Development and Validation of a Scoring System Based on 9 Glycolysis-Related Genes for Prognosis Prediction in Gastric Cancer

Characterization of the Prognosis and Tumor Microenvironment of Cellular Senescence-related Genes through scRNA-seq and Bulk RNA-seq Analysis in GC

BACKGROUND

Cellular senescence (CS) is thought to be the primary cause of cancer development and progression. This study aimed to investigate the prognostic role and molecular subtypes of CS-associated genes in gastric cancer (GC).

MATERIALS AND METHODS

The CellAge database was utilized to acquire CS-related genes. Expression data and clinical information of GC patients were obtained from The Cancer Genome Atlas (TCGA) database. Patients were then grouped into distinct subtypes using the "Consesus- ClusterPlus" R package based on CS-related genes. An in-depth analysis was conducted to assess the gene expression, molecular function, prognosis, gene mutation, immune infiltration, and drug resistance of each subtype. In addition, a CS-associated risk model was developed based on Cox regression analysis. The nomogram, constructed on the basis of the risk score and clinical factors, was formulated to improve the clinical application of GC patients. Finally, several candidate drugs were screened based on the Cancer Therapeutics Response Portal (CTRP) and PRISM Repurposing dataset.

RESULTS

According to the cluster result, patients were categorized into two molecular subtypes (C1 and C2). The two subtypes revealed distinct expression levels, overall survival (OS) and clinical presentations, mutation profiles, tumor microenvironment (TME), and drug resistance. A risk model was developed by selecting eight genes from the differential expression genes (DEGs) between two molecular subtypes. Patients with GC were categorized into two risk groups, with the high-risk group exhibiting a poor prognosis, a higher TME level, and increased expression of immune checkpoints. Function enrichment results suggested that genes were enriched in DNA repaired pathway in the low-risk group. Moreover, the Tumor Immune Dysfunction and Exclusion (TIDE) analysis indicated that immunotherapy is likely to be more beneficial for patients in the low-risk group. Drug analysis results revealed that several drugs, including ML210, ML162, dasatinib, idronoxil, and temsirolimus, may contribute to the treatment of GC patients in the high-risk group. Moreover, the risk model genes presented a distinct expression in single-cell levels in the GSE150290 dataset.

CONCLUSION

The two molecular subtypes, with their own individual OS rate, expression patterns, and immune infiltration, lay the foundation for further exploration into the GC molecular mechanism. The eight gene signatures could effectively predict the GC prognosis and can serve as reliable markers for GC patients.

A novel immunohistochemical score predicts the postoperative prognosis of gastric cancer patients

BACKGROUND AND AIM

Immunohistochemistry indicators are increasingly being used to predict the survival prognosis of cancer patients after surgery. This study aimed to combine some markers to establish an immunohistochemical score (MSI-P53-Ki-67[MPK]) and stratify postoperative patients with gastric cancer according to the score.

METHODS

We used 245 patients who underwent surgery at one center as the training cohort and 111 patients from another center as the validation cohort. All patients were treated between January 2012 and June 2018. The training cohort was screened for prognostic factors, and MPK scores were established using univariate and multifactorial COX risk proportional models. Patients were prognostically stratified according to the MPK score after gastrectomy for gastric cancer. Overall survival (OS) and recurrence-free survival (RFS) rates were compared among low-, intermediate-, and high-risk groups using the Kaplan-Meier method, and survival curves were plotted. Finally, the MPK score was validated using the validation cohort.

RESULTS

In the training group, there were statistically significant differences in OS and RFS in the low, medium, and high-risk groups (P < 0.001). Thirty patients were in the high-risk group (12.2%). The median survival times of the three groups were 64.0, 44.0, and 23.0, respectively, and median times to recurrence were 54.0, 35.0, and 16.0 months, respectively. In the validation group, the prognosis in the three risk groups remained significantly different (P < 0.001).

CONCLUSIONS

The novel MPK score could effectively predict the postoperative OS and RFS of gastric cancer patients, risk-stratify postoperative patients, and identify postoperative high-risk patients for refined management.

Methylation related genes are associated with prognosis of patients with head and neck squamous cell carcinoma via altering tumor immune microenvironment

Background/purpose

Analysis of methylomes may enable prognostic stratification in patients with head and neck squamous cell carcinoma (HNSCC). This study aimed to identify methylation-related differentially expressed genes (mrDEGs), and to assess their efficacy in predicting patients' survival, tumor immune microenvironment alterations and immune checkpoints in patients with HNSCC.

Materials and methods

The methylome and transcriptome data of 528 HNSCC and 50 normal samples from TCGA database were used as training cohort. We identified mrDEGs and constituted a risk score model using Kaplan-Meier analysis and multivariate Cox regression. The prognostic efficacy of the risk score was validated in GSE65858 and GSE41613. We determined the enrichment of previously defined biological processes of mrDEGs. We separated the HNSCC patients into low-risk and high-risk groups and compared their immune cell infiltration and immune checkpoints' expressions.

Results

The risk score model was constituted by nine prognostic mrDEGs, including LIMD2, SYCP2, EPHX3, UCLH1, STC2, PRAME, SLC7A4, PLOD2, and ACADL. The risk score was a significant prognostic factor both in training (P < 0.001) and validation dataset (GSE65858: P = 0.008; GSE41613 = 0.015). The prognostic mrDEGs were enriched in multiple immune-associated pathways. Effector immune cells were increased in low-risk patients, including CD8+ T cells, activated CD4+ T cells, and plasma cells, whereas tumor associated M2 macrophages were recruited in the high-risk group. Expressions of immune checkpoints were generally higher in low-risk patients, including CTLA-4, PD-1 and LAG3.

Conclusion

The mrDEGs can stratify HNSCC patients' prognosis, which correlates with alterations in tumor immune infiltrations and immune checkpoints.

Construction and validation of a glycolysis-related lncRNA signature for prognosis prediction in Stomach Adenocarcinoma

Background: Glycolysis is closely related to the occurrence and progression of gastric cancer (GC). Currently, there is no systematic study on using the glycolysis-related long non-coding RNA (lncRNA) as a model for predicting the survival time in patients with GC. Therefore, it was essential to develop a signature for predicting the survival based on glycolysis-related lncRNA in patients with GC. Materials and methods: LncRNA expression profiles, containing 375 stomach adenocarcinoma (STAD) samples, were obtained from The Cancer Genome Atlas (TCGA) database. The co-expression network of lncRNA and glycolysis-related genes was used to identify the glycolysis-related lncRNAs. The Kaplan-Meier survival analysis and univariate Cox regression analysis were used to detect the glycolysis-related lncRNA with prognostic significance. Then, Bayesian Lasso-logistic and multivariate Cox regression analyses were performed to screen the glycolysis-related lncRNA with independent prognostic significance and to develop the risk model. Patients were assigned into the low- and high-risk cohorts according to their risk scores. A nomogram model was constructed based on clinical information and risk scores. Gene Set Enrichment Analysis (GSEA) was performed to visualize the functional and pathway enrichment analyses of the glycolysis-related lncRNA. Finally, the robustness of the results obtained was verified in an internal validation data set. Results: Seven glycolysis-related lncRNAs (AL353804.1, AC010719.1, TNFRSF10A-AS1, AC005586.1, AL355574.1, AC009948.1, and AL161785.1) were obtained to construct a risk model for prognosis prediction in the STAD patients using Lasso regression and multivariate Cox regression analyses. The risk score was identified as an independent prognostic factor for the patients with STAD [HR = 1.315, 95% CI (1.056-1.130), p < 0.001] via multivariate Cox regression analysis. Receiver operating characteristic (ROC) curves were drawn and the area under curve (AUC) values of 1-, 3-, and 5-year overall survival (OS) were calculated to be 0.691, 0.717, and 0.723 respectively. Similar results were obtained in the validation data set. In addition, seven glycolysis-related lncRNAs were significantly enriched in the classical tumor processes and pathways including cell adhesion, positive regulation of vascular endothelial growth factor, leukocyte transendothelial migration, and JAK_STAT signaling pathway. Conclusion: The prognostic prediction model constructed using seven glycolysis-related lncRNA could be used to predict the prognosis in patients with STAD, which might help clinicians in the clinical treatment for STAD.

Pan-Cancer Analyses Reveal Oncogenic and Immunological Role of PLOD2

Some previous studies have shown that PLOD2 has some value in tumorigenesis. However, the broad significance of PLOD2 has not been discussed in depth. This study was aimed at elaborated and summarized the value of PLOD2 in various tumors. First, we integrated GTEx, The Cancer Genome Atlas and Cancer Cell Line Encyclopedia databases to analyze the expression of PLOD2, and found that it was expressed differently in normal tissues and significantly highly expressed in most tumors compared with normal tissues. Second, our analysis revealed that PLOD2 expression was negatively correlated with the prognosis of several tumors. For gastric cancer, the median overall survival time was significantly higher in the PLOD2 low expression group [HR 0.616 (95%CI 0.442–0.858), p = 0.004]. Third, for tumor immunity, PLOD2 was significantly associated with tumor infiltration, including immune infiltrating cells; immune checkpoint expression; immune microenvironment scores (immune score, stromal score and estimate scores); immunotherapy-related scores (tumor mutational burden, microsatellite instability, tumor neoantigen burden); expression of DNA repair genes Mismatch Repairs and methyltransferase; and enrichment analyses identified PLOD2-associated terms and pathways. Lastly, twenty pairs of gastric cancer and adjacent immunohistochemistry showed that PLOD2 was significantly overexpressed in gastric cancer (p < 0.001). Collectively, PLOD2 played a significant role in tumorigenesis and maybe serve as a potential biomarker for diagnosis and prognosis in cancers.

Establishment of a novel glycolysis-related prognostic gene signature for ovarian cancer and its relationships with immune infiltration of the tumor microenvironment

Background

Glycolysis affects tumor growth, invasion, chemotherapy resistance, and the tumor microenvironment. In this study, we aimed to construct a glycolysis-related prognostic model for ovarian cancer and analyze its relationship with the tumor microenvironment’s immune cell infiltration.

Methods

We obtained six glycolysis-related gene sets for gene set enrichment analysis (GSEA). Ovarian cancer data from The Cancer Genome Atlas (TCGA) database and two Gene Expression Omnibus (GEO) datasets were divided into two groups after removing batch effects. We compared the tumor environments' immune components in high-risk and low-risk groups and analyzed the correlation between glycolysis- and immune-related genes. Then, we generated and validated a predictive model for the prognosis of ovarian cancer using the glycolysis-related genes.

Results

Overall, 27/329 glycolytic genes were associated with survival in ovarian cancer, 8 of which showed predictive value. The tumor cell components in the tumor microenvironment did not differ between the high-risk and low-risk groups; however, the immune score differed significantly between groups. In total, 13/24 immune cell types differed between groups, including 10 T cell types and three other immune cell types. Eight glycolysis-related prognostic genes were related to the expression of multiple immune-related genes at varying degrees, suggesting a relationship between glycolysis and immune response.

Conclusions

We identified eight glycolysis-related prognostic genes that effectively predicted survival in ovarian cancer. To a certain extent, the newly identified gene signature was related to the tumor microenvironment, especially immune cell infiltration and immune-related gene expression. These findings provide potential biomarkers and therapeutic targets for ovarian cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03057-0.

A signature of 24 aging‑related gene pairs predict overall survival in gastric cancer

BACKGROUND

Aging is the major risk factor for most human cancers. We aim to develop and validate a reliable aging-related gene pair signature (ARGPs) to predict the prognosis of gastric cancer (GC) patients.

METHODS

The mRNA expression data and clinical information were obtained from two public databases, The Cancer Genome Atlas (TCGA) dataset, and Gene Expression Omnibus (GEO) dataset, respectively. The best prognostic signature was established using Cox regression analysis (univariate and least absolute shrinkage and selection operator). The optimal cut-off value to distinguish between high- and low-risk patients was found by time-dependent receiver operating characteristic (ROC). The prognostic ability of the ARGPS was evaluated by a log-rank test and a Cox proportional hazards regression model.

RESULTS

The 24 ARGPs were constructed for GC prognosis. Using the optimal cut-off value - 0.270, all patients were stratified into high risk and low risk. In both TCGA and GEO cohorts, the results of Kaplan-Meier analysis showed that the high-risk group has a poor prognosis (P < 0.001, P = 0.002, respectively). Then, we conducted a subgroup analysis of age, gender, grade and stage, and reached the same conclusion. After adjusting for a variety of clinical and pathological factors, the results of multivariate COX regression analysis showed that the ARGPs is still an independent prognostic factor of OS (HR, 4.919; 95% CI 3.345-7.235; P < 0.001). In comparing with previous signature, the novel signature was superior, with an area under the receiver operating characteristic curve (AUC) value of 0.845 vs. 0.684 vs. 0.695. The results of immune infiltration analysis showed that the abundance of T cells follicular helper was significantly higher in the low-risk group, while the abundance of monocytes was the opposite. Finally, we identified and incorporated independent prognostic factors and developed a superior nomogram to predict the prognosis of GC patients.

CONCLUSION

Our study has developed a robust prognostic signature that can accurately predict the prognostic outcome of GC patients.

Identifying Novel Cell Glycolysis-Related Gene Signature Predictive of Overall Survival in Gastric Cancer

Background

Gastric cancer (GC) is believed to be one of the most common digestive tract malignant tumors. The prognosis of GC remains poor due to its high malignancy, high incidence of metastasis and relapse, and lack of effective treatment. The constant progress in bioinformatics and molecular biology techniques has given rise to the discovery of biomarkers with clinical value to predict the GC patients' prognosis. However, the use of a single gene biomarker can hardly achieve the satisfactory specificity and sensitivity. Therefore, it is urgent to identify novel genetic markers to forecast the prognosis of patients with GC.

Materials and Methods

In our research, data mining was applied to perform expression profile analysis of mRNAs in the 443 GC patients from The Cancer Genome Atlas (TCGA) cohort. Genes associated with the overall survival (OS) of GC were identified using univariate analysis. The prognostic predictive value of the risk factors was determined using the Kaplan-Meier survival analysis and multivariate analysis. The risk scoring system was built in TCGA dataset and validated in an independent Gene Expression Omnibus (GEO) dataset comprising 300 GC patients. Based on the median of the risk score, GC patients were grouped into high-risk and low-risk groups.

Results

We identified four genes (GMPPA, GPC3, NUP50, and VCAN) that were significantly correlated with GC patients' OS. The high-risk group showed poor prognosis, indicating that the risk score was an effective predictor for the prognosis of GC patients.

Conclusion

The signature consisting of four glycolysis-related genes could be used to forecast the GC patients' prognosis.