Establishment of a Prognostic Signature of Stromal/Immune-Related Genes for Gastric Adenocarcinoma Based on ESTIMATE Algorithm

Exploration of signature based on T cell-related genes in stomach adenocarcinoma by analysis of single cell sequencing data

BACKGROUND

Gastric cancer (GC) is a leading reason for the death of cancer around the world. The immune microenvironment counts a great deal in immunotherapy of advanced tumors, in which T cells exert an indispensable function.

METHODS

Single-cell RNA sequencing data were utilized to characterize the expression profile of T cells, followed by T cell-related genes (TCRGs) to construct signature and measure differences in survival time, enrichment pathways, somatic mutation status, immune status, and immunotherapy between groups.

RESULTS

The complex tumor microenvironment was analyzed by scRNA-seq data of GC patients. We screened for these T cell signature expression genes and the TCRGs-based signature was successfully constructed and relied on the riskscore grouping. In gene set enrichment analysis, it was shown that pro-tumor and suppressive immune pathways were more abundant in the higher risk group. We also found different infiltration of immune cells in two groups, and that the higher risk samples had a poorer response to immunotherapy.

CONCLUSION

Our study established a prognostic model, in which different groups had different prognosis, immune status, and enriched features. These results have provided additional insights into prognostic evaluation and the development of highly potent immunotherapies in GC.

A senescence-related lncRNA signature predicts prognosis and reflects immune landscape in HNSCC

OBJECTIVE

Long noncoding RNAs (lncRNAs) regulate cancer cell senescence in many cancers. However, their specific involvement in head and neck squamous cell carcinoma (HNSCC) remains unclear. We are looking for an ingenious prognostic signature that utilizes senescence-related lncRNAs (SRlncRNAs) to predict prognosis and provide insights into the immune landscape in HNSCC.

MATERIALS AND METHODS

HNSCC clinical and Cellular senescence genes information were collected from The Cancer Genome Atlas and Human Aging Genomic Resources. Then we performed Cox and Lasso regression to locate SRlncRNAs related to the prognosis of HNSCC and built a predictive signature. Further, prognosis assessment, potential mechanisms, and immune status were assessed by Kaplan-Meier analysis, Gene Set Enrichment Analysis (GSEA), and CIBERSORT, respectively.

RESULTS

A prognosis prediction model based on sixteen SRlncRNAs was identified and internally validated. Then, patients with high-risk scores suffered an unfavorable overall survival (All p < 0.05). The risk score, age, and stage were independent prognostic parameters (all p < 0.001). Our model has good predictive ability (The AUC (area under the curves) 1-year = 0.707, AUC3-year = 0.748 and AUC5-year = 0.779). Subsequently, GESA revealed SRlncRNAs regulated immune responses. Patients in the high-risk group had higher tumor mutation burden and Tumor Immune Dysfunction and Exclusion but lower levels of 37 immune checkpoint genes, immune scores, and immune cells like CD8 + T cells, follicular helper T cells, and regulatory T cells.

CONCLUSIONS

A prognostic model based on SRlncRNAs is the potential target for improving immunotherapy outcomes for HNSCC.

Epigenetic and Tumor Microenvironment for Prognosis of Patients with Gastric Cancer

BACKGROUND

Epigenetics studies heritable or inheritable mechanisms that regulate gene expression rather than altering the DNA sequence. However, no research has investigated the link between TME-related genes (TRGs) and epigenetic-related genes (ERGs) in GC.

METHODS

A complete review of genomic data was performed to investigate the relationship between the epigenesis tumor microenvironment (TME) and machine learning algorithms in GC.

RESULTS

Firstly, TME-related differential expression of genes (DEGs) performed non-negative matrix factorization (NMF) clustering analysis and determined two clusters (C1 and C2). Then, Kaplan-Meier curves for overall survival (OS) and progression-free survival (PFS) rates suggested that cluster C1 predicted a poorer prognosis. The Cox-LASSO regression analysis identified eight hub genes (SRMS, MET, OLFML2B, KIF24, CLDN9, RNF43, NETO2, and PRSS21) to build the TRG prognostic model and nine hub genes (TMPO, SLC25A15, SCRG1, ISL1, SOD3, GAD1, LOXL4, AKR1C2, and MAGEA3) to build the ERG prognostic model. Additionally, the signature's area under curve (AUC) values, survival rates, C-index scores, and mean squared error (RMS) curves were evaluated against those of previously published signatures, which revealed that the signature identified in this study performed comparably. Meanwhile, based on the IMvigor210 cohort, a statistically significant difference in OS between immunotherapy and risk scores was observed. It was followed by LASSO regression analysis which identified 17 key DEGs and a support vector machine (SVM) model identified 40 significant DEGs, and based on the Venn diagram, eight co-expression genes (ENPP6, VMP1, LY6E, SHISA6, TMEM158, SYT4, IL11, and KLK8) were discovered.

CONCLUSION

The study identified some hub genes that could be useful in predicting prognosis and management in GC.

Models for Predicting Response to Immunotherapy and Prognosis in Patients with Gastric Cancer: DNA Damage Response Genes

Objective

DNA damage response (DDR) is a complex system that maintains genetic integrity and the stable replication and transmission of genetic material. m6A modifies DDR-related gene expression and affects the balance of DNA damage response in tumor cells. In this study, a risk model based on m6A-modified DDR-related gene was established to evaluate its role in patients with gastric cancer.

Methods

We downloaded 639 DNA damage response genes from the Gene Set Enrichment Analysis (GSEA) database and constructed risk score models using typed differential genes. We used Kaplan-Meier curves and risk curves to verify the clinical relevance of the model, which was then validated with the univariate and multifactorial Cox analysis, ROC, C-index, and nomogram, and finally this model was used to evaluate the correlation of the risk score model with immune microenvironment, microsatellite instability (MSI), tumor mutational burden (TMB), and immune checkpoints.

Results

In this study, 337 samples in The Cancer Genome Atlas (TCGA) database were used as training set to construct a DDR-related gene model, and GSE84437 was used as external data set for verification. We found that the prognosis and immunotherapy effect of gastric cancer patients in the low-risk group were significantly better than those in the high-risk group.

Conclusion

We screened eight DDR-related genes (ZBTB7A, POLQ, CHEK1, NPDC1, RAMP1, AXIN2, SFRP2, and APOD) to establish a risk model, which can predict the prognosis of gastric cancer patients and guide the clinical implementation of immunotherapy.

Molecular subtypes based on cuproptosis regulators and immune infiltration in kidney renal clear cell carcinoma

Copper toxicity involves the destruction of mitochondrial metabolic enzymes, triggering an unusual mechanism of cell death called cuproptosis, which proposes a novel approach using copper toxicity to treat cancer. However, the biological function of cuproptosis has not been fully elucidated in kidney renal clear cell carcinoma (KIRC). Using the expression profile of 13 cuproptosis regulators, we first identified two molecular subtypes related to cuproptosis defined as “hot tumor” and “cold tumor”, having different levels of biological function, clinical prognosis, and immune cell infiltration. We obtained three gene clusters using the differentially expressed genes between the two cuproptosis-related subtypes, which were associated with different molecular activities and clinical characteristics. Next, we developed and validated a cuproptosis prognostic model that included two genes (FDX1 and DBT). The calculated risk score could divide patients into high- and low-risk groups. The high-risk group had a poorer prognosis, lower level of immune infiltration, higher frequency of gene alterations, and greater levels of FDX1 methylation and limited DBT methylation. The risk score was also an independent predictive factor for overall survival in KIRC. The established nomogram calculating the risk score achieved a high predictive ability for the prognosis of individual patients (area under the curve: 0.860). We then identified small molecular inhibitors as potential treatments and analyzed the sensitivity to chemotherapy of the signature genes. Tumor immune dysfunction and exclusion (TIDE) showed that the high-risk group had a higher level of TIDE, exclusion and dysfunction that was lower than the low-risk group, while the microsatellite instability of the high-risk group was significantly lower. The results of two independent immunotherapy datasets indicated that cuproptosis regulators could influence the response and efficacy of immunotherapy in KIRC. Our study provides new insights for individualized and comprehensive therapy of KIRC.

Pan-cancer analysis of PSAP identifies its expression and clinical relevance in gastric cancer

Prosaposin (PSAP) plays a critical role in sphingolipid and cancer metabolism. Reports have shown that PSAP was involved in proliferation, tumorigenesis, and metastasis. However, the expression pattern of PSAP and its prognostic roles in gastric cancer remain elusive. PSAP expression pattern and its prognostic roles in gastric cancer (GC) were explored using data from the TCGA and Kaplan-Meier Plotter. Immunohistochemical staining of GC tissues was performed to validate the prognostic role of PSAP. TISIDB was used to analyze its correlation with immunomodulators. PSAP-associated genes, PDCD1, TGFB1, and CSF1R were used to build a risk model to evaluate immunotherapy outcomes of patients with stomach adenocarcinoma (STAD). Results showed that PSAP was highly expressed in GC. High PSAP expression in GC patients also significantly indicated a poor prognosis. The results of immunohistochemical staining showed that PSAP was an independent prognostic factor in GC patients. Based on three PSAP-associated genes, a risk model that could predict the prognosis and immunotherapy outcome of STAD was bulit. PSAP was an independent prognostic factor in GC. Our results have identified three prognosis-related genes which were useful to evaluate immunotherapy outcomes of STAD patients.

Construction of five cuproptosis-related lncRNA signature for predicting prognosis and immune activity in skin cutaneous melanoma

Cuproptosis is a newly discovered new mechanism of programmed cell death, and its unique pathway to regulate cell death is thought to have a unique role in understanding cancer progression and guiding cancer therapy. However, this regulation has not been studied in SKCM at present. In this study, data on Skin Cutaneous Melanoma (SKCM) patients were downloaded from the TCGA database. We screened the genes related to cuproptosis from the published papers and confirmed the lncRNAs related to them. We applied Univariate/multivariate and LASSO Cox regression algorithms, and finally identified 5 cuproptosis-related lncRNAs for constructing prognosis prediction models (VIM-AS1, AC012443.2, MALINC1, AL354696.2, HSD11B1-AS1). The reliability and validity test of the model indicated that the model could well distinguish the prognosis and survival of SKCM patients. Next, immune microenvironment, immunotherapy analysis, and functional enrichment analysis were also performed. In conclusion, this study is the first analysis based on cuproptosis-related lncRNAs in SKCM and aims to open up new directions for SKCM therapy.

A tumor microenvironment-related risk model for predicting the prognosis and tumor immunity of breast cancer patients

Background

This study aimed to construct a tumor microenvironment (TME)-related risk model to predict the overall survival (OS) of patients with breast cancer.

Methods

Gene expression data from The Cancer Genome Atlas was used as the training set. Differentially expressed gene analysis, prognosis analysis, weighted gene co-expression network analysis, Least Absolute Shrinkage and Selection Operator regression analysis, and Wald stepwise Cox regression were performed to screen for the TME-related risk model. Three Gene Expression Omnibus databases were used to validate the predictive efficiency of the prognostic model. The TME-risk-related biological function was investigated using the gene set enrichment analysis (GSEA) method. Tumor immune and mutation signatures were analyzed between low- and high-TME-risk groups. The patients’ response to chemotherapy and immunotherapy were evaluated by the tumor immune dysfunction and exclusion (TIDE) score and immunophenscore (IPS).

Results

Five TME-related genes were screened for constructing a prognostic signature. Higher TME risk scores were significantly associated with worse clinical outcomes in the training set and the validation set. Correlation and stratification analyses also confirmed the predictive efficiency of the TME risk model in different subtypes and stages of breast cancer. Furthermore, immune checkpoint expression and immune cell infiltration were found to be upregulated in the low-TME-risk group. Biological processes related to immune response functions were proved to be enriched in the low-TME-risk group through GSEA analysis. Tumor mutation analysis and TIDE and IPS analyses showed that the high-TME-risk group had more tumor mutation burden and responded better to immunotherapy.

Conclusion

The novel and robust TME-related risk model had a strong implication for breast cancer patients in OS, immune response, and therapeutic efficiency.

AFF3 is a novel prognostic biomarker and a potential target for immunotherapy in gastric cancer

Background

Gastric cancer (GC) is one of the most common cancers worldwide with a poor prognosis. The tumor microenvironment (TME) serves a pivotal role in affecting the prognosis and efficacy of immunotherapy. Given the poor prognosis of GC patients and the limitation of immunotherapy, we urged to identify new prognostic and immunotherapeutic biomarkers.

Methods

The transcriptome data were downloaded from the TCGA, GEO, and GEPIA databases, and performed differential analysis of AFF3 in tumor samples and normal samples. The UALCAN, Kaplan–Meier plotter and GEPIA databases were employed to assess the correlation of AFF3 with clinicopathological characteristics and prognosis. The potential mechanism of AFF3 was explored by the GO and KEGG enrichment. The potential role of AFF3 on tumor‐infiltrating immune cells (TIICs) was explored by TIMER2.0 and TISIDB. TIMER2.0 and SangerBox3.0 databases were, respectively, used to determine the correlation of AFF3 with immune checkpoint (ICs), tumor mutational burden (TMB), and microsatellite instability (MSI) in GC.

Results

We found significant downregulation of AFF3 in GC tissues as compared with normal tissues. However, GC patients having a higher expression of AFF3 were found to have worse clinicopathological characteristics and prognosis. Moreover, the GO enrichment analysis illustrated that AFF3 might regulate the immune cells in the TME. In addition, the AFF3 was positively correlated with TIICs, ICs, TMB, and MSI.

Conclusion

Here, we conclude that AFF3 may be a promising potential marker for the diagnosis and prognosis of GC patients, and may influence response to ICIs by affecting TIICs and ICs expression in the TME.

Immune-Related Genes for Predicting Future Kidney Graft Loss: A Study Based on GEO Database

Objective

We aimed to identify feature immune-related genes that correlated with graft rejection and to develop a prognostic model based on immune-related genes in kidney transplantation.

Methods

Gene expression profiles were obtained from the GEO database. The GSE36059 dataset was used as a discovery cohort. Then, differential expression analysis and a machine learning method were performed to select feature immune-related genes. After that, univariate and multivariate Cox regression analyses were used to identify prognosis-related genes. A novel Riskscore model was built based on the results of multivariate regression. The levels of these feature genes were also confirmed in an independent single-cell dataset and other GEO datasets.

Results

15 immune-related genes were expressed differently between non-rejection and rejection kidney allografts. Those differentially expressed immune-related genes (DE-IRGs) were mainly associated with immune-related biological processes and pathways. Subsequently, a 5-immune-gene signature was constructed and showed favorable predictive results in the GSE21374 dataset. Recipients were divided into the high-risk and low-risk groups according to the median value of RiskScore. The GO and KEGG analysis indicated that the differentially expressed genes (DEGs) between high-risk and low-risk groups were mainly involved in inflammatory pathways, chemokine-related pathways, and rejection-related pathways. Immune infiltration analysis demonstrated that RiskScore was potentially related to immune infiltration. Kaplan-Meier survival analysis suggested that recipients in the high-risk group had poor graft survival. AUC values of 1- and 3-year graft survival were 0.804 and 0.793, respectively.

Conclusion

Our data suggest that this immune-related prognostic model had good sensitivity and specificity in predicting the 1- and 3-year kidney graft survival and might act as a useful tool for predicting kidney graft loss.