Stromal score as a prognostic factor in primary gastric cancer and close association with tumor immune microenvironment

Stromal score is a promising index in tumor patients' outcome determination

Background

Immune status is widely acknowledged as a valuable marker for predicting cancer prognosis and therapy response. However, there has been a limited understanding of the stromal landscape in cancer.

Methods

By employing ESTIMATE, stromal- and immune-scores were inferred for 6193 tumor samples spanning 12 cancer types sourced from The Cancer Genome Atlas (TCGA). Subsequently, the samples were categorized into seven groups based on their stromal and immune scores. A comparison of prognosis, lymphocyte and stromal cell infiltration, and the response to programmed death ligand 1 (PD-L1) therapy was conducted among these subtypes.

Results

It was unveiled by the analysis that, in the majority of cancer types, stromal score exhibited a more potent predictive capability for outcomes compared to the immune score. Furthermore, it was observed that in four cancer types, intermediate immune infiltration coupled with low stromal infiltration correlated with the most favorable overall survival, whereas an unfavorable outcome was predicted in colorectal cancer (CRC) and stomach adenocarcinoma (STAD) when high immune infiltration coexisted with intermediate or high stromal infiltration.

Conclusion

In summary, while high immune scores frequently correlate with a positive prognosis, such correlation is not universal. A potential strategy to address the current limitations of the immune score in specific circumstances could involve a focus on stromal scores or a subtle integration of stromal and immune status.

ANRGs impact on gastric cancer progression and drug efficacy: A comprehensive study

Gastric cancer (GC) is a significant contributor to cancer-related mortality globally, with the heterogeneity of metastasis and treatment impacting patient prognosis. Currently, the treatment of GC still relies on early surgical resection, and comprehensive treatment is needed for patients with metastatic GC. Anikis-related genes (ANRGs) have been shown to affect tumor metastasis. Exploring the role of ANRGs in GC will help us understand the mechanism of tumor metastasis; screening precise targets and selecting appropriate chemotherapeutics will help individualize the treatment of GC patients. In this study, we established a prognostic scoring model based on ANRGs and explored their association with GC patient prognosis, immune microenvironment, chemotherapeutic drug sensitivity, and small molecule compounds. Our findings revealed that a gene signature composed of ANXA5, CCN1, EGF, VTN, and ZBTB7A accurately predicted GC patient prognosis. Patients in the low-risk group had better outcomes, higher macrophage M1 infiltration, and higher tumor mutation burden. The half maximal inhibitory concentration (IC50) values of Ponatinib (ap.24534), Motesanib (amg.706), and Navitoclax (abt.263) were lower in the high-risk group, indicating that patients in the high-risk group were more sensitive to these chemotherapy drugs, meaning with better clinical outcomes. In addition, we screened the small molecule compound SGC-CBP30 that can inhibit ANXA5 and CCN1, and these results help individualized treatment of GC patients. Our study identified key genes based on ANRGs and developed a novel gene signature for predicting the prognosis of GC patients and understanding the relationship between immunity and tumor mutation burden. Additionally, we identified chemotherapeutic drugs that can guide GC treatment and elucidated the binding affinity between specific targeted drugs and distinct protein sites, providing novel insights for the precise treatment of GC patients.

Leveraging various extracellular matrix levels to assess prognosis and sensitivity to immunotherapy in patients with ovarian cancer

Background

Ovarian cancer (OC) is the fifth leading cause of cancer-related deaths among women. Late diagnosis and heterogeneous treatment result in a poor prognosis for patients with OC. Therefore, we aimed to develop new biomarkers to predict accurate prognoses and provide references for individualized treatment strategies.

Methods

We constructed a co-expression network applying the "WGCNA" package and identified the extracellular matrix-associated gene modules. We figured out the best model and generated the extracellular matrix score (ECMS). The ECMS' ability to predict accurate OC patients' prognoses and responses to immunotherapy was evaluated.

Results

The ECMS was an independent prognostic factor in the training [hazard ratio (HR) = 3.132 (2.068-4.744), p< 0.001] and testing sets [HR = 5.514 (2.084-14.586), p< 0.001]. The receiver operating characteristic curve (ROC) analysis showed that the AUC values for 1, 3, and 5 years were 0.528, 0.594, and 0.67 for the training set, respectively, and 0.571, 0.635, and 0.684 for the testing set, respectively. It was found that the high ECMS group had shorter overall survival than the low ECMS group [HR = 2 (1.53-2.61), p< 0.001 in the training set; HR = 1.62 (1.06-2.47), p = 0.021 in the testing set; HR = 1.39 (1.05-1.86), p = 0.022 in the training set]. The ROC values of the ECMS model for predicting immune response were 0.566 (training set) and 0.572 (testing set). The response rate to immunotherapy was higher in patients with low ECMS.

Conclusion

We created an ECMS model to predict the prognosis and immunotherapeutic benefits in OC patients and provided references for individualized treatment of OC patients.

Stemness-related lncRNAs signature as a biologic prognostic model for head and neck squamous cell carcinoma

Cancer stem cells (CSCs) and long non-coding RNAs (lncRNAs) are particularly important for tumor cell growth and migration, and recurrence and drug resistance, including head and neck squamous cell carcinoma (HNSCC). The purpose of this study was to explore stemness-related lncRNAs (SRlncRNAs) that could be used for prognosis of patients with HNSCC. HNSCC RNA sequencing data and matched clinical data were obtained from TCGA database, and stem cell characteristic genes related to HNSCC mRNAsi were obtained from the online database by WGCNA analysis, respectively. Further, SRlncRNAs were obtained. Then, the prognostic model was constructed to forecast patient survival through univariate Cox regression and LASSO-Cox method based on SRlncRNAs. Kaplan-Meier, ROC and AUC were used to evaluate the predictive ability of the model. Moreover, we probed the underlying biological functions, signalling pathways and immune status hidden within differences in prognosis of patients. We explored whether the model could guide personalized treatments included immunotherapy and chemotherapy for HNSCC patients. At last, RT-qPCR was performed to analyze the expressions levels of SRlncRNAs in HNSCC cell lines. A SRlncRNAs signature was identified based on 5 SRlncRNAs (AC004943.2, AL022328.1, MIR9-3HG, AC015878.1 and FOXD2-AS1) in HNSCC. Also, risk scores were correlated with the abundance of tumor-infiltrating immune cells, whereas HNSCC-nominated chemotherapy drugs were considerably different from one another. The final finding was that these SRlncRNAs were abnormally expressed in HNSCCCS according to the results of RT-qPCR. These 5 SRlncRNAs signature, as a potential prognostic biomarker, can be utilized for personalized medicine in HNSCC patients.

Genomic analysis of matrix metalloproteinases affecting the prognosis and immunogenic profile of gastric cancer

This study systematically and comprehensively analyzed the characteristics of matrix metalloproteinases (MMPs) in gastric cancer (GC) and revealed the relationship between MMPs and prognoses, clinicopathological features, tumor microenvironment, gene mutations, and drug therapy response in patients with GC. Based on the mRNA expression profiles of 45 MMP-related genes in GC, we established a model that classified GC patients into three groups based on cluster analysis of the mRNA expression profiles. The 3 groups of GC patients showed significantly different prognoses as well as tumor microenvironmental characteristics. Next, we used Boruta's algorithm and PCA method to establish an MMP scoring system and found that lower MMP scores were associated with better prognoses, lower clinical stages, better immune cell infiltration, lower degrees of immune dysfunction and rejection, and more genetic mutations. Whereas a high MMP score was the opposite. These observations were further validated with data from other datasets, showing the robustness of our MMP scoring system. Overall, MMP could be involved in the tumor microenvironment (TME), clinical features, and prognosis of GC. An in-depth study of MMP patterns can better understand the indispensable role of MMP in the development of GC and reasonably assess the survival prognosis, clinicopathological features, and drug efficacy of different patients, thus providing clinicians with a broader vision of GC progression and treatment.

[CD40LG is a novel immune- and stroma-related prognostic biomarker in the tumor microenvironment of invasive breast cancer]

OBJECTIVE

To identify tumor microenvironment (TME)- related genes associated with the occurrence of invasive breast cancer as potential prognostic biomarkers and therapeutic targets.

METHODS

RNA transcriptome data and clinically relevant data were retrieved from TCGA database, and the StromalScore and ImmuneScore were calculated using the ESTIMATE algorithm. The differentially expressed genes (DEGs) were screened by taking the intersection. A protein- protein interaction network was established, and univariate COX regression analysis was used to identify the core genes among the DEGs. A core gene was selected for GSEA and CIBERSORT analysis to determine the function of the core gene and the proportion of tumor-infiltrating immune cells, respectively. Western blotting and qRT-PCR were performed to verify the expression level of CD40LG in breast cancer cell lines and clinical specimens.

RESULTS

A total of 1222 samples (124 normal and 1098 tumor samples) were extracted from TCGA for analysis, from which 487 DEGs were identified. These genes were mainly enriched in immune-related pathways, and crossover analysis identified 11 key genes (CD40LG, ITK, CD5, CD3E, SPN, IL7R, CD48, CCL19, CD2, CD52, and CD2711) associated with breast cancer TME status. CD40LG was selected as the core gene, whose high expression was found to be associated with a longer overall survival of breast cancer patients (P=0.002), and its expression level differed significantly with TNM stage and tumor size (P < 0.05). GSEA and CIBERSORT analyses indicated that CD40LG expression level was associated with immune activity in the TME. Western blotting and qRT-PCR showed that the protein and mRNA expression of CD40LG were significantly lower in breast cancer cells and cancer tissues than in normal breast cells and adjacent tissues.

CONCLUSIONS

The high expression of CD40LG in TME is positively correlated with the survival of patients with invasive breast cancer, suggesting its value as a potential new biomarker for predicting prognosis of the patients.

SIRGs score may be a predictor of prognosis and immunotherapy response for esophagogastric junction adenocarcinoma

Background

Esophagogastric junction adenocarcinoma (EGJA) is a special malignant tumor with unknown biological behavior. PD-1 checkpoint inhibitors have been recommended as first-line treatment for advanced EGJA patients. However, the biomarkers for predicting immunotherapy response remain controversial.

Methods

We identified stromal immune-related genes (SIRGs) by ESTIMATE from the TCGA-EGJA dataset and constructed a signature score. In addition, survival analysis was performed in both the TCGA cohort and GEO cohort. Subsequently, we explored the differences in tumor-infiltrating immune cells, immune subtypes, immune-related functions, tumor mutation burden (TMB), immune checkpoint gene expression, immunophenoscore (IPS) between the high SIRGs score and low SIRGs score groups. Finally, two validation cohorts of patients who had accepted immunotherapy was used to verify the value of SIRGs score in predicting immunotherapy response.

Results

Eight of the SIRGs were selected by LASSO regression to construct a signature score (SIRGs score). Univariate and multivariate analyses in the TCGA and GEO cohort suggested that SIRGs score was an independent risk factor for the overall survival (OS) and it could increase the accuracy of clinical prediction models for survival. However, in the high SIRGs score group, patients had more immune cell infiltration, more active immune-related functions, higher immune checkpoint gene expression and higher IPS-PD1 and IPS-PD1-CTLA4 scores, which indicate a better response to immunotherapy. The external validation illustrated that high SIRGs score was significantly associated with immunotherapy response and immune checkpoint inhibitors (ICIs) can improve OS in patients with high SIRGs score.

Conclusion

The SIRGs score may be a predictor of the prognosis and immune-therapy response for esophagogastric junction adenocarcinoma.

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

Background

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

Methods

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

Results

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

Conclusion

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

Developing an immune signature for triple-negative breast cancer to predict prognosis and immune checkpoint inhibitor response

Aim: We aimed to develop a new signature based on immune-related genes to predict prognosis and response to immune checkpoint inhibitors in patients with triple-negative breast cancer (TNBC). Materials & methods: Single-sample gene set enrichment was used to develop an immune-based prognostic signature (IPRS) for TNBC patients. We conducted multivariate Cox analysis to evaluate the prognosis value of the IPRS. Result: An IPRS based on 66 prognostic genes was developed. Multivariate Cox analysis indicated that the IPRS was an independent factor for prognosis. PD-1, PD-L1, PD-L2 and CTLA4 gene expression was higher in the low-risk group, suggesting IPRS could predict the response to immune checkpoint inhibitors. Conclusion: The IPRS might be a reliable signature to predict TNBC patients' prognosis and response to immune checkpoint inhibitors, but needs prospective validation.

Integrated Analysis to Obtain Potential Prognostic Signature in Glioblastoma

Glioblastoma multiforme (GBM) is the most malignant and multiple tumors of the central nervous system. The survival rate for GBM patients is less than 15 months. We aimed to uncover the potential mechanism of GBM in tumor microenvironment and provide several candidate biomarkers for GBM prognosis. In this study, ESTIMATE analysis was used to divide the GBM patients into high and low immune or stromal score groups. Microenvironment associated genes were filtered through differential analysis. Weighted gene co-expression network analysis (WGCNA) was performed to correlate the genes and clinical traits. The candidate genes’ functions were annotated by enrichment analyses. The potential prognostic biomarkers were assessed by survival analysis. We obtained 81 immune associated differentially expressed genes (DEGs) for subsequent WGCNA analysis. Ten out of these DEGs were significantly associated with targeted molecular therapy of GBM patients. Three genes (S100A4, FCGR2B, and BIRC3) out of these genes were associated with overall survival and the independent test set testified the result. Here, we obtained three crucial genes that had good prognostic efficacy of GBM and may help to improve the prognostic prediction of GBM.

DPP4 Is a Potential Prognostic Marker of Thyroid Carcinoma and a Target for Immunotherapy

DPP4 (dipeptidyl peptidase 4) is expressed in many cancers, but the relationship between DPP4 and thyroid carcinoma (THCA) is incompletely understood. We aim to explore the expression of DPP4 in THCA and the correlation between DPP4 expression with the prognosis of THCA and antitumor immunity. We systematically analyzed data from The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), and Gene Expression Omnibus (GEO) databases and explored DPP4 expression, its impact on prognosis, and its relationship with antitumor immunity in THCA. Next, we collected 18 pairs of fresh THCA and adjacent paracancerous tissues and performed RT-qPCR to validate the DPP4 mRNA level. Concurrently, immunohistochemistry (IHC) analysis was performed on 12 pairs of paraffin-embedded tissues of medullary thyroid carcinoma (MTC) and paracancerous tissues to validate the DPP4 protein level. Bioinformatics analysis showed that DPP4 mRNA expression in THCA was significantly higher than that in paracancerous tissues (p < 0.01). DPP4 was expressed at the highest levels in MTC than in other pathological types. The DPP4 expression level was different between groups with different clinical characteristics. The higher the DPP4 expressed in THCA, the lower the disease-free survival (DFS) was (HR = 1.8, p=0.048). DPP4 was significantly correlated with immune cell infiltration and immune response and was positively associated with 21 immune checkpoint genes (ICGs) in THCA (p < 0.05). The results of RT-qPCR showed that the relative mRNA expression of DPP4 was significantly upregulated in 18 THCA tissues compared to that in paracancerous tissues (p=0.011). IHC results showed that the DPP4 protein level was higher in 12 MTC tissues than in paracancerous tissues (p=0.011). In conclusion, DPP4 is a potential prognostic marker of THCA and may become an effective target for immunotherapy.

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

Different subtypes of gastric cancer differentially respond to immune checkpoint inhibitors (ICI). This study aimed to investigate whether the Estimation of STromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm is related to the classification and prognosis of gastric cancer and to establish an ESTIMATE-based gene signature to predict the prognosis for patients. The immune/stromal scores of 388 gastric cancer patients from TCGA were used in this analysis. The upregulated differentially expressed genes (DEGs) in patients with high stromal/immune scores were identified. The immune-related hub DEGs were selected based on protein-protein interaction (PPI) analysis. The prognostic values of the hub DEGs were evaluated in the TCGA dataset and validated in the GSE15460 dataset using the Kaplan-Meier curves. A prognostic signature was built using the hub DEGs by Cox proportional hazards model, and the accuracy was assessed using receiver operating characteristic (ROC) analysis. Different subtypes of gastric cancer had significantly different immune/stromal scores. High stromal scores but not immune scores were significantly associated with short overall survivals of TCGA patients. Nine hub DEGs were identified in PPI analysisThe expression of these hub DEG negatively correlated with the overall survival in the TCGA cohort, which was validated in the GSE15460 cohort. A 9-gene prognostic signature was constructed. The risk factor of patients was calculated by this signature. High-risk patients had significantly shorter overall survival than low-risk patients. ROC analysis showed that the prognostic model accurately identified high-risk individuals within different time frames. We established an effective 9-gene-based risk signature to predict the prognosis of gastric cancer patients, providing guidance for prognostic stratification.

Development and Verification of an Immune-Based Gene Signature for Risk Stratification and Immunotherapeutic Efficacy Assessment in Gastric Cancer

Objective

Due to the molecular heterogeneity of gastric cancer, only minor patients respond to immunotherapeutic schemes. This study is aimed at developing an immune-based gene signature for risk stratification and immunotherapeutic efficacy assessment in gastric cancer.

Methods

An immune-based gene signature was developed in gastric cancer by LASSO method in the training set. The predictive performance was validated in the external datasets. KEGG pathways related to risk scores were assessed by GSEA. Based on multivariate Cox regression analysis, a nomogram was established. Sensitivity to chemotherapy drugs was evaluated between high- and low-risk samples. The relationships of risk scores with infiltration levels of immune cells, stromal scores, immune scores, immune cell subgroups, and overall response to anti-PD-L1 therapy were determined.

Results

Our results showed that high risk scores were indicative of undesirable survival outcomes both in the training set (p < 0.0001) and the validation set (p = 0.002). Moreover, this signature could independently predict patients' survival (HR: 2.656 (1.919-3.676) and p < 0.001). Subgroup analysis confirmed the sensitivity of this signature in predicting prognosis (all p < 0.05). Cancer-related pathways were primarily enriched in high-risk samples, such as MAPK and TGF-β pathways (p < 0.05). By incorporating stage and the risk score, we established a nomogram for predicting one-, three-, and five-year survival probability. Patients with high-risk scores were more sensitive to chemotherapy drugs (p < 0.05). There was heterogeneity in immune cells between high- and low-risk samples (p < 0.05). Samples with progressive disease exhibited the highest risk score, and those with complete response had the lowest risk score (p < 0.05).

Conclusion

This immune-based gene signature might be representative of a promising prognostic classifier for predicting risk stratification and immunotherapeutic efficacy in gastric cancer, assisting personalized therapy and follow-up plan.

Gene Expression Subtyping Reveals Immune alterations:TCGA Database for Prognosis in Ovarian Serous Cystadenocarcinoma

Serous ovarian cancer is the most common and primary death type in ovarian cancer. In recent studies, tumor microenvironment and tumor immune infiltration significantly affect the prognosis of ovarian cancer. This study analyzed the four gene expression types of ovarian cancer in TCGA database to extract differentially expressed genes and verify the prognostic significance. Meanwhile, functional enrichment and protein interaction network analysis exposed that these genes were related to immune response and immune infiltration. Subsequently, we proved these prognostic genes in an independent data set from the GEO database. Finally, multivariate cox regression analysis revealed the prognostic significance of TAP1 and CXCL13. The genetic alteration and interaction network of these two genes were shown. Then, we established a nomogram model related to the two genes and clinical risk factors. This model performed well in Calibration plot and Decision Curve Analysis. In conclusion, we have obtained a list of genes related to the immune microenvironment with a better prognosis for serous ovarian cancer, and based on this, we have tried to establish a clinical prognosis model.

Disparity of Hepatocellular Carcinoma in Tumor Microenvironment-Related Genes and Infiltrating Immune Cells between Asian and Non-Asian Populations

Hepatocellular carcinoma (HCC) is the most common cause of primary liver cancer deaths worldwide. The major risk factors for liver cancer development are cirrhosis, hepatitis B virus (HBV), hepatitis C virus (HCV) infection, and chronic alcohol abuse. HCC displays heterogeneity in terms of biology, etiology, and epidemiology. In Southeast Asia and Africa, chronic HBV infection is a major risk factor for HCC, whereas chronic HCV infection is a risk factor for HCC in western countries and Japan. Environmental and genetic conditions also play a role in the regional and temporal variations in the incidence of HCC. In this study, we used the ESTIMATE (ESTIMATE, Estimation of stromal and immune cells in malignant tumor tissues using expression data) algorithm and the CIBERSOFT tool to analyze gene expression profiles and infiltrating immune cells in HCC between Asian and non-Asian patients. The results showed that stromal and immune scores were dependent on overall survival (OS) in non-Asian patients but not in Asian patients. Kaplan-Meier survival analysis revealed four differentially expressed genes (DEGs) that were significantly associated with OS in non-Asian patients only. CIBERSORT (CIBERSORT, Cell type identification by estimating relative subsets of known RNA transcripts) analysis indicated that the composition of infiltrating immune cells was significantly different between Asian and non-Asian patients. By parsing the subclasses of HCC, the ability to predict prognosis and guide therapeutic targets for potentially actionable HCC may be improved.

Identification of a novel 10 immune-related genes signature as a prognostic biomarker panel for gastric cancer

Background

Emerging evidence indicates that immune infiltrating cells in tumor microenvironment (TME) correlates with the development and progression of gastric cancer (GC). This study aimed to systematically investigate the immune‐related genes (IRGs) to develop a prognostic signature to predict the overall survival (OS) in GC.

Method

The gene expression profiles of training dataset (GSE62254), validation dataset I (GSE15459), and validation dataset II (GSE84437) were retrieved from GEO and TCGA databases. In the present study, we developed a 10 IRGs prognostic signature with the combination of weighted gene co‐expression network analysis (WGCNA) and least absolute shrinkage and selection operator method (LASSO) COX model.

Results

In the training dataset, the accuracy of the signature was 0.681, 0.741, and 0.72 in predicting 1, 3, and 5‐year OS separately. The signature also had good performance in validation dataset Ⅰ with the accuracy of 0.57, 0.619, and 0.694, and in validation dataset Ⅱ with the accuracy of 0.559, 0.624, and 0.585. Then, we constructed a nomogram using the signature and clinical information which had strong discrimination ability with the c‐index of 0.756. In the immune infiltration analysis, the signature was correlated with multiple immune infiltrating cells such as CD8 T cells, CD4 memory T cells, NK cells, and macrophages. Furthermore, several significant pathways were enriched in gene set enrichment analysis (GSEA) analysis, including TGF‐beta signaling pathway and Wnt signaling pathway.

Conclusion

The signature of 10 IRGs we identified can effectively predict the prognosis of GC and provides new insight into discovering candidate prognostic biomarkers of GC.

Identification of potential targets of triptolide in regulating the tumor microenvironment of stomach adenocarcinoma patients using bioinformatics

This study aimed to identify potential pharmacological targets of triptolide regulating the tumor microenvironment (TME) of stomach adenocarcinoma (STAD) patients. A total of 343 STAD cases from The Cancer Genome Atlas (TCGA) were assigned into high- or low-score groups applying Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE). Hub genes were identified from differentially expressed genes (DEGs) shared by stromal- and immune-related components in the TME of STAD patients using R software. Cox regression analysis was used to identify genes significantly correlated with STAD patient survival. Triptolide target genes were predicted from the Traditional Chinese Medicine Systems Pharmacology Database and Analysis Platform (TCMSP). Top 30 genes filtered by Cytohubba from 734 DEGs were screened as hub genes. Forty-two genes were found to be at high risk for STAD prognosis. Thirty-four targets of triptolide were predicted using the TCMSP database. Importantly, C-X-C chemokine receptor type 4 (CXCR4) was identified as a potential target of triptolide associated with the TME in STAD. Analysis of survival highlighted the association between CXCR4 upregulation with STAD progression and poor prognosis. Gene Set Enrichment Analysis (GSEA) confirmed that genes in the CXCR4- upregulated group had significant enrichment in immune-linked pathways. Additionally, triptolide targets were found to be significantly enriched in CXCR4-related chemokine and cancer-related p53 signaling pathways. Molecular docking demonstrated a high affinity between triptolide and CXCR4. In conclusion, CXCR4 may be a therapeutic target of triptolide in the treatment of STAD patients by modulating the TME.

A Novel RNA-Binding Protein Signature to Predict Clinical Outcomes and Guide Clinical Therapy in Gastric Cancer

Objective: This study aimed to develop an RNA-binding protein (RBP)-based signature for risk stratification and guiding clinical therapy in gastric cancer. Methods: Based on survival-related RBPs, an RBP-based signature was established by LASSO regression analysis in TCGA dataset. Kaplan-Meier curves were drawn between high- and low-risk groups. The predictive efficacy of this signature was assessed via ROCs at 1-, 3-, and 5-year survival. Its generalizability was verified in an external dataset. Following adjustment with other clinicopathological characteristics, the independency of survival prediction was evaluated via multivariate Cox regression and subgroup analyses. GSEA was utilized in identifying activated pathways in two groups. Stromal score, immune score, tumor purity, and infiltration levels of 22 immune cells were determined in each sample via the ESTIMATE and CIBERSORT algorithms. The sensitivity to chemotherapy drugs was assessed through the GDSC database. Results: Data showed that patients with high risk exhibited unfavorable clinical outcomes than those with low risk. This signature possessed good performance in predicting 1-, 3-, and 5-year survival and can be independently predictive of patients' survival. Calcium, ECM receptor interaction, and focal adhesion were highly enriched in high-risk samples. High-risk samples presented increased stromal and immune scores and reduced tumor purity. Moreover, this signature presented close relationships with immune infiltrations. Low-risk specimens were more sensitive to sorafenib, gefitinib, vinorelbine, and gemcitabine than high-risk specimens. Conclusion: This RBP-based signature may be a promising tool for predicting clinical outcomes and guiding clinical therapy in gastric cancer.

Identification of Prognostic Related Genes of Tumor Microenvironment Derived From Esophageal Cancer Patients

Background and Objective: Esophageal cancer (ESCA) is a commonly occurring cancer worldwide with poor survival and limited therapeutic options. Due to the lack of biomarkers that facilitate early detection, its treatment remains a great challenge. This study aims at identifying the tumor microenvironment (TME)-related genes, which might affect prognosis and accelerate clinical treatment for ESCA patients.

Methods: We integrated the expression profiles from ESCA patients in The Cancer Genome Atlas. Then, we determined the stromal and immune scores of each sample using the R package. The Gene Expression Omnibus database was used to validate the expression profile of the key genes.

Results: Tumor mutational burden showed a significant difference between the groups of ESCA patients with high and low ESTIMATE scores. We identified 859 intersection genes among patients with different immune and stromal scores. Moreover, gene ontology analysis demonstrated that these 859 intersection genes were closely related to adaptive immune response and regulation of lymphocyte activation. Kyoto Encyclopedia of Genes and Genomes showed the enrichment of cytokine-cytokine receptor interaction and chemokine signaling pathway in the TME. Furthermore, the protein–protein interaction network consisted of 175 nodes. We selected 35 hub genes, including ITGAM, CXCL10, CCR2, CCR5, and CCR1. Of these, 23 intersection genes predicted the overall survival rate. C1QA and FCER1G correlated with overall survival of the ESCA patients in the two databases.

Conclusion: We identified a set of stromal and immune score-related prognostic differentially expressed genes that could influence the complexity of the TME. C1QA and FCER1G were identified and validated with respect to their role in the progression of ESCA.

Overexpressed PLAU and its potential prognostic value in head and neck squamous cell carcinoma

Background

Metastasis is a major event for survival and prognosis in patients with head and neck squamous cell carcinomas (HNSCC). A primary cause of metastasis is the proteolytic degradation of the extracellular matrix (ECM). The plasminogen activator urokinase (PLAU) is involved in the transformation of plasminogen to plasmin leading to hydrolyzation of ECM-related proteins. However, the role of PLAU expression in HNSCC is unclear and the worth being investigated.

Methods

PLAU expression profiles and clinical parameters from multiple HNSCC datasets were used to investigate the relationship of PLAU expression and HNSCC survival. GO and PPI network were established on PLAU-related downstream molecular. The stroma score was deconvoluted for analysis of PLAU’s association with the immune environment. ROC analysis was applied to show the performance of PLAU in predicting HNSCC prognosis.

Results

PLAU mRNA was significantly elevated, as opposed to its methylation, in HNSCC tumor samples over normal specimens (all p < 0.01). Univariate and multivariate cox analysis showed PLAU could be an independent indicator for HNSCC prognosis. Combining with neck lymph node status, the AUC of PLAU in predicting 5-years overall survival reached to 0.862. GO enrichment analysis showed the major biological process (extracellular matrix organization and the P13K-Akt signaling pathway) may involve to the possible mechanism of PLAU’s function on HNSCC prognosis. Furthermore, PLAU expression was positively correlated with stroma cell score, M1 type macrophages, and negatively associated with CD4 + T cell, Tregs cell, and follicular helper T cell.

Conclusions

PLAU might be an independent biomarker for predicting outcomes of HNSCC patients. The elevated expression of PLAU was associated with HPV positivity and neck node status. The PI3K-Akt pathway and aberrant proportions of immune cells might underly the mechanism of PLAU’s oncogene role in HNSCC.

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.

Unsupervised Clustering Reveals Distinct Subtypes of Biliary Atresia Based on Immune Cell Types and Gene Expression

BACKGROUND

Biliary atresia (BA) is a severe cholangiopathy of early infancy that destroys cholangiocytes, obstructs ductular pathways and if left untreated, culminates to liver cirrhosis. Mechanisms underlying the etiological heterogeneity remain elusive and few studies have attempted phenotyping BA. We applied machine learning to identify distinct subtypes of BA which correlate with the underlying pathogenesis.

METHODS

The BA microarray dataset GSE46995 was downloaded from the Gene Expression Omnibus (GEO) database. Unsupervised hierarchical cluster analysis was performed to identify BA subtypes. Then, functional enrichment analysis was applied and hub genes identified to explore molecular mechanisms associated with each subtype. An independent dataset GSE15235 was used for validation process.

RESULTS

Based on unsupervised cluster analysis, BA patients can be classified into three distinct subtypes: Autoimmune, Viral and Embryonic subtypes. Functional analysis of Subtype 1 correlated with Fc Gamma Receptor (FCGR) activation and hub gene FCGR2A, suggesting an autoimmune response targeting bile ducts. Subtype 2 was associated with immune receptor activity, cytokine receptor, signaling by interleukins, viral protein interaction, suggesting BA is associated with viral infection. Subtype 3 was associated with signaling and regulation of expression of Robo receptors and hub gene ITGB2, corresponding to embryonic BA. Moreover, Reactome pathway analysis showed Neutrophil degranulation pathway enrichment in all subtypes, suggesting it may result from an early insult that leads to biliary stasis.

CONCLUSIONS

The classification of BA into different subtypes improves our current understanding of the underlying pathogenesis of BA and provides new insights for future studies.

Integrating Tumor Stroma Biomarkers With Clinical Indicators for Colon Cancer Survival Stratification

The tumor stroma plays an important role in tumor progression and chemotherapeutic resistance; however, its role in colon cancer (CC) survival prognosis remains to be investigated. Here, we identified tumor stroma biomarkers and evaluated their role in CC prognosis stratification. Four independent datasets containing a total of 1,313 patients were included in this study and were divided into training and testing sets. Stromal scores calculated using the estimation of stromal and immune cells in malignant tumors using expression data (ESTIMATE) algorithm were used to assess the tumor stroma level. Kaplan-Meier curves and the log-rank test were used to identify relationships between stromal score and prognosis. Tumor stroma biomarkers were identified by cross-validation of multiple datasets and bioinformatics methods. Cox proportional hazards regression models were constructed using four prognosis factors (age, tumor stage, the ESTIMATE stromal score, and the biomarker stromal score) in different combinations for prognosis prediction and compared. Patients with high stromal scores had a lower overall survival rate (p = 0.00016), higher risk of recurrence (p < 0.0001), and higher probability of chemotherapeutic resistance (p < 0.0001) than those with low scores. We identified 16 tumor stroma biomarkers and generated a new prognosis indicator termed the biomarker stromal score (ranging from 0 to 16) based on their expression levels. Its addition to an age/tumor stage-based model significantly improved prognosis prediction accuracy. In conclusion, the tumor stromal score is significantly negatively associated with CC survival prognosis, and the new tumor stroma indicator can improve CC prognosis stratification.

A Prognostic Microenvironment-Related Immune Signature via ESTIMATE (PROMISE Model) Predicts Overall Survival of Patients With Glioma

Objective

In the development of immunotherapies in gliomas, the tumor microenvironment (TME) needs to be investigated. We aimed to construct a prognostic microenvironment-related immune signature via ESTIMATE (PROMISE model) for glioma.

Methods

Stromal score (SS) and immune score (IS) were calculated via ESTIMATE for each glioma sample in the cancer genome atlas (TCGA), and differentially expressed genes (DEGs) were identified between high-score and low-score groups. Prognostic DEGs were selected via univariate Cox regression analysis. Using the lower-grcade glioma (LGG) data set in TCGA, we performed LASSO regression based on the prognostic DEGs and constructed a PROMISE model for glioma. The model was validated with survival analysis and the receiver operating characteristic (ROC) in TCGA glioma data sets (LGG, glioblastoma multiforme [GBM] and LGG+GBM) and Chinese glioma genome atlas (CGGA). A nomogram was developed to predict individual survival chances. Further, we explored the underlying mechanisms using gene set enrichment analysis (GSEA) and Cibersort analysis of tumor-infiltrating immune cells between risk groups as defined by the PROMISE model.

Results

We obtained 220 upregulated DEGs and 42 downregulated DEGs in both high-IS and high-SS groups. The Cox regression highlighted 155 prognostic DEGs, out of which we selected 4 genes (CD86, ANXA1, C5AR1, and CD5) to construct a PROMISE model. The model stratifies glioma patients in TCGA as well as in CGGA with distinct survival outcome (P<0.05, Hazard ratio [HR]>1) and acceptable predictive accuracy (AUCs>0.6). With the nomogram, an individualized survival chance could be predicted intuitively with specific age, tumor grade, Isocitrate dehydrogenase (IDH) status, and the PROMISE risk score. ROC showed significant discrimination with the area under curves (AUCs) of 0.917 and 0.817 in TCGA and CGGA, respectively. GSEA between risk groups in both data sets were significantly enriched in multiple immune-related pathways. The Cibersort analysis highlighted four immune cells, i.e., CD 8 T cells, neutrophils, follicular helper T (Tfh) cells, and Natural killer (NK) cells.

Conclusions

The PROMISE model can further stratify both LGG and GBM patients with distinct survival outcomes.These findings may help further our understanding of TME in gliomas and shed light on immunotherapies.

Stromal score as a prognostic factor in primary gastric cancer and close association with tumor immune microenvironment

Background

Gastric cancer remains one of the major causes for tumor‐related deaths worldwide. Our study aimed to provide an understanding of primary gastric cancer and prompt its clinical diagnosis and treatment.

Methods

We integrated the expression profiles and overall survival information of primary gastric cancer in TCGA and GEO database and estimated the stromal score of each sample by the estimate R package. Stromal score and clinicopathologic characteristics associated with overall survival were analyzed by using Cox regression and the Kaplan‐Meier method. Gene set enrichment analysis (GSEA) and KEGG analysis were performed to explore the potential molecular mechanism in TCGA dataset. The relationship between immunotherapy‐associated markers or immune cell types and stromal score was explored by using Pearson correlation analysis.

Results

A total of 796 samples were collected for the analysis. Patients with stromal score‐high showed poor overall survival (P < .01, HR: 1.407, 95% CI: 1.144‐1.731) and identified as an independent prognostic factor. KEGG analysis revealed that stromal score actively involved in diverse tumor‐associated pathways. GSEA analysis also revealed stromal score associated with diverse immune‐related biological processes. Furthermore, stromal score was related with immunotherapy‐associated markers and multiple immune cells.

Conclusion

Our results showed that stromal score could serve as a potential prognostic biomarker in primary gastric cancer and play an important role in the recognition, surveillance, and prognosis of gastric cancer.