, while INHBA downregulation had the opposite effects.

Conclusion

The five-cholesterogenic-gene signature had independent prognostic value and robust reliability in predicting the prognosis of young BC patients. The cell experiment results suggested that NFKBIA played a protective role, while INHBA played the pro-cancer role in breast cancer.

Identification of a Four-Gene Signature for Diagnosing Paediatric Sepsis

Aim

Early diagnosis of paediatric sepsis is crucial for the proper treatment of children and reduction of hospitalization and mortality. Biomarkers are a convenient and effective method for diagnosing any disease. However, huge differences among the studies reporting biomarkers for diagnosing sepsis have limited their clinical application. Therefore, in this study, we aimed to evaluate the diagnostic value of key genes involved in paediatric sepsis based on the data of the Gene Expression Omnibus database.

Methods

We used the GSE119217 dataset to identify differentially expressed genes (DEGs) between patients with and without paediatric sepsis. The most relevant gene modules of paediatric sepsis were screened through the weighted gene coexpression network analysis (WGCNA). Common genes (CGs) were found between DEGs and WGCNA. Genes with a potential diagnostic value in paediatric sepsis were selected from the CGs using least absolute shrinkage and selection operator regression and support vector machine recursive feature elimination. The principal component analysis, receiver operating characteristic curves, and C-index were used to verify the diagnostic value of the identified genes in six other independent sepsis datasets. Subsequently, a meta-analysis of the selected genes was performed to evaluate the value of these genes as biomarkers in paediatric sepsis.

Results

A total of 41 CGs were selected from the GSE119217 dataset. A four-gene signature composed of ANXA3, CD177, GRAMD1C, and TIGD3 effectively distinguished patients with paediatric sepsis from those in the control group. The signature was verified using six other independent datasets. In addition, the meta-analysis results showed that the pooled sensitivity, specificity, and area under the curve values were 1.00, 0.98, and 1.00, respectively.

Conclusion

The four-gene signature can be used as new biomarkers to distinguish patients with paediatric sepsis from healthy individuals.

Identification and Validation of a Novel Six-Gene Expression Signature for Predicting Hepatocellular Carcinoma Prognosis

Background

Hepatocellular carcinoma (HCC) is a highly lethal disease. Effective prognostic tools to guide clinical decision-making for HCC patients are lacking.

Objective

We aimed to establish a robust prognostic model based on differentially expressed genes (DEGs) in HCC.

Methods

Using datasets from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), and the International Genome Consortium (ICGC), DEGs between HCC tissues and adjacent normal tissues were identified. Using TCGA dataset as the training cohort, we applied the least absolute shrinkage and selection operator (LASSO) algorithm and multivariate Cox regression analyses to identify a multi-gene expression signature. Proportional hazard assumptions and multicollinearity among covariates were evaluated while building the model. The ICGC cohort was used for validation. The Pearson test was used to evaluate the correlation between tumor mutational burden and risk score. Through single-sample gene set enrichment analysis, we investigated the role of signature genes in the HCC microenvironment.

Results

A total of 274 DEGs were identified, and a six-DEG prognostic model was developed. Patients were stratified into low- or high-risk groups based on risk scoring by the model. Kaplan-Meier analysis revealed significant differences in overall survival and progression-free interval. Through univariate and multivariate Cox analyses, the model proved to be an independent prognostic factor compared to other clinic-pathological parameters. Time-dependent receiver operating characteristic curve analysis revealed satisfactory prediction of overall survival, but not progression-free interval. Functional enrichment analysis showed that cancer-related pathways were enriched, while immune infiltration analyses differed between the two risk groups. The risk score did not correlate with levels of PD-1, PD-L1, CTLA4, or tumor mutational burden.

Conclusions

We propose a six-gene expression signature that could help to determine HCC patient prognosis. These genes may serve as biomarkers in HCC and support personalized disease management.

A pan-cancer analysis revealing the role of TIGIT in tumor microenvironment

T cell immunoreceptor with immunoglobulin and ITIM domain (TIGIT), an immune checkpoint, plays a pivotal role in immune suppression. However its role in tumor immunity and correlation with the genetic and epigenetic alterations remains unknown. Here, we comprehensively analyzed the expression patterns of the TIGIT and its value of prognostic prediction among 33 types of cancers based on the data collected from The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression projects (GTEx). Furthermore, the correlations of TIGIT with pathological stages, tumor-infiltrating immune cells (TIICs), signatures of T cells subtypes, immune checkpoint genes, the degree of Estimation of STromal and Immune cells in MAlignant Tumor tissues using the Expression data (ESTIMATE), tumor mutation burden (TMB), microsatellite instability (MSI), mismatch repair (MMR) genes, and DNA methyltransferases (DNMTs) were also explored. Gene functional enrichment was conducted by Gene Set Enrichment Analysis (GSEA). Our results showed that the expression of TIGIT was upregulated in most of the cancer types. Cox regression model showed that high expression of TIGIT in tumor samples correlates with poor prognosis in KIRC, KIRP, LGG, UVM, and with favorable prognosis in BRCA, CECS, HNSC, SKCM. TIGIT expression positively correlated with advanced stages, TIICs, the signatures of effector T cells, exhausted T cells, effector Tregs and the degree of ESTIMATE in KIRC, KIRP and UVM. TIGIT expression also positively correlated with CTLA4, PDCD1 (PD-1), CD274 (PD-L1), ICOS in most of the cancer types. Furthermore, the expression of TIGIT was correlated with TMB, MSI, MMR genes and DNMTs in different types of cancers. GSEA analysis showed that the expression of TIGIT was related to cytokine-cytokine receptor interaction, allograft rejection, oxidative phosphorylation. These findings suggested that TIGIT could serve as a potential biomarker for prognosis and a novel target for immunotherapies in cancers.

A united risk model of 11 immune‑related gene pairs and clinical stage for prediction of overall survival in clear cell renal cell carcinoma patients

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer. Currently, we lack effective risk models for the prognosis of ccRCC patients. Given the significant role of cancer immunity in ccRCC, we aimed to establish a novel united risk model including clinical stage and immune-related gene pairs (IRGPs) to assess the prognosis. The gene expression profile and clinical data of ccRCC patients from The Cancer Genome Atlas and Arrayexpress were divided into training cohort (n = 381), validation cohort 1 (n = 156), and validation cohort 2 (n = 101). Through univariate Cox regression analysis and Least Absolute Shrinkage and Selection Operator analysis, 11 IRGPs were obtained. After further analysis, it was found that clinical stage could be an independent prognostic factor; hence, we used it to construct a united prognostic model with 11 IRGPs. Based on this model, patients were divided into high-risk and low-risk groups. In Kaplan–Meier analysis, a significant difference was observed in overall survival (OS) among all three cohorts (p < 0.001). The calibration curve revealed that the signature model is in high accordance with the observed values of each data cohort. The 1-year, 3-year, and 5-year receiver operating characteristic curves of each data cohort showed better performance than only IRGP signatures. The results of immune infiltration analysis revealed significantly (p < 0.05) higher abundance of macrophages M0, T follicular helper cells, and other tumor infiltrating cells. In summary, we successfully established a united prognostic risk model, which can effectively assess the OS of ccRCC patients.

Role of methyltransferase-like enzyme 3 and methyltransferase-like enzyme 14 in urological cancers

N6-methyladenosine (m6A) modifications can be found in eukaryotic messenger RNA (mRNA), long non-coding RNA (lncRNA), and microRNA (miRNA). Several studies have demonstrated a close relationship between m6A modifications and cancer cells. Methyltransferase-like enzyme 3 (METTL3) and methyltransferase-like enzyme 14 (METTL14) are two major enzymes involved in m6A modifications that play vital roles in various cancers. However, the roles and regulatory mechanisms of METTL3 and METTL14 in urological cancers are largely unknown. In this review, we summarize the current research results for METTL3 and METTL14 and identify potential pathways involving these enzymes in kidney, bladder, prostate, and testicular cancer. We found that METTL3 and METTL14 have different expression patterns in four types of urological cancers. METTL3 is highly expressed in bladder and prostate cancer and plays an oncogenic role on cancer cells; however, its expression and role are opposite in kidney cancer. METTL14 is expressed at low levels in kidney and bladder cancer, where it has a tumor suppressive role. Low METTL3 or METTL14 expression in cancer cells negatively regulates cell growth-related pathways (e.g., mTOR, EMT, and P2XR6) but positively regulates cell death-related pathways (e.g., P53, PTEN, and Notch1). When METTL3 is highly expressed, it positively regulates the NF-kB and SHH-GL1pathways but negatively regulates PTEN. These results suggest that although METTL3 and METTL14 have different expression levels and regulatory mechanisms in urological cancers, they control cancer cell fate via cell growth- and cell death-related pathways. These findings suggest that m6A modification may be a potential new therapeutic target in urological cancer.