Comprehensive analysis of tumor mutation burden and immune microenvironment in gastric cancer

FUT11 expression in gastric cancer: its prognostic significance and role in immune regulation

BACKGROUND

Gastric cancer (GC) is a malignant digestive tract tumor with a high recurrence rate and poor prognosis. Fucosylation is important in tumor glycosylation, in which the key enzyme is fucosyltransferase (FUT). FUT11 is a member of the fucosyltransferase family and has been closely associated with the development of multiple cancers. However, the specific relationship between FUT11 and GC prognosis and its molecular mechanism has not been fully studied. This study explored FUT11 expression, clinical correlation, and its role in GC occurrence and development to deepen understanding of its function.

METHODS

FUT11 expression in 33 cancers was preliminarily analyzed using the Tumor Immunoassay Resource (TIMER2.0) database. FUT11 expression in GC was evaluated using The Cancer Genome Atlas stomach adenocarcinoma (TCGA-STAD) and Gene Expression Profiling Interactive Analysis (GEPIA2) data and verified using the Gene Expression Omnibus (GEO) GSE65801 dataset. Furthermore, we studied the survival prognosis of FUT11 in GC and analyzed its effect on the survival rate of patients with GC using the KM-plotter. We also performed COX regression analysis on TCGA GC clinical data and analyzed FUT11 expression in the pathway using the STRING and LinkedOmics databases. Moreover, the relationship between FUT11 and GC immune infiltration level was examined, and the Kaplan-Meier survival analysis diagram was constructed. The FUT11 genetic variation information was retrieved using cBioPortal, and its drug sensitivity was analyzed using CellMiner. Finally, differential FUT11 expression in GC tissues was verified using immunohistochemistry.

RESULTS

The data mining and analysis demonstrated that FUT11 expression was abnormally elevated in GC tissues and correlated with poor patient prognosis. The FUT11 expression level was an independent prognostic factor for GC. The difference in FUT11 expression level resulted in different degrees of immune cell infiltration in the patients with GC, which might regulate the tumor microenvironment. FUT11 affected GC development by participating in cancer pathways such as PI3K-AKT, neuroactive ligand-receptor, and MAPK. Immunohistochemical staining revealed that FUT11 was highly expressed in GC.

CONCLUSIONS

This study revealed that FUT11 expression is significantly increased in GC tissues. This increase is associated with poor prognosis and might affect immune regulation. FUT11 might have immunological and targeted therapeutic value, providing a new approach to GC treatment.

Unraveling Emerging Anal Cancer Clinical Biomarkers from Current Immuno-Oncogenomics Advances

Anal squamous cell carcinoma (ASCC) is a rare gastrointestinal malignancy associated with high-risk human papillomavirus (HPV) and is currently one of the fastest-growing causes of cancer incidence and mortality in developed countries. Although next-generation sequencing technologies (NGS) have revolutionized cancer and immuno-genomic research in various tumor types, a limited amount of clinical research has been developed to investigate the expression and the functional characterization of genomic data in ASCC. Herein, we comprehensively assess recent advancements in "omics" research, including a systematic analysis of genome-based studies, aiming to identify the most relevant ASCC cancer driver gene expressions and their associated signaling pathways. We also highlight the most significant biomarkers associated with anal cancer progression, gene expression of potential diagnostic biomarkers, expression of therapeutic drug targets, and emerging treatment opportunities. This review stresses the urgent need for developing target-specific therapies in ASCC. By illuminating the molecular characteristics and drug-target expression in ASCC, this study aims to provide insights for the development of precision medicine in anal cancer.

MRI radiomics for predicting poor disease-free survival in muscle invasive bladder cancer: the results of the retrospective cohort study

OBJECTIVES

To develop an MRI radiomic nomogram capable of identifying muscle invasive bladder cancer (MIBC) patients with high-risk molecular characteristics related to poor 2-year disease-free survival (DFS).

METHODS

We performed a retrospective analysis of DNA sequencing data, prognostic information, and radiomics features from 91 MIBC patients at stages T2-T4aN0M0 without history of immunotherapy. To identify risk stratification, we employed Cox regression based on TP53 mutation status and tumor mutational burden (TMB) level. Radiomics signatures were selected using the least absolute shrinkage and selection operator (LASSO) to construct a nomogram based on logistic regression for predicting the stratification in the training cohort. The predictive performance of the nomogram was assessed in the testing cohort using receiver operator curve (ROC), Hosmer-Lemeshow (HL) test, clinical impact curve (CIC), and decision curve analysis (DCA).

RESULTS

Among 91 participants, the mean TMB value was 3.3 mut/Mb, with 60 participants having TP53 mutations. Patients with TP53 mutations and a below-average TMB value were identified as high risk and had a significantly poor 2-year DFS (hazard ratio = 4.36, 95% CI 1.82-10.44, P < 0.001). LASSO identified five radiomics signatures that correlated with the risk stratification. In the testing cohort, the nomogram achieved an area under the ROC curve of 0.909 (95% CI 0.789-0.991) and an accuracy of 0.889 (95% CI 0.708-0.977).

CONCLUSION

The molecular risk stratification based on TP53 mutation status combined with TMB level is strongly associated with DFS in MIBC. Radiomics signatures can effectively predict this stratification and provide valuable information to clinical decision-making.

Attention mechanism based multi-sequence MRI fusion improves prediction of response to neoadjuvant chemoradiotherapy in locally advanced rectal cancer

BACKGROUND

Accurate prediction of response to neoadjuvant chemoradiotherapy (nCRT) is very important for treatment plan decision in locally advanced rectal cancer (LARC). The aim of this study was to investigate whether self-attention mechanism based multi-sequence fusion strategy applied to multiparametric magnetic resonance imaging (MRI) based deep learning or hand-crafted radiomics model construction can improve prediction of response to nCRT in LARC.

METHODS

This retrospective analysis enrolled 422 consecutive patients with LARC who received nCRT before surgery at two hospitals. All patients underwent multiparametric MRI scans with three imaging sequences. Tumor regression grade (TRG) was used to assess the response of nCRT based on the resected specimen. Patients were separated into 2 groups: poor responders (TRG 2, 3) versus good responders (TRG 0, 1). A self-attention mechanism, namely channel attention, was applied to fuse the three sequence information for deep learning and radiomics models construction. For comparison, other two models without channel attention were also constructed. All models were developed in the same hospital and validated in the other hospital.

RESULTS

The deep learning model with channel attention mechanism achieved area under the curves (AUCs) of 0.898 in the internal validation cohort and 0.873 in the external validation cohort, which was the best performed model in all cohorts. More importantly, both the deep learning and radiomics model that applied channel attention mechanism performed better than those without channel attention mechanism.

CONCLUSIONS

The self-attention mechanism based multi-sequence fusion strategy can improve prediction of response to nCRT in LARC.

Genomic and transcriptomic profiling indicates the prognosis significance of mutational signature for TMB-high subtype in Chinese patients with gastric cancer

INTRODUCTION

Gastric cancer (GC)is the third leading cause of cancer-related deaths in China and immunotherapy emerging as a revolutionary treatment for GC recently. Tumor mutational burden (TMB) is a predictive biomarker of immunotherapy in multiple cancers. However, the prognostic significance and subtype of TMB in GC is not fully understood.

OBJECTIVES

This study aims to evaluate the prognostic value of TMB in Chinese GC and further classify TMB-high GC (GCTMB-H) patients combing with mutational signatures.

METHODS

Genomic profiling of 435 cancer-gene panel was performed using 206 GC samples from Chinese people. Actionable genetic alterations were compared across all the samples to generate actionable subtyping. The prognostic value of TMB in Chinese GC was evaluated. Mutational signatures were analyzed on TMB-H subtype to stratify the prognosis of TMB. Transcriptomic analysis was applied to compare the distributed immunocytes among different subtypes.

RESULTS

88.3% (182/206) of GC samples had at least one mutation, while 45.1% (93/206) had at least one somatic copy number alteration (SCNA). 29.6% (61/206) of GC samples were TMB-H, including 13 MSI-H and 48 MSS tumors. According to distinct genetic alteration profiles of 69 actionable genes, we classified GC samples into eight molecular subtypes, including TMB-H, ERBB2 amplified, ATM mutated, BRCA2 mutated, CDKN2A/B deleted, PI3KCA mutated, KRAS mutated, and less-mutated subtype. TMB-H subtype presented a remarkable immune-activated phenotype as determined by transcriptomic analysis that was further validated in the TCGA GC cohort. GCTMB-H patients exhibited significantly better survival (P = 0.047). But Signature 1-high GCTMB-H patients had relatively worse prognosis (P = 0.0209, HR = 2.571) than Signature 1-low GCTMB-H patients from Chinese GC cohort, also validated in TCGA GC cohort, presenting highly activated carbohydrate, fatty acid or lipid metabolism.

CONCLUSION

The Signature 1-high GCTMB-H could be a marker of poor prognosis and is associated with metabolism disorder.

Potential role of LPAR5 gene in prognosis and immunity of thyroid papillary carcinoma and pan-cancer

Papillary carcinomas account for the largest proportion of thyroid cancers, with papillary thyroid carcinoma (PTC) being prone to early lymph node metastasis. Some studies have confirmed that LPAR5 can promote the progression of PTC, but immune-related analyses of LPAR5 and PTC have not been widely discussed. This study aimed to determine the role of LPAR5 in PTC prognosis and immunity. We will further explore the role of LPAR5 in 33 different tumor types. Regarding PTC, we analyzed the effect of LPAR5 expression on overall survival (OS). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed. Immune-related analyses of immune checkpoints (ICPs) and immune cell infiltration were also performed. For pan-cancer, R packages were used to analyze prognosis, tumor mutational burden (TMB), microsatellite instability (MSI), and immune cell infiltration. Analysis of tumor microenvironment (TME) and ICPs was performed using Sangerbox ( http://vip.sangerbox.com/home.html ). The TISIDB database ( http://cis.hku.hk/TISIDB/index.php ) was used to identify immune and molecular subtypes. LPAR5 expression is associated with PTC prognosis and immunity as well as various human tumors. LPAR5 may be a potential biomarker for multiple malignancies and may provide a new target for cancer immunotherapy.

Prognostic prediction based on histopathologic features of tumor microenvironment in colorectal cancer

Purpose

To automatically quantify colorectal tumor microenvironment (TME) in hematoxylin and eosin stained whole slide images (WSIs), and to develop a TME signature for prognostic prediction in colorectal cancer (CRC).

Methods

A deep learning model based on VGG19 architecture and transfer learning strategy was trained to recognize nine different tissue types in whole slide images of patients with CRC. Seven of the nine tissue types were defined as TME components besides background and debris. Then 13 TME features were calculated based on the areas of TME components. A total of 562 patients with gene expression data, survival information and WSIs were collected from The Cancer Genome Atlas project for further analysis. A TME signature for prognostic prediction was developed and validated using Cox regression method. A prognostic prediction model combined the TME signature and clinical variables was also established. At last, gene-set enrichment analysis was performed to identify the significant TME signature associated pathways by querying Gene Ontology database and Kyoto Encyclopedia of Genes and Genomes database.

Results

The deep learning model achieved an accuracy of 94.2% for tissue type recognition. The developed TME signature was found significantly associated to progression-free survival. The clinical combined model achieved a concordance index of 0.714. Gene-set enrichment analysis revealed the TME signature associated genes were enriched in neuroactive ligand-receptor interaction pathway.

Conclusion

The TME signature was proved to be a prognostic factor and the associated biologic pathways would be beneficial to a better understanding of TME in CRC patients.

PIK3CA somatic mutations as potential biomarker for immunotherapy in elder or TP53 mutated gastric cancer patients

Immune checkpoint inhibitors (ICIs) improve overall survival in patients with advanced gastric cancer (GC). However, the molecular characterization of GC in ICIs responders is unclear. A total of 288 advanced GC patients were included in this study. Next-generation sequencing analysis was performed on tumor tissue and paired blood to screen for somatic mutants in 639 tumor-associated genes. We demonstrated that ARID1A, HER2/3/4, KMT2C/2D, LRP1B, PIK3CA, SPTA1, and TP53 mutations were significantly correlated with high tumor mutation burden (TMB) score, as well as HER2 amplification. For HER2 and PIK3CA mutations types, this relationship was statistically significant with age and TP53 mutation status, which was also found in the CDH1 gene. These results were confirmed by sequencing 873 GC cases in the cBioPortal database. PIK3CA mutations appear to be associated with longer survival in elderly population and TP53 mutant subtypes. For the first time, we found that GC patients ≥60 years old or with TP53 mutated type and PIK3CA mutations were associated with higher TMB and better ICI response. Building upon the age and TP53 mutation status, this study suggested a novel stratification approach to GC patients and explored the correlations between genetic somatic mutations and TMB score.

Prognostic value of nicotinamide adenine dinucleotide (NAD+) metabolic genes in patients with stomach adenocarcinoma based on bioinformatics analysis

Background

Because stomach adenocarcinoma (STAD) has a poor prognosis, it is necessary to explore new prognostic genes to stratify patients to guide existing individualized treatments.

Methods

Survival and clinical information, RNA-seq data and mutation data of STAD were acquired from The Cancer Genome Atlas (TCGA) database. Fifty-one nicotinamide adenine dinucleotide (NAD⁺) metabolism-related genes (NMRGs) were obtained from the Kyoto Encyclopedia of Genes and Genomes (KEGG) and Reactome databases. Differentially expressed NMRGs (DE-NMRGs) between STAD and normal samples were screened, and consistent clustering analysis of STAD patients was performed based on the DE-NMRGs. Survival analysis, Gene Set Enrichment Analysis (GSEA), mutation frequency analysis, immune microenvironment analysis and drug prediction were performed among different clusters. Additionally, the differentially expressed genes (DEGs) among different clusters were selected, and the intersections of DEGs and DE-NMRGs were selected as the prognostic genes. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was performed on a human gastric mucosa epithelial cell line and cancer cell line to verify the expression of the prognostic genes.

Results

A total of 27 DE-NMRGs and two clusters were selected. There was a difference in survival between clusters 1 and 2. Furthermore, 18 DE-NMRGs were significantly different between clusters 1 and 2. The different Gene Ontology (GO) biological processes and KEGG pathways between clusters 1 and 2 were mainly enriched in cyclic nucleotide mediated signaling, synaptic signaling and hedgehog signaling pathway, etc. The somatic mutation frequencies were different between the two clusters, and TTN was the highest mutated gene in the patients of the clusters 1 and 2. Additionally, eight immune cells, immune score, stromal score, and estimate score were different between clusters 1 and 2. The patients in cluster 2 were sensitive to CTLA4 inhibitor treatment. Furthermore, the top five drugs (AP.24534, BX.795, Midostaurin, WO2009093927 and CCT007093) were significantly higher in cluster 1 than in cluster 2. Finally, three genes (AOX1, NNMT and PTGIS) were acquired as prognostic, and their expressions were consistent with the results of bioinformatics analysis.

Conclusions

Three prognostic genes related to NAD⁺ metabolism in STAD were screened out, which provides a theoretical basis and reference value for future treatment and prognosis of STAD.

A novel necroptosis-related gene index for predicting prognosis and a cold tumor immune microenvironment in stomach adenocarcinoma

Background

Gastric cancer (GC) represents a major global clinical problem with very limited therapeutic options and poor prognosis. Necroptosis, a recently discovered inflammatory form of cell death, has been implicated in carcinogenesis and inducing necroptosis has also been considered as a therapeutic strategy.

Objective

We aim to evaluate the role of this pathway in gastric cancer development, prognosis and immune aspects of its tumor microenvironment.

Methods and results

In this study, we evaluated the gene expression of 55 necroptosis-related genes (NRGs) that were identified via carrying out a comprehensive review of the medical literature. Necroptosis pathway was deregulated in gastric cancer samples (n=375) as compared to adjacent normal tissues (n=32) obtained from the “The Cancer Genome Atlas (TCGA)”. Based on the expression of these NRGs, two molecular subtypes were obtained through consensus clustering that also showed significant prognostic difference. Differentially expressed genes between these two clusters were retrieved and subjected to prognostic evaluation via univariate cox regression analysis and LASSO cox regression analysis. A 13-gene risk signature, termed as necroptosis-related genes prognostic index (NRGPI), was constructed that comprehensively differentiated the gastric cancer patients into high- and low-risk subgroups. The prognostic significance of NRGPI was validated in the GEO cohort (GSE84437: n=408). The NRGPI-high subgroup was characterized by upregulation of 10 genes (CYTL1, PLCL1, CGB5, CNTN1, GRP, APOD, CST6, GPX3, FCN1, SERPINE1) and downregulation of 3 genes (EFNA3, E2F2, SOX14). Further dissection of these two risk groups by differential gene expression analysis indicated involvement of signaling pathways associated with cancer cell progression and immune suppression such as WNT and TGF-β signaling pathway. Para-inflammation and type-II interferon pathways were activated in NRGPI-high patients with an increased infiltration of Tregs and M2 macrophage indicating an exhausted immune phenotype of the tumor microenvironment. These molecular characteristics were mainly driven by the eight NRGPI oncogenes (CYTL1, PLCL1, CNTN1, GRP, APOD, GPX3, FCN1, SERPINE1) as validated in the gastric cancer cell lines and clinical samples. NRGPI-high patients showed sensitivity to a number of targeted agents, in particular, the tyrosine kinase inhibitors.

Conclusions

Necroptosis appears to play a critical role in the development of gastric cancer, prognosis and shaping of its tumor immune microenvironment. NRGPI can be used as a promising prognostic biomarker to identify gastric cancer patients with a cold tumor immune microenvironment and poor prognosis who may response to selected molecular targeted therapy.

A bioinformatics framework to identify the biomarkers and potential drugs for the treatment of colorectal cancer

Colorectal cancer (CRC), a common malignant tumor, is one of the main causes of death in cancer patients in the world. Therefore, it is critical to understand the molecular mechanism of CRC and identify its diagnostic and prognostic biomarkers. The purpose of this study is to reveal the genes involved in the development of CRC and to predict drug candidates that may help treat CRC through bioinformatics analyses. Two independent CRC gene expression datasets including The Cancer Genome Atlas (TCGA) database and GSE104836 were used in this study. Differentially expressed genes (DEGs) were analyzed separately on the two datasets, and intersected for further analyses. 249 drug candidates for CRC were identified according to the intersected DEGs and the Crowd Extracted Expression of Differential Signatures (CREEDS) database. In addition, hub genes were analyzed using Cytoscape according to the DEGs, and survival analysis results showed that one of the hub genes, TIMP1 was related to the prognosis of CRC patients. Thus, we further focused on drugs that could reverse the expression level of TIMP1. Eight potential drugs with documentary evidence and two new drugs that could reverse the expression of TIMP1 were found among the 249 drugs. In conclusion, we successfully identified potential biomarkers for CRC and achieved drug repurposing using bioinformatics methods. Further exploration is needed to understand the molecular mechanisms of these identified genes and drugs/small molecules in the occurrence, development and treatment of CRC.

Mechanism investigation and experiment validation of capsaicin on uterine corpus endometrial carcinoma

Background: Using bioinformatics analysis and experimental operations, we intend to analyze the potential mechanism of action of capsaicin target gene GATA1 in the treatment of uterine corpus endometrial carcinoma (UCEC) and develop a prognostic model for the disease to validate this model.

Methods: By obtaining capsaicin and UCEC-related DR-DEGs, the prognosis-related gene GATA1 was screened. The survival analysis was conducted via establishing high and low expression groups of GATA1. Whether the GATA1 could be an independent prognostic factor for UCEC, it was also validated. The therapeutic mechanism of capsaicin-related genes in UCEC was further investigated using enrichment analysis and immune methods as well as in combination with single-cell sequencing data. Finally, it was validated by cell experiments.

Results: GATA1, a high-risk gene associated with prognosis, was obtained by screening. Kaplan-Meier analysis showed that the survival of the high expression group was lower than that of low expression group. ROC curves showed that the prediction effect of the model was good and stable (1-year area under curve (AUC): 0.601; 2-years AUC: 0.575; 3-years AUC: 0.610). Independent prognosis analysis showed that the GATA1 can serve as an independent prognostic factor for UCEC. Enrichment analysis showed that “neuroactive Ligand - receptor interaction and TYPE I DIABETES MELLITUS” had a significant enrichment effect. Single-cell sequencing showed that the GATA1 was significantly expressed in mast cells. Cell experiments showed that the capsaicin significantly reduced the UCEC cell activity and migration ability, as well as inhibited the expression of GATA1.

Conclusion: This study suggests that the capsaicin has potential value and application prospect in the treatment of UCEC. It provides new genetic markers for the prognosis of UCEC patients.

Comprehensive analysis of tumor mutation burden and immune microenvironment in prostate cancer

PURPOSE

Prostate adenocarcinoma (PRAD) is a high incidence of malignant tumor of the urinary system and the second most common male cancer in the world. Immune checkpoint inhibitor (ICIS) therapy is becoming a new hope for cancer treatment.

METHODS

To realize the possibility of PRAD patients benefiting from ICIS treatment, we analyzed the mutation spectrum of all PRAD patients, calculated the TMB of each PRAD patient, and divided the patients into high TMB group and low TMB group. Differentially expressed genes (DEGs) between the two groups were identified and path analysis was carried out. The immune cell infiltration of each PRAD patient was evaluated and survival analysis was performed to explore the effect of immune cell infiltration on the prognosis.

RESULTS

We found that high TMB was associated with better survival outcomes, with higher TMB scores in young patients, T2 and N0 patients. 28 hub genes were screened by the overlap between 229 DEGs and immune-related genes. T cells CD8 and CD4 memory activated in the high TMB group were higher than those in the low TMB group, while Mast cells resting in the low TMB group were higher than that in the high TMB group. High neutrophil infiltration is associated with poor prognosis in patients with PRAD. Finally, from the immune genes used to construct the prognostic risk model of TMB, it is found that CHP2 and NRG1 are independent prognostic factors of PRAD.

CONCLUSIONS

This study provides new insights into the immune microenvironment and potential immunotherapy of PRAD.

Characterization of Tumor Mutation Burden-Based Gene Signature and Molecular Subtypes to Assist Precision Treatment in Gastric Cancer

Objective

Tumor mutation burden (TMB) represents a useful biomarker for predicting survival outcomes and immunotherapy response. Here, we aimed to conduct TMB-based gene signature and molecular subtypes in gastric cancer.

Methods

Based on differentially expressed genes (DEGs) between high- and low-TMB groups in TCGA, a LASSO model was developed for predicting overall survival (OS) and disease-free survival (DFS). The predictive performance was externally verified in the GSE84437 dataset. Molecular subtypes were conducted via consensus clustering approach based on TMB-related DEGs. The immune microenvironment was estimated by ESTIMATE and ssGSEA algorithms.

Results

High-TMB patients had prolonged survival duration. TMB-related DEGs were distinctly enriched in cancer- (MAPK, P53, PI3K-Akt, and Wnt pathways) and immune-related pathways (T cell selection and differentiation). The TMB-based gene model was developed (including MATN3, UPK1B, GPX3, and RGS2), and high-risk score was predictive of poor prognosis and recurrence. ROC and multivariate analyses revealed the well predictive performance, which was confirmed in the external cohort. Furthermore, we established the nomogram containing the risk score, age, and stage for personalized prediction of OS and DFS. High-risk score was characterized by high stromal score, increased immune checkpoints, immune cell infiltrations, and enhanced sensitivity to gefitinib, vinorelbine, and gemcitabine. Three TMB-based molecular subtypes were conducted, characterized by distinct prognosis, immune microenvironment, and drug sensitivity.

Conclusion

Collectively, we established a prognostic signature and three distinct molecular subtypes based on TMB features for gastric cancer, which might be beneficial for prognostic prediction and clinical decision-making.

Identification of three immune subtypes characterized by distinct tumor immune microenvironment and therapeutic response in stomach adenocarcinoma

BACKGROUND

In primary stomach adenocarcinoma (STAD), the tumor immune microenvironment (TIME) is important for cancer occurrence and progression; however, its clinical significance remains unclear. This study investigated the association between patient survival, TIME, and therapeutic response to STAD.

METHODS

Gene expression profiles of STAD cases were collected from the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus. Molecular subtypes were explored with consistent clustering methods according to 119 immune signatures and the infiltrating scores of 22 immune cells using the Multi-Omics Immuno-Oncology Biological Research algorithm. We determined IFNγ scores and immune cytolytic activity (CYT) scores on the basis of corresponding gene signatures via single-sample Gene Set Enrichment Analysis. Comparisons of survival, TIME, 10 immunity-related oncogenic pathways, immune checkpoint expression, and therapeutic response were conducted among the three subtypes. We further applied linear discriminant analysis to construct a characteristic index to classify the subtypes, and the Pearson correlation coefficient for the relationship between the index and immune checkpoint genes. Weighted Correlation Network Analysis (WGCNA) was used to mine the associated modules and specific genes.

RESULTS

We collected gene expression profiles from 352 STAD cases in the TCGA database, 300 in GSE62254, and 344 in GSE84437. Three STAD subtypes (IS1-IS3) were established according to the TIME signatures. The IS3 subtype had the highest immune score and the best prognosis, as well as markedly increased immune T-cell CYT, Th1/IFNγ scores, and immune checkpoint gene expression, compared to the other two subtypes. It was highly similar to the PD-1 response group in the previous study samples of GSE91061. The established TIME classification index performed well in classifying subtypes and was directly proportional to immune checkpoint-related gene expression levels. WGCNA explored 6 modules and 14 genes, namely DYSF, MAN1C1, HTRA3, EMCN, RFLNB, KANK3, MAGEH1, CD93, PCAT19, FUT11, BMP1, FOSB, DCHS1, and TCF3, which were associated with the established TIME classification index and STAD patient prognosis.

CONCLUSION

TIME phenotypes of STAD patients could be divided into three different molecular subtypes, which displayed different prognoses, immune features, and therapeutic responses. Our results shed new light on predicting patient outcomes and the discovery of new anti-STAD therapeutic strategies according to the TIME.

Integrated weighted gene coexpression network analysis identifies Frizzled 2 (FZD2) as a key gene in invasive malignant pleomorphic adenoma

Background

Invasive malignant pleomorphic adenoma (IMPA) is a highly malignant neoplasm of the oral salivary glands with a poor prognosis and a considerable risk of recurrence. Many disease-causing genes of IMPA have been identified in recent decades (e.g., P53, PCNA and HMGA2), but many of these genes remain to be explored. Weighted gene coexpression network analysis (WGCNA) is a newly emerged algorithm that can cluster genes and form modules based on similar gene expression patterns. This study constructed a gene coexpression network of IMPA via WGCNA and then carried out multifaceted analysis to identify novel disease-causing genes.

Methods

RNA sequencing (RNA-seq) was performed for 10 pairs of IMPA and normal tissues to acquire the gene expression profiles. Differentially expressed genes (DEGs) were screened out with the cutoff criteria of |log₂ Fold change (FC)|> 1 and adjusted p value  < 0.05. Then, WGCNA was applied to systematically identify the hidden diagnostic hub genes of IMPA.

Results

In this research, a total of 1970 DEGs were screened out in IMPA tissues, including 1056 upregulated DEGs and 914 downregulated DEGs. Functional enrichment analysis was performed for identified DEGs and revealed an enrichment of tumor-associated GO terms and KEGG pathways. We used WGCNA to identify gene module most relevant with the histological grade of IMPA. The gene FZD2 was then recognized as the hub gene of the selected module with the highest module membership (MM) value and intramodule connectivity in protein–protein interaction (PPI) network. According to immunohistochemistry (IHC) staining, the expression level of FZD2 was higher in low-grade IMPA than in high-grade IMPA.

Conclusion

FZD2 shows an expression dynamic that is negatively correlated with the clinical malignancy of IMPA and it plays a central role in the transcription network of IMPA. Thus, FZD2 serves as a promising histological indicator for the precise prediction of IMPA histological stages.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-021-03204-7.

An age stratified analysis of the biomarkers in patients with colorectal cancer

Colorectal cancer (CRC), a common malignant tumor of the digestive tract, has a high incidence and mortality rate. Several recent studies have found that aging is associated with the increasing risk of cancer. Nevertheless, the expression status and function of age-related genes in CRC is still not well understood. In the study, we comprehensively analyzed the gene expression data of CRC patients from The Cancer Genome Atlas (TCGA) database. Age-related differential expression genes (age-related DEGs) in tumor tissues compared with normal tissues of CRC were further identified. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses of age-related DEGs were performed by clusterProfiler of R. Afterwards, we used the STRING database to map the protein-protein interaction network of DEGs. We constructed prognostic model through univariate and multivariate COX regression analyses, and further evaluated their predictive power. The prognostic gene signature-related functional pathways were explored by gene set enrichment analysis (GSEA). The weighted gene co-expression network analysis (WGCNA) was used to identify key module associated with two prognostic gene signatures. Finally, we used the Metascape to perform functional enrichment analysis of genes in the key module. A total of 279 age-related DEGs were identified from the TCGA database. GO and KEGG enrichment analysis showed that the age-related DEGs were enriched in the Modulation of chemical synaptic transmission and Neuroactive ligand-receptor interaction. Moreover, we established a novel age-related gene signature (DLX2 and PCOLCE2) for overall survival in CRC, which was further predicted in both the training and validation sets. The results of GSEA demonstrated that numerous disease-related pathways were enriched in the high-risk group. We identified 43 genes related to the DLX2 and PCOLCE2 by the WGCNA co-expression network. We also found that these 43 genes were enriched in the cancer-related pathways. To sum up, the study identified an age-related gene signature for predicting the prognosis of CRC patients, which is conducive to the identification of novel prognostic molecular markers.

Identification of a Gene Prognostic Model of Gastric Cancer Based on Analysis of Tumor Mutation Burden

Introduction: Gastric cancer is one of the most common cancers. Although some progress has been made in the treatment of gastric cancer with the improvement of surgical methods and the application of immunotherapy, the prognosis of gastric cancer patients is still unsatisfactory. In recent years, there has been increasing evidence that tumor mutational load (TMB) is strongly associated with survival outcomes and response to immunotherapy. Given the variable response of patients to immunotherapy, it is important to investigate clinical significance of TMB and explore appropriate biomarkers of prognosis in patients with gastric cancer (GC).

Material and Methods: All data of patients with gastric cancer were obtained from the database of The Cancer Genome Atlas (TCGA). Samples were divided into two groups based on median TMB. Differently expressed genes (DEGs) between the high- and low-TMB groups were identified and further analyzed. We identified TMB-related genes using Lasso, univariate and multivariate Cox regression analysis and validated the survival result of 11 hub genes using Kaplan-Meier Plotter. In addition, “CIBERSORT” package was utilized to estimate the immune infiltration.

Results: Single nucleotide polymorphism (SNP), C > T transition were the most common variant type and single nucleotide variant (SNV), respectively. Patients in the high-TMB group had better survival outcomes than those in the low-TMB group. Besides, eleven TMB-related DEGs were utilized to construct a prognostic model that could be an independent risk factor to predict the prognosis of patients with GC. What’s more, the infiltration levels of CD4⁺ memory-activated T cells, M0 and M1 macrophages were significantly increased in the high-TMB group compared with the low-TMB group.

Conclusions: Herein, we found that patients with high TMB had better survival outcomes in GC. In addition, higher TMB might promote immune infiltration, which could provide new ideas for immunotherapy.

Identification of a Novel Immune-Related CpG Methylation Signature to Predict Prognosis in Stage II/III Colorectal Cancer

With the increasing incidence of colorectal cancer (CRC) and continued difficulty in treating it using immunotherapy, there is an urgent need to identify an effective immune-related biomarker associated with the survival and prognosis of patients with this disease. DNA methylation plays an essential role in maintaining cellular function, and changes in methylation patterns may contribute to the development of autoimmunity, aging, and cancer. In this study, we aimed to identify a novel immune-related methylated signature to aid in predicting the prognosis of patients with CRC. We investigated DNA methylation patterns in patients with stage II/III CRC using datasets from The cancer genome atlas (TCGA). Overall, 182 patients were randomly divided into training (n = 127) and test groups (n = 55). In the training group, five immune-related methylated CG sites (cg11621464, cg13565656, cg18976437, cg20505223, and cg20528583) were identified, and CG site-based risk scores were calculated using univariate Cox proportional hazards regression in patients with stage II/III CRC. Multivariate Cox regression analysis indicated that methylated signature was independent of other clinical parameters. The Kaplan-Meier analysis results showed that CG site-based risk scores could significantly help distinguish between high- and low-risk patients in both the training (P = 0.000296) and test groups (P = 0.022). The area under the receiver operating characteristic curve in the training and test groups were estimated to be 0.771 and 0.724, respectively, for prognosis prediction. Finally, stratified analysis results suggested the remarkable prognostic value of CG site-based risk scores in CRC subtypes. We identified five methylated CG sites that could be used as an efficient overall survival (OS)-related biomarker for stage II/III CRC patients.