Identification and validation of stromal-tumor microenvironment-based subtypes tightly associated with PD-1/PD-L1 immunotherapy and outcomes in patients with gastric cancer

BiSpec Pairwise AI: guiding the selection of bispecific antibody target combinations with pairwise learning and GPT augmentation

PURPOSE

Bispecific antibodies (BsAbs), capable of targeting two antigens simultaneously, represent a significant advancement by employing dual mechanisms of action for tumor suppression. However, how to pair targets to develop effective and safe bispecific drugs is a major challenge for pharmaceutical companies.

METHODS

Using machine learning models, we refined the biological characteristics of currently approved or in clinical development BsAbs and analyzed hundreds of membrane proteins as bispecific targets to predict the likelihood of successful drug development for various target combinations. Moreover, to enhance the interpretability of prediction results in bispecific target combination, we combined machine learning models with Large Language Models (LLMs). Through a Retrieval-Augmented Generation (RAG) approach, we supplement each pair of bispecific targets' machine learning prediction with important features and rationales, generating interpretable analytical reports.

RESULTS

In this study, the XGBoost model with pairwise learning was employed to predict the druggability of BsAbs. By analyzing extensive data on BsAbs and designing features from perspectives such as target activity, safety, cell type specificity, pathway mechanism, and gene embedding representation, our model is able to predict target combinations of BsAbs with high market potential. Specifically, we integrated XGBoost with the GPT model to discuss the efficacy of each bispecific target pair, thereby aiding the decision-making for drug developers.

CONCLUSION

The novelty of this study lies in the integration of machine learning and GPT techniques to provide a novel framework for the design of BsAbs drugs. This holistic approach not only improves prediction accuracy, but also enhances the interpretability and innovativeness of drug design.

Intestinal-Type Adenocarcinoma in Head and Neck: Dissecting Oncogenic Gene Alterations Through Whole Transcriptome and Exome Analysis

Adenocarcinomas of the nasal/paranasal sinuses are uncommon, but intestinal-type adenocarcinomas (ITACs) are important. Due to the rarity of these tumors, their molecular profile is not well known. To further investigate the molecular profile and find potential oncogenic drivers, we compared the whole transcriptome and exome of ITACs at different anatomic locations in the head and neck. Twenty-one head and neck adenocarcinomas were used in this study, divided into 10 sinonasal adenocarcinomas (SNT) and 11 extrasinonasal (T) head and neck adenocarcinomas according to anatomic location and histology. Tumor samples along with normal mucosa were microdissected from formalin-fixed, paraffin-embedded samples, and RNA and DNA were subjected to whole-transcriptome and -exome shotgun sequencing. Analysis of ITACs at sinonasal locations showed 410 subtype-specific differentially expressed (DE) genes and noncoding transcripts compared with the group of other anatomic locations, with 2909 subtype-specific DE genes. The groups shared 872 genes, with 17 highly different or opposing DE genes. Whole-exome mutation analysis revealed the gene MLL3 (KMT2C) to be exhibiting the most frequent loss-of-function mutations in all adenocarcinomas investigated. The results suggest that the head and neck ITACs investigated were mainly caused by loss-of-function mutations in MLL3 that disabled chromatin methylation and remodeling of all MLL3-targeted enhancers in the tumors. This changed the activity of multiple genes/gene clusters, supporting oncogenicity mostly via pathways of signaling, dedifferentiation, proliferation, migration, and immune and inflammatory deregulation, indicating a truly epigenetic event as the root cause for the heterogenous diversity of these enteric types of cancer. The data of this study form the basis for understanding cell fate determination and cellular homeostasis in the normal respiratory mucosa at different anatomic sites and show the contribution of different mucosal components to the etiology/molecular pathology of ITAC.

Immunobiological signatures and the emerging role of SPP1 in predicting tumor heterogeneity, malignancy, and clinical outcomes in stomach adenocarcinoma

BACKGROUND

Immunotherapy, as a form of immunobiological therapy, represents a promising approach for enhancing patients' immune responses. This work aims to present innovative ideas and insights for prognostic assessment and clinical treatment of stomach adenocarcinoma (STAD) by leveraging immunobiological signatures.

METHODS

We employed weighted gene co-expression network analysis (WGCNA) and unsupervised clustering analysis to identify hub genes. These hub genes were utilized to construct a prognostic risk model, and their impact on the tumor microenvironment (TME) and DNA variations was assessed using large-scale STAD patient cohorts. Additionally, we conducted transfection experiments with plasmids to investigate the influence of SPP1 on the malignancy of HGC27 and NCI-N87 cells.

RESULTS

Unsupervised clustering of 12 immune-related genes (IRGs) revealed three distinct alteration patterns with unique molecular phenotypes, clinicopathological characteristics, prognosis, and TME features. Using LASSO and multivariate Cox regression analyses, we identified three hub genes (MMP12, SPP1, PLAU) from the IRGs to establish a risk signature. This IRG-related risk model significantly stratified the prognosis risk among STAD patients in the training (n = 522), testing (n = 521), and validation (n = 300) cohorts. Notably, there were discernible differences in therapy responses and TME characteristics, such as tumor purity and lymphocyte infiltration, between the risk model groups. Subsequently, a nomogram that incorporates the IRG signature and clinicopathological factors demonstrated superior sensitivity and specificity in predicting outcomes for STAD patients. Furthermore, down-regulation of SPP1, as observed after siRNA transfection, significantly inhibited the proliferation and migration abilities of HGC27 and NCI-N87 cells.

CONCLUSIONS

In summary, this study highlights the critical role of immune-related signatures in STAD and offers novel insights into prognosis indicators and immunotherapeutic targets for this condition. SPP1 emerges as an independent prognostic factor for STAD and appears to regulate STAD progression by influencing the immune microenvironment.

EFHD1 expression is correlated with tumor-infiltrating neutrophils and predicts prognosis in gastric cancer

Background

Gastric cancer (GC) ranks third in terms of mortality worldwide. The tumor microenvironment is critical for the progression of gastric cancer. This study investigated the association between EF-hand domain containing 1 (EFHD1) expression and its clinical significance in the tumor microenvironment (TME) of gastric cancer.

Methods

We used bioinformatic analyses to assess the relevance of EFHD1 mRNA in the TME of gastric carcinoma tissues and its relationship with clinical features. Therefore, we performed multiplex immunohistochemistry analyses to determine the potential role of the EFHD1 protein in the TME of gastric cancer.

Results

EFHD1 expression increased dramatically in gastric cancer tissues compared to levels in non-cancerous tissue samples (t = 6.246, P < 0.001). The EFHD1 protein presentation was associated with invasion depth (χ2 = 19.120, P < 0.001) and TNM stages (χ2 = 14.468, P = 0.002). Notably, EFHD1 protein expression was significantly related to CD66b + neutrophil infiltration of the intratumoral (r = 0.420, P < 0.001) and stromal (r = 0.367, P < 0.001) TME in gastric cancer. Additionally, Cox regression analysis revealed that EFHD1 was an independent prognostic predictor (hazard ratio [HR] = 2.262, P < 0.001) in patients with gastric cancer.

Conclusions

Our study revealed the pattern of EFHD1 overexpression in the TME of patients with gastric cancer and demonstrated its utility as a biomarker for unfavorable clinical outcomes, thereby providing a potential immunotherapy target.

Evolution of immune and stromal cell states and ecotypes during gastric adenocarcinoma progression

Understanding tumor microenvironment (TME) reprogramming in gastric adenocarcinoma (GAC) progression may uncover novel therapeutic targets. Here, we performed single-cell profiling of precancerous lesions, localized and metastatic GACs, identifying alterations in TME cell states and compositions as GAC progresses. Abundant IgA+ plasma cells exist in the premalignant microenvironment, whereas immunosuppressive myeloid and stromal subsets dominate late-stage GACs. We identified six TME ecotypes (EC1-6). EC1 is exclusive to blood, while EC4, EC5, and EC2 are highly enriched in uninvolved tissues, premalignant lesions, and metastases, respectively. EC3 and EC6, two distinct ecotypes in primary GACs, associate with histopathological and genomic characteristics, and survival outcomes. Extensive stromal remodeling occurs in GAC progression. High SDC2 expression in cancer-associated fibroblasts (CAFs) is linked to aggressive phenotypes and poor survival, and SDC2 overexpression in CAFs contributes to tumor growth. Our study provides a high-resolution GAC TME atlas and underscores potential targets for further investigation.

Integrating multiple machine learning algorithms for prognostic prediction of gastric cancer based on immune-related lncRNAs

Background: Long non-coding RNAs (lncRNAs) play an important role in the immune regulation of gastric cancer (GC). However, the clinical application value of immune-related lncRNAs has not been fully developed. It is of great significance to overcome the challenges of prognostic prediction and classification of gastric cancer patients based on the current study.

Methods: In this study, the R package ImmLnc was used to obtain immune-related lncRNAs of The Cancer Genome Atlas Stomach Adenocarcinoma (TCGA-STAD) project, and univariate Cox regression analysis was performed to find prognostic immune-related lncRNAs. A total of 117 combinations based on 10 algorithms were integrated to determine the immune-related lncRNA prognostic model (ILPM). According to the ILPM, the least absolute shrinkage and selection operator (LASSO) regression was employed to find the major lncRNAs and develop the risk model. ssGSEA, CIBERSORT algorithm, the R package maftools, pRRophetic, and clusterProfiler were employed for measuring the proportion of immune cells among risk groups, genomic mutation difference, drug sensitivity analysis, and pathway enrichment score.

Results: A total of 321 immune-related lncRNAs were found, and there were 26 prognostic immune-related lncRNAs. According to the ILPM, 18 of 26 lncRNAs were selected and the risk score (RS) developed by the 18-lncRNA signature had good strength in the TCGA training set and Gene Expression Omnibus (GEO) validation datasets. Patients were divided into high- and low-risk groups according to the median RS, and the low-risk group had a better prognosis, tumor immune microenvironment, and tumor signature enrichment score and a higher metabolism, frequency of genomic mutations, proportion of immune cell infiltration, and antitumor drug resistance. Furthermore, 86 differentially expressed genes (DEGs) between high- and low-risk groups were mainly enriched in immune-related pathways.

Conclusion: The ILPM developed based on 26 prognostic immune-related lncRNAs can help in predicting the prognosis of patients suffering from gastric cancer. Precision medicine can be effectively carried out by dividing patients into high- and low-risk groups according to the RS.

A prognostic gene signature for gastric cancer and the immune infiltration-associated mechanism underlying the signature gene, PLG

BACKGROUND

Globally, gastric cancer (GC) is a common and lethal solid malignant tumor. Identifying the molecular signature and its functions can provide mechanistic insights into GC development and new methods for targeted therapy.

METHODS

Differentially expressed genes (DEGs) and prognostic genes (from univariate Cox regression analysis) were overlapped to obtain prognostic DEGs. Subsequently, molecular modules and the functions of these prognostic DEGs were identified by Metascape and Gene Ontology (GO)/Kyoto Encyclopedia of Genes and Genomes (KEGG)/Gene Set Enrichment Analysis (GSEA) enrichment analyses, respectively. Protein-protein interaction (PPI) networks of up- and down-regulated prognostic DEGs in GC were analyzed using the MCC algorithm of the Cytohubba plug-in in Cytoscape. The prognostic gene signature was defined on hub genes of the PPI networks by least absolute shrinkage and selection operator (LASSO)-Cox regression analysis. Furthermore, the expressional level of PLG in our clinical GC samples was validated by quantitative PCR (qPCR), western blotting, and immunohistochemistry (IHC). Subsequently, the PLG expression-correlation analysis was performed to assess the role of PLG in GC progression. Immune infiltration analysis was performed by single-sample gene set enrichment analysis (ssGSEA) to assess the inhibitory effect of PLG on immune infiltration.

RESULTS

Firstly, Up- and down-regulated prognostic DEGs and hub genes in protein-protein interaction (PPI) networks in GC were identified. A prognostic five-gene signature (i.e., PLG, SPARC, FGB, SERPINE1, and KLHL41) was identified. Among the five genes, the relationship between plasminogen (PLG) and GC remains largely unclear. Moreover, the functions of PLG-correlated genes in GC, like 'fibrinolysis', 'hemostasis', 'ion channel complex', and 'transporter complex' were identified. In addition, PLG expression correlated negatively with the infiltration of almost all immune cell types. Interestingly, the expression of PLG was significantly and highly correlated with that of CD160, an immune checkpoint inhibitor.

CONCLUSION

Our findings defined a new five-gene signature for predicting GC prognosis, but more validation is required to assess the effects and mechanism of the five genes, especially PLG, for the development of new GC therapies.

Identification of immune related molecular subtypes and prognosis model for predicting prognosis, drug resistance in cervical squamous cell carcinoma

Background: One of the features of tumor immunity is the immunosuppressive tumor microenvironment (TME). In this study, TME gene signatures were used to define the characteristics of Cervical squamous cell carcinoma (CESC) immune subtypes and construct a new prognostic model. Methods: Single sample gene set enrichment analysis (ssGSEA) was used to quantify pathway activity. RNA-seq of 291 CESC were obtained from the Cancer Genome Atlas (TCGA) database as a training set. Microarray-based data of 400 cases of CESC were obtained from the Gene Expression Compilation (GEO) database as an independent validation set. 29 TME related gene signatures were consulted from previous study. Consensus Cluster Plus was employed to identify molecular subtype. Univariate cox regression analysis and random survival forest (RSF) were used to establish the immune-related gene risk model based on the TCGA data set of CESC, and the accuracy of prognosis prediction was verified by GEO data set. ESTIMATE algorithm was used to perform immune and matrix scores on the data set. Results: three molecular subtypes (C1, C2, C3) were screened in TCGA-CESC on account of 29 TME gene signatures. Among, C3 with better survival outcome had higher immune related gene signatures, while C1 with worse prognosis time had enhanced matrix related features. Increased immune infiltration, inhibition of tumor related pathways, widespread genomic mutations and prone immunotherapy were observed in C3. Furthermore, a five immune genes signature was constructed and predicted overall survival for CESC, which successfully validated in GSE44001 dataset. A positive phenomenon was observed between five hub genes expressions and methylation. Similarly, high group enriched in matrix related features, while immune related gene signatures were enriched in low group. Immune cell, immune checkpoints genes expression levels were negatively, while most TME gene signatures were positively correlated with Risk Score. In addition, high group was more sensitive to drug resistance. Conclusion: This work identified three distinct immune subtypes and a five genes signature for predicting prognosis in CESC patients, which provided a promising treatment strategy for CESC.

A novel metabolism-related prognostic gene development and validation in gastric cancer

BACKGROUND

The importance of metabolism-related alterations in the development of gastric cancer (GC) is increasingly recognized. The present study aimed to identify metabolism-related genes to facilitate prognosis of GC patients.

METHODS

Gene expression datasets and clinical information of GC patients were downloaded from TCGA and GEO databases. We scored the enrichment of human metabolism-related pathways (n = 86) in GC samples by GSV, constructed prognostic risk models using LASSO algorithm and multivariate Cox regression analysis, combined with clinical information to construct a nomogram, and finally cis score algorithm to analyze the abundance of immune-related cells in different subtypes. We used Weka software to screen for prognosis-related marker genes and finally validated the expression of the selected genes in clinical cancer patient tissues.

RESULTS

We identified that two GC metabolism-related signatures were strongly associated with OS and the levels of immune cell infiltration. Moreover, a survival prediction model for GC was established based on six GC metabolism-related genes. Time-dependent ROC analysis showed good stability of the risk prediction scoring model. The model was successfully validated in an independent ACRG cohort, and the expression trends of key genes were also verified in the GC tissues of patients. DLX1, LTBP2, FGFR1 and MMP2 were highly expressed in the cluster with poorer prognosis while SLC13A2 and SLCO1B3 were highly expressed in the cluster with better prognosis.

CONCLUSIONS

We identified a risk predictive score model based on six metabolism-related genes related to survival, which may serve as prognostic indicators and potential therapeutic targets for GC.

DNA methylation regulator-mediated modification patterns and tumor microenvironment characterization in glioma

Growing evidences indicate DNA methylation plays a crucial regulatory role in inflammation, innate immunity, and immunotherapy. However, the overall landscape of various DNA methylation regulatory genes and their relationship with the infiltration of immune cells into the tumor microenvironment (TME) as well as the response to immunotherapy in gliomas is still not clear. Therefore, we comprehensively analyzed the correlation between DNA methylation regulator patterns, infiltration of immune cell-types, and tumor immune response status in gather glioma cohorts. Furthermore, we calculated the DNA methylation score (DMS) for individual glioma samples, then evaluated the relationship between DMS, clinicopathological characteristics, and overall survival (OS) in patients with gliomas. Our results showed three distinct DNA methylation regulator patterns among the glioma patients which correlated with three distinct tumor immune response phenotypes, namely, immune-inflamed, immune-excluded, and immune desert. We then calculated DMS for individual glioma samples based on the expression of DNA methylation-related gene clusters. Furthermore, DMS, tumor mutation burden (TMB), programmed death 1 (PD-1) expression, immune cell infiltration status in the TME, and Tumor Immune Dysfunction and Exclusion (TIDE) scores were associated with survival outcomes and clinical responses to immune checkpoint blockade therapy. We also validated the predictive value of DMS in two independent immunotherapy cohorts. In conclusion, our results demonstrated that three DNA methylation regulator patterns that correlated with three tumor immune response phenotypes. Moreover, we demonstrated that DMS was an independent predictive biomarker that correlated with survival outcomes of glioma patients and their responses to immunotherapy therapeutic regimens.

Combination of Tumor Mutational Burden and DNA Damage Repair Gene Mutations with Stromal/Immune Scores Improved Prognosis Stratification in Patients with Lung Adenocarcinoma

Background

Both the tumor environment and the genomic landscape of lung cancer may shape patient responses to treatments, including immunotherapy, but their joint impacts on lung adenocarcinoma (LUAD) prognosis are underexplored.

Methods

RNA sequencing data and whole-exome sequencing results were downloaded from the TCGA database, and only LUAD-related data were included in this study. Based on gene expression data, the ESTIMATE algorithm was used to estimate stromal and immune scores, and CIBERSORT analysis was used for quantification of the relative abundances of immune cells. Somatic mutations were used for calculating tumor mutation burden (TMB). Specific mutations in genes involved in DNA damage repair (DDR) pathways were identified. The individual and joint associations of stromal and immune score, TMB, and DDR gene mutations with 5-year survival were analyzed by the Kaplan–Meier method and multivariate Cox model.

Results

LUAD patients with a high (>highest 25%) stromal or immune score had prolonged survival as compared to those with a low (<lowest 25%) score (log-rank P=0.05 and 0.035, respectively). Patients with both high stromal and immune scores had the most favorable survival. Although the survival differences between patients with high (>highest 25%) and low (<lowest 25%) TMB, or between patients with mutant- and wild-type DDR genes were not statistically significant, a survival benefit from high TMB or DDR gene mutations was observed in patients with high stromal or immune scores.

Conclusion

A comprehensive evaluation of transcriptomic signatures and genomic biomarkers may provide a novel avenue for improving prognosis stratification in LUAD.

Development and validation of a necroptosis-related gene prognostic score to predict prognosis and efficiency of immunotherapy in gastric cancer

Necroptosis is a novel type of regulated cell death that is intimately associated with a variety of tumors. However, how necroptosis affects the identification of gastric cancer (GC) remains unclear. Here we seek to find new potential necroptosis-related biomarkers to predict GC prognosis and immunotherapy effect. We used Cox analysis to obtain shared prognostic markers related to necroptosis from five datasets (TCGA and four GEO datasets). Then, a necroptosis-related gene prognostic score (NRGPS) system was constructed using LASSO Cox regression, NRGPS consisting of three necroptosis-related mRNAs (AXL, RAI14, and NOX4) was identified, 31 pairs of GC and adjacent normal tissues from the Second Hospital of Harbin Medical University were collected and Real-Time Quantitative PCR (RT-qPCR) was used to detect the relative expression levels of the three necroptosis-related mRNAs, and external validation was performed on four GEO datasets (GSE84437, GSE26901, GSE62254 and GSE15459). In this study, Overall survival (OS) in the high-NRGPS group was significantly lower than in the low-NRGPS group. Cox regression analyses showed that NRGPS was an independent prognostic variable. Tumor-mutation-burden (TMB), tumor microenvironment (TME), microsatellite instability (MSI), and Tumor Immune Dysfunction and Exclusion (TIDE) scoring were used as predictors of the immunotherapy response. A cancer-friendly immune microenvironment, a high TIDE score, a low TMB, and a low MSI were all characteristics of the high-NRGPS group, and they all consistently showed that the issues seen there are related to immune escape in GC. The combination of three candidate genes may be an effective method for diagnostic assessment of GC prognosis and immunotherapy efficacy.

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.

Comprehensive Analysis of 5-Methylcytosine (m5C) Regulators and the Immune Microenvironment in Pancreatic Adenocarcinoma to Aid Immunotherapy

Background

Pancreatic adenocarcinoma (PAAD) is one of the most malignant cancers and has a poor prognosis. As a critical RNA modification, 5-methylcytosine (m⁵C) has been reported to regulate tumor progression, including PAAD progression. However, a comprehensive analysis of m⁵C regulators in PAAD is lacking.

Methods

In the present study, PAAD datasets were obtained from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and ArrayExpress databases. The expression pattern of m⁵C regulators were analyzed and patients were divided into different m⁵C clusters according to consensus clustering based on m⁵C regulators. Additionally, m⁵C differentially expressed genes (DEGs) were determined using Limma package. Based on m⁵C DEGs, patients were divided into m⁵C gene clusters. Moreover, m⁵C gene signatures were derived from m⁵C DEGs and a quantitative indicator, the m⁵C score, was developed from the m⁵C gene signatures.

Results

Our study showed that m⁵C regulators were differentially expressed in patients with PAAD. The m⁵C clusters and gene clusters based on m⁵C regulators and m⁵C DEGs were related to immune cell infiltration, immune-related genes and patient survival status, indicating that m⁵C modification play a central role in regulating PAAD development partly by modulating immune microenvironment. Additionally, a quantitative indicator, the m⁵C score, was also developed and was related to a series of immune-related indicators. Moreover, the m⁵C score precisely predicted the immunotherapy response and prognosis of patients with PAAD.

Conclusion

In summary, we confirmed that m⁵C regulators regulate PAAD development by modulating the immune microenvironment. In addition, a quantitative indicator, the m⁵C score, was developed to predict immunotherapy response and prognosis and assisted in identifying PAAD patients suitable for tailored immunotherapy strategies.

Spatial metabolomics identifies distinct tumor-specific subtypes in gastric cancer patients

PURPOSE

Current systems of gastric cancer (GC) molecular classification include genomic, molecular, and morphological features. GC classification based on tissue metabolomics remains lacking. This study aimed to define metabolically distinct GC subtypes and identify their clinicopathological and molecular characteristics.

EXPERIMENTAL DESIGN

Spatial metabolomics by high mass resolution imaging mass spectrometry was performed in 362 GC patients. K-means clustering was used to define tumor and stroma-related subtypes based on tissue metabolites. The identified subtypes were linked with clinicopathological characteristics, molecular features, and metabolic signatures. Responses to trastuzumab treatment were investigated across the subtypes by introducing an independent patient cohort with HER2-positive GC from a multicenter observational study.

RESULTS

Three tumor- and three stroma-specific subtypes with distinct tissue metabolite patterns were identified. Tumor-specific subtype T1(HER2+MIB+CD3+) positively correlated with HER2, MIB1, DEFA-1, CD3, CD8, FOXP3, but negatively correlated with MMR. Tumor-specific subtype T2(HER2-MIB-CD3-) negatively correlated with HER2, MIB1, CD3, FOXP3, but positively correlated with MMR. Tumor-specific subtype T3(pEGFR+) positively correlated with pEGFR. Patients with tumor subtype T1(HER2+MIB+CD3+) had elevated nucleotide levels, enhanced DNA metabolism, and a better prognosis than T2(HER2-MIB-CD3-) and T3(pEGFR+). An independent validation cohort confirmed that the T1 subtype benefited from trastuzumab therapy. Stroma-specific subtypes had no association with clinicopathological characteristics, however linked to distinct metabolic pathways and molecular features.

CONCLUSIONS

Patient subtypes derived by tissue-based spatial metabolomics are a valuable addition to existing GC molecular classification systems. Metabolic differences between the subtypes and their associations with molecular features could provide a valuable tool to aid in selecting specific treatment approaches.

Characteristics of immunophenotypes and immunological in tumor microenvironment and analysis of immune implication of CXCR4 in gastric cancer

The formation of gastric cancer (GC) is a complicated process involving multiple factors and multiple steps. The tumor–immune microenvironment is essential for the growth of GC and affects the prognosis of patients. We performed multiple machine learning algorithms to identify immunophenotypes and immunological characteristics in GC patients’ information from the TCGA database and extracted immune genes relevance of the GC immune microenvironment. C-X-C motif chemokine receptor 4 (CXCR4), belongs to the C-X-C chemokine receptor family, which can promote the invasion and migration of tumor cells. CXCR4 expression is significantly correlated to metastasis and the worse prognosis. In this work, we assessed the condition of immune cells and identified the connection between CXCR4 and GC immune microenvironment, as well as the signaling pathways that mediate the immune responses involved in CXCR4. The work showed the risk scores generated by CXCR4-related immunomodulators could distinguish risk groups consisting of differential expression genes and could use for the personalized prognosis prediction. The findings suggested that CXCR4 is involved in tumor immunity of GC, and CXCR4 is considered as a potential prognostic biomarker and immunotherapy target of GC. The prognostic immune markers from CXCR4-associated immunomodulators can independently predict the overall survival of GC.

Matrix Metalloproteinases in Helicobacter pylori-Associated Gastritis and Gastric Cancer

Gastric cancer is one of the leading causes of the cancer-related mortality worldwide. The etiology of this disease is complex and involves genetic predisposition and environmental factors, including Helicobacter pylori. Infection of the stomach with H. pylori leads to gastritis and gastric atrophy, which can progress stepwise to gastric cancer. Matrix metalloproteinases (MMPs) actively participate in the pathology development. The further progression of gastric cancer seems to be less dependent on bacteria but of intra-tumor cell dynamics. Bioinformatics data confirmed an important role of the extracellular matrix constituents and specific MMPs in stomach carcinoma invasion and metastasis, and revised their potential as predictors of the disease outcome. In this review, we describe, in detail, the impact of MMPs in H. pylori-associated gastritis and gastric cancer.

Single-cell analysis reveals that cancer-associated fibroblasts stimulate oral squamous cell carcinoma invasion via the TGF-β/Smad pathway

Although substantial progress has been made in cancer biology and treatment, the prognosis of oral squamous cell carcinoma (OSCC) is still not satisfactory because of local tumor invasion and frequent lymph node metastasis. The tumor microenvironment (TME) is a potential target in which cancer-associated fibroblasts (CAFs) are of great significance due to their interactions with cancer cells. However, the exact mechanism is still unclear. Therefore, we focus on the crosstalk between cancer cells and CAFs and discover that CAFs are the main source of TGF-β1. Transwell assays and western blot analysis further prove that CAFs activate the TGF-β1/Smad pathway to promote OSCC invasion. Through survival analysis, we confirm that CAF overexpression is correlated with poor overall survival in OSCC. To further elucidate the origin and role of CAFs in OSCC, we analyze single-cell RNA sequencing (scRNA-seq) data from 14 OSCC tumor samples and identify four distinct cell types, including CAFs, in the TME, indicating high intratumoral heterogeneity. Then, two subtypes of CAFs, namely, myofibroblasts (mCAFs) and inflammatory CAFs (iCAFs), are further distinguished. Based on the differentially upregulated genes of mCAFs and iCAFs, GO enrichment analysis reveals their different roles in OSCC progression. Furthermore, the gene expression pattern is dynamically altered across pseudotime, potentially taking part in the transformation from epithelial to mCAFs or iCAFs through the epithelial to mesenchymal transition.

An Unfolded Protein Response Related Signature Could Robustly Predict Survival Outcomes and Closely Correlate With Response to Immunotherapy and Chemotherapy in Bladder Cancer

Background: The unfolded protein response (UPR) plays a significant role in maintaining protein hemostasis in tumor cells, which are crucial for tumor growth, invasion, and resistance to therapy. This study aimed to develop a UPR-related signature and explore its correlation with immunotherapy and chemotherapy in bladder cancer. Methods: The differentially expressed UPR-related genes were put into Lasso regression to screen out prognostic genes, which constituted the UPR signature, and were incorporated into multivariate Cox regression to generate risk scores. Subsequently, the predictive performance of this signature was estimated by receiver operating characteristic (ROC) curves. The CIBERSORTx, the maftool, and Gene set enrichment analysis (GSEA) were applied to explore infiltrated immune cells, tumor mutational burden (TMB), and enriched signaling pathways in both risk groups, respectively. Moreover, The Cancer Immunome Atlas (TCIA) and Genomics of Drug Sensitivity in Cancer (GDSC) databases were used to predict responses to chemotherapy and immunotherapy. Results: Twelve genes constituted the UPR-related signature. Patients with higher risk scores had worse overall survival (OS) in training and three validation sets. The UPR-related signature was closely correlated with clinicopathologic parameters and could serve as an independent prognostic factor. M0 macrophages showed a significantly infiltrated difference in both risk groups. TMB analysis showed that the risk score in the wild type and mutation type of FGFR3 was significantly different. GSEA indicated that the immune-, extracellular matrix-, replication and repair associated pathways belonged to the high risk group and metabolism-related signal pathways were enriched in the low risk group. Prediction of immunotherapy and chemotherapy revealed that patients in the high risk group might benefit from chemotherapy, but had a worse response to immunotherapy. Finally, we constructed a predictive model with age, stage, and UPR-related risk score, which had a robustly predictive performance and was validated in GEO datasets. Conclusion: We successfully constructed and validated a novel UPR-related signature in bladder cancer, which could robustly predict survival outcomes and closely correlate with the response to immunotherapy and chemotherapy in bladder cancer.

The Novel Tumor Microenvironment-Related Prognostic Gene AIF1 May Influence Immune Infiltrates and is Correlated with TIGIT in Esophageal Cancer

BACKGROUND

Esophageal carcinoma (EC) is the sixth most common cause of cancer-related mortality worldwide. Studying the associations of the tumor microenvironment (TME) with pathology and prognosis would illustrate the underlying mechanism of prognostic prediction and provide novel targets for immunotherapy in the treatment of EC.

METHODS

Transcriptomic profiles of 159 EC patients were obtained from The Cancer Genome Atlas (TCGA) database. Stromal and immune scores were calculated using the ESTIMATE algorithm. Differentially expressed genes (DEGs) were identified by the optimal score cutoff. Functional enrichments were analyzed by DAVID, while prognostic genes were explored using the Kaplan-Meier method. Validation analysis was performed using immunohistochemistry in tissue microarrays containing samples from 145 EC patients. Multiplex immunofluorescence staining was performed to detect a panel of 6 immune markers, including T-cell immunoreceptor with Ig and ITIM domains (TIGIT), in 90 EC patients.

RESULTS

Immune scores significantly increased with increasing age, while stromal scores were dramatically elevated with increasing tumor stage. Fifteen TME-related DEGs including allograft inflammatory factor 1 (AIF1) were identified as prognostic factors of EC. Furthermore, the validation cohort indicated that AIF1 was negatively associated with the prognosis of esophageal squamous cell carcinoma patients. Subsequent analyses suggested that AIF1 may affect immune infiltrates, including T cells and natural-killer cells. Moreover, a correlation between AIF1 and TIGIT was identified.

CONCLUSIONS

These results indicate that the TME-related gene AIF1 is a promising predictor of prognosis and is related to immune infiltrates and TIGIT expression in EC. However, further mechanistic studies are needed.

The Fibronectin-ILT3 Interaction Functions as a Stromal Checkpoint that Suppresses Myeloid Cells

Suppressive myeloid cells inhibit antitumor immunity by preventing T-cell responses. Immunoglobulin-like transcript 3 (ILT3; also known as LILRB4) is highly expressed on tumor-associated myeloid cells and promotes their suppressive phenotype. However, the ligand that engages ILT3 within the tumor microenvironment and renders tumor-associated myeloid cells suppressive is unknown. Using a screening approach, we identified fibronectin as a functional ligand for ILT3. The interaction of fibronectin with ILT3 polarized myeloid cells toward a suppressive state, and these effects were reversed with an ILT3-specific antibody that blocked the interaction of ILT3 with fibronectin. Furthermore, ex vivo treatment of human tumor explants with anti-ILT3 reprogrammed tumor-associated myeloid cells toward a stimulatory phenotype. Thus, the ILT3-fibronectin interaction represents a "stromal checkpoint" through which the extracellular matrix actively suppresses myeloid cells. By blocking this interaction, tumor-associated myeloid cells may acquire a stimulatory phenotype, potentially resulting in increased antitumor T-cell responses.

Identification of Target PTEN-Based miR-425 and miR-576 as Potential Diagnostic and Immunotherapeutic Biomarkers of Colorectal Cancer With Liver Metastasis

A major complication of colorectal cancer (CRC), one of the most common and fatal types of cancers, is secondary liver metastasis. For patients with this fate, there are very few biomarkers available in clinical application, and the disease remains incurable. Recently, increasing studies demonstrated that tumorigenesis and development are closely related to immune escape, indicating that the roles of immune-related indicators might have been neglected in the past in colorectal cancer liver metastases (CRLM). Here, we unveil that elevated miR-425 and miR-576 promote CRLM through inhibiting PTEN-mediated cellular immune function. Specifically, miR-425 and miR-576 were identified for their significant upregulation in CRLM compared with the primary CRC tissues based on GSE81581 (n = 8) and GSE44121 (n = 18) datasets. Besides, we determined that the two microRNAs (miRNAs) coparticipated in restraining P53 and transforming growth factor beta (TGF-β) signaling pathways associated with tumor metastasis, and both shortened the overall survival of the patients with metastatic susceptibility. Notably, in situ hybridization on relatively large samples of paired CRC tissues (n = 157) not only substantiated that the expression of miR-425 and miR-576 was dramatically upregulated in CRLM but also revealed that they were closely related to tumor deterioration, especially liver metastases. Moreover, we further confirmed that the combination of miR-425 and miR-576 was an effective predictive model for liver metastases and poor clinical outcomes. Mechanically, downregulated PTEN (GSE81558, n = 6) was verified to be a shared target of miR-425 and miR-576 acting as metastasis-related oncogenes, on account of the presence of binding sites (+2928-+2934 and +4371-+4378, respectively) and the collaborative suppression of P53/TGF-β signaling in CRLM, which was further confirmed in CRC cells (HCT116 and SW480) based on systematic molecular biology experiments. Importantly, the target PTEN was strongly associated with microsatellite instability, tumor microenvironment, and immune cell infiltration. Thus, we speculate that miR-425 and miR-576 are novel biomarkers for CRLM prevention and immunotherapy and upstream inhibitors of the PTEN-P53/TGF-β function axis.

The Prognosis and Immune Checkpoint Blockade Efficacy Prediction of Tumor-Infiltrating Immune Cells in Lung Cancer

Backgrounds

The high morbidity and mortality of lung cancer are serious public health problems. The prognosis of lung cancer and whether to apply immune checkpoint blockade (ICB) are currently urgent problems to be solved.

Methods

Using R software, we performed Kaplan–Meier (K-M) analysis, Cox regression analysis, functional enrichment analysis, Spearman correlation analysis, and the single-sample gene set enrichment analysis.

Results

On the Tumor IMmune Estimation Resource (TIMER2.0) website, we calculated the abundance of tumor-infiltrating immune cells (TIICs) of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) patients. B cell and myeloid dendritic cell (DC1) were independent prognostic factors for LUAD and LUSC patients, respectively. Enrichment analysis confirmed that genes highly related to B cell or DC1 were closely related to the immune activation of lung cancer patients. In terms of adaptive immune resistance markers, CD8A, CD8B, immunomodulators (immunostimulants, major histocompatibility complex, receptors, and chemokines), immune-related pathways, tumor microenvironment score, and TIICs, high B cell/DC1 infiltration tissue was inflamed and immune-activated and might benefit more from the ICB. Genes most related to B cell [CD19, toll-like receptor 10 (TLR10), and Fc receptor-like A (FCRLA)] and DC1 (ITGB2, LAPTM5, and SLC7A7) partially clarified the roles of B cell/DC1 in predicting ICB efficacy. Among the 186 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, there were three and four KEGG pathways, which partially explained the molecular mechanisms by which B cell and DC1 simultaneously predicted the prognosis and efficacy of immunotherapy, respectively. Among five immune subtypes, the abundance of B cell/DC1 and expression of six hub genes were higher in immune C2, C3, and C6.

Conclusion

B cell and DC1 could predict the prognosis and ICB efficacy of LUAD and LUSC patients, respectively. The six hub genes and seven KEGG pathways might be novel immunotherapy targets. Immune C2, C3, and C6 subtypes of lung cancer patients might benefit more from ICB therapy.

Two Similar Signatures for Predicting the Prognosis and Immunotherapy Efficacy of Stomach Adenocarcinoma Patients

Background

Globally, stomach adenocarcinoma (STAD)’s high morbidity and mortality should arouse our urgent attention. How long can STAD patients survive after surgery and whether novel immunotherapy is effective are questions that our clinicians cannot escape.

Methods

Various R packages, GSEA software, Metascape, STRING, Cytoscape, Venn diagram, TIMER2.0 website, TCGA, and GEO databases were used in our study.

Results

In the TCGA and GEO, macrophage abundance of STAD tissues was significantly higher than that of adjacent tissues and was an independent prognostic factor, significantly related to the overall survival (OS) of STAD patients. Between the high- and low- macrophage abundance, we conducted differential expression, univariate and multivariate Cox analysis, and obtained 12 candidate genes, and finally constructed a 3-gene signature. Both low macrophage abundance group and group D had higher TMB and PD-L1 expression. Furthermore, top 5 common gene-mutated STAD tissues had lower macrophage abundance. Macrophage abundance and 3 key genes expression were also lower in the Epstein-Barr Virus (EBV) and HM-indel STAD subtypes and significantly correlated with the tumor microenvironment score. The functional enrichment and ssGSEA revealed 2 signatures were similar and closely related to BOQUEST_STEM_CELL_UP, including genes up-regulated in proliferative stromal stem cells. Hsa-miR-335-5p simultaneously regulated 3 key genes and significantly related to the expression of PD-L1, CD8A and PDCD1.

Conclusion

macrophage abundance and 3-gene signature could simultaneously predict the OS and immunotherapy efficacy, and both 2 signatures had remarkable similarities. Hsa-miR-335-5p and BOQUEST_STEM_CELL_UP might be novel immunotherapy targets.

Comparing immunotherapies to other frequently used treatments of gastric cancer

Introduction: Although standard doublet chemotherapy represents the upfront gold standard to increase survival and improve quality of life of gastric cancer patients, overall improvements in long-term outcomes are modest and novel treatments are urgently needed. Among these, immunotherapy is an increasingly attractive option.Areas covered: A number of clinical trials have shown that checkpoint inhibitors may be of value, but many unclear issues remain controversial and should be promptly untangled. In our short review, we offer the current available data regarding immunotherapies in gastric cancers, discuss potential limits of the reported trials, compare outcomes of checkpoints inhibitor to those of standard chemotherapy or other novel treatments, and present basic principles of immune surveillance and immune escape that may be embraced in the near future with novel drug combinations.Expert opinion: Gastric cancer patients may benefit from immunotherapy, both given alone in advanced lines and upfront in combination with chemotherapy. We believe that appropriate patients' and tumor's selection are crucial issues to maximize its potential efficacy. In addition, we think that assay standardization, biomarker agreement, and translational studies will improve the benefit-to-risk ratio of these agents in the clinical practice.

Advance investigation on synthetic small-molecule inhibitors targeting PD-1/PD-L1 signaling pathway

Immune checkpoint blockade has displayed substantial anti-tumor resistance in a variety of forms of cancer, but the fundamental regulation role remains unclear, and several questions continue to be addressed. PD-1/PD-L1 has been recognized as an anti-cancer drug target for several years, and through targeting the PD-1/PD-L1 signaling pathway, many monoclonal antibodies have thus far produced promising results in cancer therapy. The discovery of small-molecule inhibitors focused on the PD-1/PD-L1 signaling pathway is steadily reviving over decades, owing to the intrinsic shortcomings of the antibodies. PD-1 function and its PD-L1 or PD-L2 ligands are essential for the activation, proliferation, and cytotoxic secretion of T-cells in cancer to degenerate anti-tumor immune response. The axis PD-1/PD-L1 is important for the immune escape of cancer which has an immense impact on cancer treatment. In this review, we summarize the function of PD-1 and PD-L1 in cancer and aiming to enhance cancer therapy.

miR-105-5p regulates PD-L1 expression and tumor immunogenicity in gastric cancer

Cancer immunotherapies targeting the interaction between Programmed death 1 (PD-1) and Programmed death ligand 1 (PD-L1) have recently been approved for the treatment of multiple cancer types, including gastric cancer. However, not all patients respond to these therapies, while some eventually acquire resistance. A partial predictive biomarker for positive response to PD-1/PD-L1 therapy is PD-L1 expression, which has been shown to be under strict post-transcriptional control in cancer. By fractionating the PD-L1 3' untranslated region (3'UTR) into multiple overlapping fragments, we identified a small 100-nucleotide-long cis-acting region as being necessary and sufficient for post-transcriptional repression of PD-L1 expression in gastric cancer. In parallel, we performed a correlation analysis between PD-L1 expression and all host miRNAs in stomach cancer patient samples. A single miRNA, miR-105-5p, was predicted to bind to the identified cis-acting 3'UTR region and to negatively correlate with PD-L1 expression. Overexpression of miR-105-5p in gastric cancer cell lines resulted in decreased expression of PD-L1, both at the total protein and surface expression levels, and induced CD8⁺ T cell activation in co-culture assays. Finally, we show that expression of miR-105-5p in gastric cancer is partly controlled by DNA methylation of a cancer- and germline-specific promoter of its host gene, GABRA3. Dysregulation of miR-105-5p is observed in many cancer types and this study shows the importance of this miRNA in controlling the immunogenicity of cancer cells, thus highlighting it as a potential biomarker for PD-1/PD-L1 therapy and target for combinatorial immunotherapy.

Bioinformatics-based analysis of the association between the A1-chimaerin (CHN1) gene and gastric cancer

Gastric cancer (GC) is one of the most common causes of cancer-related deaths worldwide and the identification of additional therapeutic targets and biomarkers has become vital. The A1-chimaerin (CHN1) gene encodes a ras-related protein that can be activated or inactivated by binding to GTP or GDP. The present study aimed to assess the expression of CHN1 in GC tissue and cells, to explore its relationship with GC progression, and to discover the potential mechanisms underlying these associations. The ONCOMINE database and The Cancer Genome Atlas (TCGA) were used to determine the transcriptional levels of CHN1 in GC. Western blot and immunohistochemistry were used for detecting protein expression. Correlations between CHN1 levels and the clinical outcomes of GC patients were examined using Kaplan–Meier and Cox regression analyses. Moreover, the CIBERSORT algorithm was used to estimate immune cell infiltration. In GC patients, CHN1 transcription and CHN1 protein expression were upregulated, and a high expression of CHN1 was remarkably linked to poor survival in GC patients. CHN1 expression was associated with immune infiltrates and this gene showed potential involvement in multiple cancer-related pathways. Furthermore, the expression of CHN1 was correlated with the immunotherapeutic response. Finally, our results indicated that the pro-carcinogenic role of CHN1 may involve DNA methylation. To our knowledge, this is the first report characterizing CHN1 expression in GC. Our results show that high CHN1 levels could be used as a clinical biomarker for poor prognosis and that CHN1 inhibitors may have potential as anti-cancer drugs.

Identification of gastric cancer subtypes based on pathway clustering

Gastric cancer (GC) is highly heterogeneous in the stromal and immune microenvironment, genome instability (GI), and oncogenic signatures. However, a classification of GC by combining these features remains lacking. Using the consensus clustering algorithm, we clustered GCs based on the activities of 15 pathways associated with immune, DNA repair, oncogenic, and stromal signatures in three GC datasets. We identified three GC subtypes: immunity-deprived (ImD), stroma-enriched (StE), and immunity-enriched (ImE). ImD showed low immune infiltration, high DNA damage repair activity, high tumor aneuploidy level, high intratumor heterogeneity (ITH), and frequent TP53 mutations. StE displayed high stromal signatures, low DNA damage repair activity, genomic stability, low ITH, and poor prognosis. ImE had strong immune infiltration, high DNA damage repair activity, high tumor mutation burden, prevalence of microsatellite instability, frequent ARID1A mutations, elevated PD-L1 expression, and favorable prognosis. Based on the expression levels of four genes (TAP2, SERPINB5, LTBP1, and LAMC1) in immune, DNA repair, oncogenic, and stromal pathways, we developed a prognostic model (IDOScore). The IDOScore was an adverse prognostic factor and correlated inversely with immunotherapy response in cancer. Our identification of new GC subtypes provides novel insights into tumor biology and has potential clinical implications for the management of GCs.

Identification of molecular heterogeneity of hepatocellular carcinoma based on immune gene expression signatures

Although various molecular subtypes of hepatocellular carcinoma (HCC) have been investigated, most of these studies identify HCC subtype based on genomic profiling. Few studies have investigated the classification based on immune signatures, and none have classified HCC based on Immune activation and immunosuppressive. We performed immune gene expression of tumor tissue in 374 HCC patients from The Cancer Genome Atlas (TCGA) database and used unsupervised consensus clustering to stratify tumors. We then used HCC patients from the International Cancer Genome Consortium (ICGC) and Gene Expression Omnibus (GEO) as replication datasets. Based on the expression of 782 immune-related genes, HCC was stratified into four distinct immune subtypes. Tumors in one cluster (high immune activation; high-IA) indicate a higher level of Immune activation, which was characterized by higher anti-tumor immunity, higher pro-tumor immune-suppressive cell types, higher fractions of CD8+ T cells and M0 Macrophages compared with other subtypes. The high-IA also presents higher cancer-related hallmark signatures, such as epithelial-mesenchymal transition (EMT), angiogenesis, and apoptosis. We also found subpopulations of regulatory and exhaustion T lymphocyte were characterized by an opposite trend in high-IA, though samples in high-IA response to immunotherapy with better survival. The comparison of the immune profile in tumor and normal tissue indicates the activation of immune responses which only occurred in high-IA patients, while we conducted comparison of cirrhosis and non-cirrhosis tumor immune signatures, immune response activation was almost occurred in high-IA, but some of immune responses occurred in low-IA (low immune activation).

PLCG2 as a potential indicator of tumor microenvironment remodeling in soft tissue sarcoma

The tumor microenvironment (TME) plays an important role in the occurrence and development of soft tissue sarcoma (STS). A number of studies have shown that to inhibit tumor growth, the TME can be remodeled into an environment unsuitable for tumor proliferation. However, a lack of understanding exists regarding the dynamic regulation of TME.In this study, we used CIBERSORT and ESTIMATE calculation methods from the Cancer Genome Atlas (TCGA) database to calculate the proportion of tumor infiltrating immune cells (TICs) and the number of immune and stromal components in 263 STS samples. Differential expression genes (DEGs) shared by Immune Score and Stromal Score were obtained via difference analysis. Univariate Cox regression analysis and construction of protein-protein interaction (PPI) networks were applied to the DEGs.Through intersection analysis of univariate COX and PPI, PLCG2 was determined as the indicator. Further analysis showed that PLCG2 expression was positively correlated with the survival of STS patients. Gene set enrichment analysis (GSEA) showed that genes in the highly expressed PLCG2 group were enriched in immune-related activities. In the low-expression PLCG2 group, genes were enriched in the E2F, G2M, and MYC pathways. Difference analysis and correlation analysis showed that CD8+ T cells, gamma delta T cells, monocytes, and M1 macrophages were positively correlated with PLCG2 expression, indicating that PLCG2 may represent the immune status of TME.Therefore, the level of PLCG2 may aid in determining the prognosis of STS patients, especially the status of TME. These data provide additional insights into the remodeling of TME.

T cell immune regulator 1 is a prognostic marker associated with immune infiltration in glioblastoma multiforme

Glioma is the most common primary brain tumor and glioblastoma multiforme (GBM) is the most malignant brain glioma with the worst prognosis. T cell immune regulator 1 (TCIRG1) constitutes the V₀a₃ subunit of vacuolar ATPase (V-ATPase), and the function of V-ATPase in malignant tumors, such as breast cancer, melanoma and hepatocellular carcinoma, has been reported. However, the effect of the TCIRG1 subunit on GBM remains to be fully elucidated. mRNA levels of TCIRG1 in different cancer types and the corresponding normal tissues were extracted from the Oncomine and Tumor Immune Estimation Resource (TIMER) databases. The Gene Expression Omnibus (access number: GSE16011), the Chinese Glioma Genome Atlas and The Cancer Genome Atlas were used to investigate the mRNA level of TCIRG1 in glioma. Protein level validation in glioma was performed using western blotting. The Database for Annotation, Visualization and Integrated Discovery was used to analyze Gene Ontology (GO) categories for genes correlated with TCIRG1 in GBM. Protein-protein interaction (PPI) networks and module analyses were performed using Cytoscape software and the MCODE plugin. The correlation between tumor immune cell infiltration and TCIRG1 expression was explored using the TIMER database. Additionally, the correlation between TCIRG1 and the gene signature of immune infiltration was explored through TIMER and Gene Expression Profiling Interactive Analysis. External validation of TCIRG1 expression according to immune signatures in GBM was performed using the GSE16011 dataset with the GlioVis online tool. It was found that TCIRG1 expression was increased in GBM and numerous malignant tumors and may serve as a biomarker of the mesenchymal subtype of GBM. GO category analysis of positively correlated genes revealed that TCIRG1 was correlated with the immune response in GBM. PPI network and module analyses also supported the potential function of TCIRG1 in the local immune response. The expression of TCIRG1 was associated with various immune markers. It was therefore speculated that TCIRG1 is associated with glioma malignancy and may be a marker of unfavorable prognosis in patients with GBM, and it could be regarded as a prognostic biomarker and an indicator of immune infiltration in GBM.

Unsupervised Hierarchical Clustering Identifies Immune Gene Subtypes in Gastric Cancer

Objectives: The pathogenesis of heterogeneity in gastric cancer (GC) is not clear and presents as a significant obstacle in providing effective drug treatment. We aimed to identify subtypes of GC and explore the underlying pathogenesis. Methods: We collected two microarray datasets from GEO (GSE84433 and GSE84426), performed an unsupervised cluster analysis based on gene expression patterns, and identified related immune and stromal cells. Then, we explored the possible molecular mechanisms of each subtype by functional enrichment analysis and identified related hub genes. Results: First, we identified three clusters of GC by unsupervised hierarchical clustering, with average silhouette width of 0.96, and also identified their related representative genes and immune cells. We validated our findings using dataset GSE84426. Subtypes associated with the highest mortality (subtype 2 in the training group and subtype C in the validation group) showed high expression of SPARC, COL3A1, and CCN. Both subtypes also showed high infiltration of fibroblasts, endothelial cells, hematopoietic stem cells, and a high stromal score. Furthermore, subtypes with the best prognosis (subtype 3 in the training group and subtype A in the validation group) showed high expression of FGL2, DLGAP1-AS5, and so on. Both subtypes also showed high infiltration of CD4⁺ T cells, CD8⁺ T cells, NK cells, pDC, macrophages, and CD4⁺ T effector memory cells. Conclusion: We found that GC can be classified into three subtypes based on gene expression patterns and cell composition. Findings of this study help us better understand the tumor microenvironment and immune milieu associated with heterogeneity in GC and provide practical information to guide personalized treatment.

Identification and Validation of EMT-Related lncRNA Prognostic Signature for Colorectal Cancer

Background: This study aimed to explore the biological functions and prognostic role of Epithelial-mesenchymal transition (Epithelial-mesenchymal transition)-related lncRNAs in colorectal cancer (CRC). Methods: The Cancer Genome Atlas database was applied to retrieve gene expression data and clinical information. An EMT-related lncRNA risk signature was constructed relying on univariate Cox regression, Least Absolute Shrinkage and Selector Operation (LASSO) and multivariate Cox regression analysis of the EMT-related lncRNA expression data and clinical information. Then, an individualized prognostic prediction model based on the nomogram was developed and the predictive accuracy and discriminative ability of the nomogram were determined by the receiver operating characteristic curve and calibration curve. Finally, a series of analyses, such as functional analysis and unsupervised cluster analysis, were conducted to explore the influence of independent lncRNAs on CRC. Results: A total of 581 patients were enrolled and an eleven-EMT-related lncRNA risk signature was identified relying on the comprehensive analysis of the EMT-related lncRNA expression data and clinical information in the training cohort. Then, risk scores were calculated to divide patients into high and low-risk groups, and the Kaplan-Meier curve analysis showed that low-risk patients tended to have better overall survival (OS). Multivariate Cox regression analysis indicated that the EMT-related lncRNA signature was significantly associated with prognosis. The results were subsequently confirmed in the validation dataset. Then, we constructed and validated a predictive nomogram for overall survival based on the clinical factors and risk signature. Functional characterization confirmed this signature could predict immune-related phenotype and was associated with immune cell infiltration (i.e., macrophages M0, M1, Tregs, CD4 memory resting cells, and neutrophils), tumor mutation burden (TMB). Conclusions: Our study highlighted the value of the 11-EMT-lncRNA signature as a predictor of prognosis and immunotherapeutic response in CRC.

Review: Gastric cancer: Basic aspects

Gastric cancer is still one of the most prevalent and deadliest cancers in the world. Although our knowledge about the disease has progressed extraordinarily, this has not been accompanied by our capacity to effectively treat the disease. In the last years, immunotherapy made its way into the cancer field and was responsible for major changes in the treatment success rates for several cancer types. Although gastric cancer was not among the first successful targets of this type of therapy, the relationship between this type of cancer, immunosurveillance and immunotherapy is now being actively researched. In this article, we review the literature of the past year regarding the relationship between gastric cancer, its immune microenvironment and response to immunotherapy. Published data indicate that the immune microenvironment influences the clinical behaviour of gastric cancer, and is correlated with its histologic and molecular subtypes with an emphasis on the microsatellite- and EBV-positive tumour subgroups. Although the literature regarding response to immunotherapy is scarce, there is good evidence that patient stratification for immunotherapy is going to become a reality in gastric cancer.

Exploring TCGA database for identification of potential prognostic genes in stomach adenocarcinoma

Background

Stomach adenocarcinoma (STAD) is the fifth most prevalent cancer in the world and ranks third among cancer-related deaths worldwide. The tumour microenvironment (TME) plays an important role in tumorigenesis, development, and metastasis. Hence, we calculated the immune and stromal scores to find the potential prognosis-related genes in STAD using bioinformatics analysis.

Methods

The ESTIMATE algorithm was used to calculate the immune/stromal scores of the STAD samples. Functional enrichment analysis, protein–protein interaction (PPI) network analysis, and overall survival analysis were then performed on differential genes. And we validated these genes using data from the Gene Expression Omnibus database. Finally, we used the Human Protein Atlas (HPA) databases to verify these genes at the protein levels by IHC.

Results

Data analysis revealed correlation between stromal/immune scores and the TNM staging system. The top 10 core genes extracted from the PPI network, and primarily involved in immune responses, extracellular matrix, and cell adhesion. There are 31 genes have been validated with poor prognosis and 16 genes were upregulated in tumour tissues compared with normal tissues at the protein level.

Conclusions

In summary, we identified genes associated with the tumour microenvironment with prognostic implications in STAD, which may become potential therapeutic markers leading to better clinical outcomes.