An Innovative Prognostic Model Based on Four Genes in Asian Patient with Gastric Cancer

Evaluation of vital genes correlated with CD8 + T cell infiltration as prognostic biomarkers in stomach adenocarcinoma

BACKGROUND

Infiltration of CD8 + T cells in the tumor microenvironment is correlated with better prognosis in various malignancies. Our study aimed to investigate vital genes correlated with CD8 + T cell infiltration in stomach adenocarcinoma (STAD) and develop a new prognostic model.

METHODS

Using the STAD dataset, differentially expressed genes (DEGs) were analyzed, and co-expression networks were constructed. Combined with the CIBERSORT algorithm, the most relevant module of WGCNA with CD8 + T cell infiltration was selected for subsequent analysis. The vital genes were screened out by univariate regression analysis to establish the risk score model. The expression of the viral genes was verified by lasso regression analysis and in vitro experiments.

RESULTS

Four CD8 + T cell infiltration-related genes (CIDEC, EPS8L3, MUC13, and PLEKHS1) were correlated with the prognosis of STAD. Based on these genes, a risk score model was established. We found that the risk score could well predict the prognosis of STAD, and the risk score was positively correlated with CD8 + T cell infiltration. The validation results of the gene expression were consistent with TCGA. Furthermore, the risk score was significantly higher in tumor tissues. The high-risk group had poorer overall survival (OS) in each subgroup.

CONCLUSIONS

Our study constructed a new risk score model for STAD prognosis, which may provide a new perspective to explore the tumor immune microenvironment mechanism in STAD.

PLEKHS1 drives PI3Ks and remodels pathway homeostasis in PTEN-null prostate

The PIP3/PI3K network is a central regulator of metabolism and is frequently activated in cancer, commonly by loss of the PIP3/PI(3,4)P2 phosphatase, PTEN. Despite huge research investment, the drivers of the PI3K network in normal tissues and how they adapt to overactivation are unclear. We find that in healthy mouse prostate PI3K activity is driven by RTK/IRS signaling and constrained by pathway feedback. In the absence of PTEN, the network is dramatically remodeled. A poorly understood YXXM- and PIP3/PI(3,4)P2-binding PH domain-containing adaptor, PLEKHS1, became the dominant activator and was required to sustain PIP3, AKT phosphorylation, and growth in PTEN-null prostate. This was because PLEKHS1 evaded pathway-feedback and experienced enhanced PI3K- and Src-family kinase-dependent phosphorylation of Y258XXM, eliciting PI3K activation. hPLEKHS1 mRNA and activating Y419 phosphorylation of hSrc correlated with PI3K pathway activity in human prostate cancers. We propose that in PTEN-null cells receptor-independent, Src-dependent tyrosine phosphorylation of PLEKHS1 creates positive feedback that escapes homeostasis, drives PIP3 signaling, and supports tumor progression.

Analysis and application of RNA binding protein gene pairs to predict the prognosis of gastric cancer

Background

RNA-binding proteins (RBPs) are closely related to tumors, but little is known about the mechanism of RBPs in tumorigenesis and progression of gastric cancer (GC). As genes do not usually act alone in the pathway deregulation, gene pair combinations are more likely to become stable and accurate biomarkers. The purpose of our research is to establish a novel signature based on RBP gene pairs to predict the prognosis of gastric cancer patients.

Methods

We downloaded genetic and clinical information from the TCGA and GEO database. TCGA and GSE13911 were used for screening differentially expressed genes (DEGs). The RBP genes were gathered from previous studies and employed to screen out DE-RBP genes after intersecting with DEGs. Samples were classified according to the relative expression of each pair of DE-RBP genes. The univariate Cox regression analysis and random forest were used to identify hub gene pairs to construct signature for predicting the prognosis of gastric cancer. Time-dependent ROC curves and KM survival curves were performed to evaluate the signature. GSEA was performed in TCGA training cohort and GSE62254 testing cohort to analyze enrichment pathways. Finally, the influence of these gene pairs on the prognosis of GC patients was further elucidated respectively through the combination of high and low expression of the two genes in each hub gene pair.

Results

We screened out 6 hub RBP gene pairs (COL5A2/FEN1, POP1/GFRA1, EXO1/PLEKHS1, SLC39A10/CHI3L1, MMP7/PPP1R1 B and SLC5A6/BYSL) to predict the prognosis of patients with gastric cancer. Using the optimal cut-off value to divide patients into high-risk and low-risk groups in the training and testing cohort, we found that the overall survival (OS) of the low-risk group was higher than that of the high-risk group (P < 0.05). The area under the ROC curves for 1, 3, and 5 years were (0.659, 0.744, 0.758) and (0.624, 0.650, 0.653) in two cohorts. Univariate and multivariate Cox regression analysis showed that 6 RBP gene pairs signature were independent prognostic factors for gastric cancer (P < 0.05). In addition, the prognostic survival analysis showed that COL5A2-high/FEN1-low, POP1-low/GFRA1-high, EXO1-low/PLEKHS1-low,SLC39A10-high/CHI3L1-low, MMP7-high/PPP1R1 B-low, SLC5A6-low/BYSL-low had worse OS (P < 0.05). And the gene correlation analysis showed that there was no obvious correlation between the genes in each gene pairs except SLC5A6/BYSL and POP1/GFRA1. Finally, GSEA analysis showed that the high-risk group was enriched in tumor migration, invasion and growth-related pathways.

Conclusion

Our study identified a novel 6 RBP gene pairs signature to predict the prognosis of gastric cancer patients and provide potential targets for clinical gene therapy.

An integrated analysis of prognostic mRNA signature in early- and progressive-stage gastric adenocarcinoma

The pathogenesis and vital factors of early and progressive stages of stomach adenocarcinoma (STAD) have not been fully elucidated. In order to discover novel and potential targets to guide effective treatment strategies, a comprehensive bioinformatics study was performed, and the representative results were then validated by quantitative polymerase chain reaction (qPCR) and immunohistochemical (IMC) staining in clinical samples. A total of 4,627, 4,715, and 3,465 differentially expressed genes (DEGs) from overall-, early-, and progressive-stage STAD were identified, respectively. Prognostic models of 5-year OS were established for overall-, early-, and progressive-stage STAD, and ROC curves demonstrated AUC values for each model were 0.73, 0.87, and 0.92, respectively. Function analysis revealed that mRNAs of early-stage STAD were enriched in chemical stimulus-related pathways, whereas remarkable enrichment of mRNAs in progressive-stage STAD mainly lay in immune-related pathways. Both qPCR and IHC data confirmed the up-regulation of IGFBP1 in the early-stage and CHAF1A in progressive-stage STAD compared with their matched normal tissues, indicating that these two representative targets could be used to predict the prognostic status of the patients in these two distinct STAD stages, respectively. In addition, seven mRNAs (F2, GRID2, TF, APOB, KIF18B, INCENP, and GCG) could be potential novel biomarkers for STAD at different stages from this study. These results contributed to identifying STAD patients at high-risk, thus guiding targeted treatment with efficacy in these patients.

Three Prognostic Biomarkers Correlate with Immune Checkpoint Blockade Response in Bladder Urothelial Carcinoma

Aim

We aim to develop a signature that could accurately predict prognosis and evaluate the response to immune checkpoint blockade (ICB) in bladder urothelial carcinoma (BLCA).

Methods

Based on comprehensive analysis of public database, we identified prognosis-related hub genes and investigated their predictive values for the ICB response in BLCA.

Results

Among 69 common DEGs, three genes (AURKA, BIRC5, and CKS1B) were associated with poor prognosis, and which were related to histological subtypes, TP53 mutation status, and the C2 (IFN-gamma dominant) subtype. Three genes and their related risk model can effectively predict the response of immunotherapy. Their related drugs were identified through analysis of drug bank database.

Conclusions

Three genes could predict prognosis and evaluate the response to ICB in BLCA.

Bayesian hierarchical lasso Cox model: A 9-gene prognostic signature for overall survival in gastric cancer in an Asian population

Objective

Gastric cancer (GC) is one of the most common tumour diseases worldwide and has poor survival, especially in the Asian population. Exploration based on biomarkers would be efficient for better diagnosis, prediction, and targeted therapy.

Methods

Expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Survival-related genes were identified by gene set enrichment analysis (GSEA) and univariate Cox. Then, we applied a Bayesian hierarchical lasso Cox model for prognostic signature screening. Protein-protein interaction and Spearman analysis were performed. Kaplan–Meier and receiver operating characteristic (ROC) curve analysis were applied to evaluate the prediction performance. Multivariate Cox regression was used to identify prognostic factors, and a prognostic nomogram was constructed for clinical application.

Results

With the Bayesian lasso Cox model, a 9-gene signature included TNFRSF11A, NMNAT1, EIF5A, NOTCH3, TOR2A, E2F8, PSMA5, TPMT, and KIF11 was established to predict overall survival in GC. Protein-protein interaction analysis indicated that E2F8 was likely related to KIF11. Kaplan-Meier analysis showed a significant difference between the high-risk and low-risk groups (P<0.001). Multivariate analysis demonstrated that the 9-gene signature was an independent predictor (HR = 2.609, 95% CI 2.017–3.370), and the C-index of the integrative model reached 0.75. Function enrichment analysis for different risk groups revealed the most significant enrichment pathway/term, including pyrimidine metabolism and respiratory electron transport chain.

Conclusion

Our findings suggested that a novel prognostic model based on a 9-gene signature was developed to predict GC patients in high-risk and improve prediction performance. We hope our model could provide a reference for risk classification and clinical decision-making.

Identification of lncRNAs based on different patterns of immune infiltration in gastric cancer

BACKGROUND

Gastric cancer is one of the most common malignant tumors in the world, which brings great challenges to people's life and health. The purpose of this study was to investigate immune related-lncRNAs and identify new biomarkers for the prognosis of gastric cancer (GC).

METHODS

We downloaded data from The Cancer Genome Atlas (TCGA) and used R software to determine the ESTIMATEScore, ImmuneScore, and StromalScore of each tumor sample. We performed prognostic analysis and identified the differentially expressed lnRNAs, which were then used to construct a prognostic model. Among the 44 hub genes in the competitive endogenous RNA (ceRNA) network, 3 differentially expressed genes were verified by qPCR.

RESULTS

Based on the degree of immune infiltration, cluster A had a higher ESTIMATEScore, ImmuneScore, and StromalScore and higher expression levels of PD-L1 (CD274) and CTLA4 than cluster B. Univariate Cox analysis was conducted for these differential lncRNAs, and 57 lncRNAs were found to have prognostic value (P<0.05). gene cluster A had a worse prognosis than gene cluster B (P=0.021). Then, a prognostic model was constructed. The low-risk group had a significantly higher survival rate. Finally, the qPCR results showed that the expression levels of BMPER, PRUNE2, and RBPMS2 were low in GC cell lines.

CONCLUSIONS

We identified a risk score of 19 lncRNAs as a prognostic marker of GC. There was a relationship between these 19 prognostic-related lncRNAs and the subtypes of infiltrating immune cells. An approach for predicting the prognosis of GC was therefore provided in this study.

Integrative analysis-based identification and validation of a prognostic immune cell infiltration-based model for patients with advanced gastric cancer

BACKGROUNDS

Advanced gastric cancer (GC) remains difficult to conduct individualized prognostic evaluations owing to the highly heterogeneous nature and the low level of immune cell infiltration (ICI) within GC tumors. This study thus sought to develop a model capable of classifying GC patients according to the degree of tumor ICI and gauging prognosis.

METHODS

The degree of ICI in GC patients from the GSE15459, GSE57303, and GSE62254 datasets were estimated, and these values were used to group patients via an unsupervised clustering approach, after which ICI cluster-related genes were identified the association with prognosis through Cox and LASSO regression analyses. The primary risk genes were then verified by immunohistochemical staining of GC tumor tissue samples.

RESULTS

570 patients were clustered into three clusters and 289 ICI cluster-related genes were identified. A prognostic model based on the expression of six crucial ICI risk genes (CXCL11, RBPMS2, LOC400043, JCHAIN, CT83, and ORM1) wa constructed. Patients identified as being high risk based upon the model have poorer clinical features and survival outcomes compared to the other patients. Adjuvant intervention was found to be more beneficial for patients expressing high levels of RBPMS2, JCHAIN, or ORM1. Furthermore, patients expressing low levels of JCHAIN or CT83 in GC tumor tissues were verified to exhibit a significantly better prognosis in a CMU cohort.

CONCLUSION

Advanced GC patients were successfully grouped into clusters based on the degree of intratumoral ICI, and a prognostic evaluation model based on 6 ICI risk genes was developed and validated.

Multiomics analysis reveals CT83 is the most specific gene for triple negative breast cancer and its hypomethylation is oncogenic in breast cancer

Triple-negative breast cancer (TNBC) is a highly aggressive breast cancer (BrC) subtype lacking effective therapeutic targets currently. The development of multi-omics databases facilities the identification of core genes for TNBC. Using TCGA-BRCA and METABRIC datasets, we identified CT83 as the most TNBC-specific gene. By further integrating FUSCC-TNBC, CCLE, TCGA pan-cancer, Expression Atlas, and Human Protein Atlas datasets, we found CT83 is frequently activated in TNBC and many other cancers, while it is always silenced in non-TNBC, 120 types of normal non-testis tissues, and 18 types of blood cells. Notably, according to the TCGA-BRCA methylation data, hypomethylation on chromosome X 116,463,019 to 116,463,039 is significantly correlated with the abnormal activation of CT83 in BrC. Using Kaplan-Meier Plotter, we demonstrated that activated CT83 is significantly associated with unfavorably overall survival in BrC and worse outcomes in some other cancers. Furthermore, GSEA suggested that the abnormal activation of CT83 in BrC is probably oncogenic by triggering the activation of cell cycle signaling. Meanwhile, we also noticed copy number variations and mutations of CT83 are quite rare in any cancer type, and its role in immune infiltration is not significant. In summary, we highlighted the significance of CT83 for TNBC and presented a comprehensive bioinformatics strategy for single-gene analysis in cancer.

KK-LC-1 may be an effective prognostic biomarker for gastric cancer

Background

The objective of the study was to detect the expression of Kita-Kyushu lung cancer antigen-1 (KK-LC-1) in gastric cancer (GC) specimens and analyse the associations between KK-LC-1 expression and clinicopathological parameters and clinical prognosis.

Methods

All of the 94 patients in this study were GC patients who underwent surgical resection. KK-LC-1 protein expression in GC tissue was detected by immunohistochemistry. This report applies the histological score (H-score) to evaluate KK-LC-1 expression. To calculate this indicator, the number of positive cells in each section and their staining intensity were converted to corresponding values. The expression of KK-LC-1 in the cytoplasm of cancer and normal tissues was scored to obtain their respective H values. The chi-square test, Kaplan-Meier method and Cox regression were used to analyse the linear association between KK-LC-1 expression and clinicopathological data and prognosis.

Results

In the cytoplasm, KK-LC-1 expression in tumour tissues was significantly higher than that in normal tissues (P < 0.001). Using the median H-score as the cut-off value, we discovered that GC patients with high levels of KK-LC-1 expression in the cytoplasm had favourable overall survival (OS) (P = 0.016), and this result was statistically significant in the Cox regression analysis. Additionally, a negative correlation was found between KK-LC-1 protein expression and the pathological grade of the tumour (P = 0.036), with significantly more KK-LC-1 protein expression observed in the intestinal type of GC than in the diffuse type (P = 0.008).

Conclusions

Our research data showed that KK-LC-1 expression was greater in GC tissues than in normal tissues, and higher KK-LC-1 expression was associated with longer OS of GC patients. KK-LC-1 can be used as a biomarker for a good prognosis in GC patients.