Eleven immune-gene pairs signature associated with TP53 predicting the overall survival of gastric cancer: a retrospective analysis of large sample and multicenter from public database

Pterostilbene suppresses gastric cancer proliferation and metastasis by inhibiting oncogenic JAK2/STAT3 signaling: In vitro and in vivo therapeutic intervention

BACKGROUND

Gastric cancer (GC) represents a significant health burden with dire prognostic implications upon metastasis and recurrence. Pterostilbene (PTE) has been proven to have a strong ability to inhibit proliferation and metastasis in other cancers, while whether PTE exhibits anti-GC activity and its potential mechanism remain unclear.

PURPOSE

To explore the efficacy and potential mechanism of PTE in treating GC.

METHODS

We employed a comprehensive set of assays, including CCK-8, EdU staining, colony formation, flow cytometry, cell migration, and invasion assays, to detect the effect of PTE on the biological function of GC cells in vitro. The xenograft tumor model was established to evaluate the in vivo anti-GC activity of PTE. Network pharmacology was employed to predict PTE's potential targets and pathways within GC. Subsequently, Western blotting, immunofluorescence, and immunohistochemistry were utilized to analyze protein levels related to the cell cycle, EMT, and the JAK2/STAT3 pathway.

RESULTS

Our study demonstrated strong inhibitory effects of PTE on GC cells both in vitro and in vivo. In vitro, PTE significantly induced cell cycle arrest at G0/G1 and S phases and suppressed proliferation, migration, and invasion of GC cells. In vivo, PTE led to a dose-dependent reduction in tumor volume and weight. Importantly, PTE exhibited notable safety, leaving mouse weight, liver function, and kidney function unaffected. The involvement of the JAK2/STAT3 pathway in PTE's anti-GC effect was predicted utilizing network pharmacology. PTE suppressed JAK2 kinase activity by binding to the JH1 kinase structural domain and inhibited the downstream STAT3 signaling pathway. Western blotting confirmed PTE's inhibition of the JAK2/STAT3 pathway and EMT-associated protein levels. The anti-GC effect was partially reversed upon STAT3 activation, validating the pivotal role of the JAK2/STAT3 signaling pathway in PTE's activity.

CONCLUSION

Our investigation validates the potent inhibitory effects of PTE on the proliferation and metastasis of GC cells. Importantly, we present novel evidence implicating the JAK2/STAT3 pathway as the key mechanism through which PTE exerts its anti-GC activity. These findings not only establish the basis for considering PTE as a promising lead compound for GC therapeutics but also contribute significantly to our comprehension of the intricate molecular mechanisms underlying its exceptional anti-cancer properties.

DNA damage repair molecular subtype derived immune signature applicable for the prognosis and immunotherapy response prediction in colon cancer

Background

The DNA damage repair (DDR) pathway is one of the pathways of tumor pathogenesis, but its relationship with the immunophenotype has not been clarified in colon cancer (CC).

Methods

We identified the differentially expressed immune-related genes (DEIRGs) between two DDR molecular subtypes, namely, C1 and C2, and used univariate Cox analysis and least absolute shrinkage and selection operator (LASSO) penalized Cox regression analysis to construct the risk score in the training cohort [n=1,009, a combination of The Cancer Genome Atlas (TCGA) and GSE39582]. Regarding the median risk score as the unified cutoff to classify the patients into high- and low-risk groups. Two independent cohorts (GSE17538, n=232; GSE38832, n=122) were used for external validation of the prognostic value of the risk score. The IMvigor210 cohort (n=348) was used to test the predictive value of the risk score for immunotherapy response. Gene set variation analysis (GSVA) and gene set enrichment analysis (GSEA) were performed to discover the underlying mechanism. Immune cell infiltration was quantified by the single sample gene set enrichment analysis (ssGSEA) algorithm.

Results

The high-risk group showed significantly reduced overall survival (OS), disease-specific survival (DSS), disease-free survival (DFS), progression-free survival (PFS), and relapse-free survival (RFS) compared to the low-risk group, and the two groups differed significantly in lymphatic invasion, American Joint Committee on Cancer (AJCC) TNM stage, preoperative carcinoembryonic antigen (CEA) level, etc. The enrichment levels of pathways related to colorectal cancer, epithelial-mesenchymal transition (EMT), angiogenesis, hypoxia, P53, TGF-β, KRAS signaling, etc., were upregulated in the high-risk group, but DDR-related pathways were defective in the high-risk group. The immunophenotypes of the high-risk group tended to be desert and excluded, and the risk score of patients who responded to immunotherapy was significantly lower than that of patients who did not respond to immunotherapy. The higher the infiltration levels of gamma delta T cells (γδ T cells), immature dendritic cells, and T follicular helper (Tfh) cells, the more significant adverse impact on the prognosis of CC patients was exhibited and an obviously positive correlation with the risk score was showed.

Conclusions

An immune gene risk score associated with the DDR molecular subtype was built and verified herein; that is applicable to the prognosis and immunotherapy response prediction in CC.

A novel signature based on CeRNA and immune status predicts prognostic risk and drug sensitivity in gastric cancer patients

Background

At present, there is increasing evidence that both competitive endogenous RNAs (ceRNAs) and immune status in the tumor microenvironment (TME) can affect the progression of gastric cancer (GC), and are closely related to the prognosis of patients. However, few studies have linked the two to jointly determine the prognosis of patients with GC. This study aimed to develop a combined prognostic model based on ceRNAs and immune biomarkers.

Methods

First, the gene expression profiles and clinical information were downloaded from TCGA and GEO databases. Then two ceRNA networks were constructed on the basis of circRNA. Afterwards, the key genes were screened by univariate Cox regression analysis and Lasso regression analysis, and the ceRNA-related prognostic model was constructed by multivariate Cox regression analysis. Next, CIBERSORT and ESTIMATE algorithms were utilized to obtain the immune cell infiltration abundance and stromal/immune score in TME. Furthermore, the correlation between ceRNAs and immunity was found out through co-expression analysis, and another immune-related prognosis model was established. Finally, combining these two models, a comprehensive prognostic model was built and visualized with a nomogram.

Results

The (circRNA, lncRNA)-miRNA-mRNA regulatory network of GC was constructed. The predictive power of ceRNA-related and immune-related prognosis models was moderate. Co-expression analysis showed that the ceRNA network was correlated with immunity. The integrated model of combined ceRNAs and immunity in the TCGA training set, the AUC values of 1, 3, and 5-year survival rates were 0.78, 0.76, and 0.78, respectively; in the independent external validation set GSE62254, they were 0.81, 0.79, and 0.78 respectively; in GSE15459, they were 0.84, 0.88 and 0.89 respectively. Besides, the prognostic score of the comprehensive model can predict chemotherapeutic drug resistance. Moreover, we found that plasma variant translocation 1 (PVT1) and infiltrating immune cells (mast cells) are worthy of further investigation as independent prognostic factors.

Conclusions

Two ceRNA regulatory networks were constructed based on circRNA. At the same time, a comprehensive prognosis model was established, which has a high clinical significance for prognosis prediction and chemotherapy drug selection of GC patients.

Comprehensive analysis of metabolic pathway activity subtypes derived prognostic signature in hepatocellular carcinoma

OBJECTIVE

Metabolic reprogramming is one of the hallmarks of cancer, but metabolic pathway activity-related subtypes of hepatocellular carcinoma (HCC) have not been identified.

METHODS

Based on the quantification results of 41 metabolic pathway activities by gene set variation analysis, the training cohort (n = 609, merged by TCGA and GSE14520) was clustered into three subtypes (C1, C2, and C3) with the nonnegative matrix factorization method. Totally 1371 differentially expressed genes among C1, C2, and C3 were identified, and an 8-gene risk score was established by univariable Cox regression analysis, least absolute shrinkage and selection operator method, and multivariable Cox regression analysis.

RESULTS

C1 had the strongest metabolic activity, good prognosis, the highest CTNNB1 mutation rate, with massive infiltration of eosinophils and natural killer cells. C2 had the weakest metabolic activity, poor prognosis, was younger, was inclined to vascular invasion and advanced stage, had the highest TP53 mutation rate, exhibited a higher expression level of immune checkpoints, accompanied by massive infiltration of regulatory T cells. C3 had moderate metabolic activity and prognosis, the highest LRP1B mutation rate, and a higher infiltration level of neutrophils and macrophages. Internal cohorts (TCGA, n = 370; GSE14520, n = 239), external cohorts (ICGC, n = 231; GSE116174, n = 64), and clinical subgroup validation showed that the risk score was applicable for patients with diverse clinical features and was effective in predicting the prognosis and malignant progression of patients with HCC. Compared with the low-risk group, the high-risk group had a poor prognosis, enhanced cancer stem cell characteristics, activated DNA damage repair, weakened metabolic activity, cytolytic activity, and interferon response.

CONCLUSION

We identified HCC subtypes from the perspective of metabolism-related pathway activity and proposed a robust prognostic signature for HCC.

Immune Subtype Profiling and Establishment of Prognostic Immune-Related lncRNA Pairs in Human Ovarian Cancer

This study collected immune-related genes (IRGs) and used gene expression data from TCGA database to construct a molecular subtype of ovarian cancer (OV) based on immune-related lncRNA gene pairs (IRLnc_GPs). The relationships between molecular subtypes and prognosis and clinical characteristics were further explored. IRGs were acquired from the ImmPort database, and round-robin pairing of immune-related lncRNAs was performed. The NMF algorithm was used to identify molecular subtypes, and the immune score of a single sample was calculated through ESTIMATE, TIMER, ssGSEA, MCPcounter, and CIBERSORT. The relationship between molecular subtypes and immune microenvironments was identified. A hypergeometric test was used to test the lncRNA pairs among the OV molecular subtypes (C1 and C2 subtypes). The BH method was used to screen the different lncRNA pairs, and a predictive risk model was constructed and verified. Finally, correlation analysis between the risk model, immune checkpoint genes, and chemotherapy drugs was carried out. Based on IRLnc_GP to classify 373 OV samples of TCGA, the samples were divided into two subtypes, and the prognosis between the subtypes showed significant differences. The C1 subtype with a poor prognosis was more related to the pathways of tumor occurrence and development. We identified 180 differential lncRNA pairs between subtypes and constructed a prognostic risk model based on 8 IRLnc_GPs. In the independent dataset, the distribution of subtypes in functional modules was different and highly repeatable. There were significant differences in the molecular and clinical characteristics of the subtypes and the drug sensitivity of immunotherapy/chemotherapy. In conclusion, the risk model established based on IRLnc_GP can better evaluate the prognosis of OV samples and can also assess the effects of different drug treatments in the high- and low-risk groups, providing new insights and ideas for the treatment of OV.

A novel immune-related lncRNA pair signature for prognostic prediction and immune response evaluation in gastric cancer: a bioinformatics and biological validation study

Background

Gastric cancer (GC), the most commonly diagnosed cancer worldwide with poor 5-year survival rate in advanced stages. Although immune-related and survival-related biomarkers, which typically comprise aberrantly expressed long non-coding RNAs (lncRNAs) and genes, have been identified, there are no reports of immune-related lncRNA pair (IRLP) signatures for GC.

Methods

In this study, we acquired lncRNA expression profiles from The Cancer Genome Atlas (TCGA) and used the least absolute shrinkage and selection operator (LASSO) Cox proportional hazards model (iteration = 1000) to develop a IRLP prognostic signature. The area under curve (AUC) was used to assess the prognosis predictive power. The multivariate Cox regression analysis was performed to identify whether this signature was an independent prognostic factor. The immune cell infiltration analysis was performed between the two risk groups. Last, molecular experiments were performed to explore LINC01082 is involved in the development of GC.

Results

We acquired lncRNA expression profiles and used the LASSO Cox model to develop an 18-IRLP signature with a strong prognostic predictive power. The 5-year AUC values of the training, validation, and overall TCGA datasets were 0.77, 0.86, and 0.80, respectively. The different prognostic outcomes between the high- and low-risk groups were determined using our 18-IRLP signature. Moreover, our 18-IRLP signature was an independent prognostic factor as per the multivariate Cox regression analysis, and showed better prognostic evaluation than the traditional TNM staging system as well as other clinical features. We also found differences in cancer-associated fibroblast and macrophage M2 infiltration and the expression of PD-L1, CTLA4, LAG3, and HLA were also observed between the two risk groups (P < 0.05). Analysis of biological functions revealed that target genes of the lncRNAs in the IRLP signature were enriched in focal adhesion and regulation of actin cytoskeleton. Finally, as one of significant candidates of IRLP signature, overexpression of LINC01082 suppressed the invasion ability of GC cells as well as PD-L1 expression profiles.

Conclusions

Our novel 18-IRLP signature provides new insights regarding immunological biomarkers, imparts a better understanding of the tumor immune microenvironment, and can be used for predicting prognosis and evaluating immune response in GC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02493-2.

A Five-Gene Signature Associated With DNA Damage Repair Molecular Subtype Predict Overall Survival for Hepatocellular Carcinoma

Background: DNA damage repair (DDR) is an important mechanism for the occurrence and development of hepatocellular carcinoma (HCC), but its impact on prognosis has not been fully understood.

Materials and methods: A total of 904 HCC patients were included in our study, TCGA (n = 370) and GSE14520 (n = 239) were merged into a large-sample training cohort (n = 609). The training cohort was clustered into C1 and C2 based on prognostic DDR-related genes, the differentially expressed genes (DEGs) between C1 and C2 were identified by the Wilcoxon signed-rank test referred to criteria (|log2FC|≥1 and FDR&lt; 0.05). The univariate Cox analysis was used to screen the prognostic-related DEGs, and Lasso penalized Cox regression analysis was used to construct the risk score. The patients were clarified into high- and low-risk groups based on the median risk score. ICGC (n = 231) and GSE116174 (n = 64) cohorts were used for external validation of the risk score’s prognostic value.

Results: The Kaplan–Meier survival analysis showed that the high-risk group had a significantly reduced overall survival (OS) compared to the low-risk group in the three independent cohorts, and the time-dependent ROC curve showed that the five-gene (STMN1, PON1, PLOD2, MARCKSL1, and SPP1) risk score with a high accuracy in predicting OS. The patients with AFP &gt;300 ng/ml, tumor poor differentiation (grade 3–4), micro and macro vascular tumor invasion, advanced stage (AJCC III-IV, BCLC stage B-C, and CLIP score &gt;2) exhibited a higher risk score. Subgroup survival analysis found that the risk score was applicable to patients with different clinical characteristics. GO and KEGG functional enrichment analysis revealed that cell cycle, p53 signaling, TNF signaling-related pathways were upregulated in the high-risk group. The higher infiltration level of activated CD4 T cell, CD56 bright natural killer cell, plasmacytoid dendritic cell, and type 2 T helper cells were found to lead an unfavorable impact on the OS of HCC patients, and these four kinds of immune cells exhibited a higher infiltration level in the high-risk group.

Conclusion: The five-gene risk score proposed in the research may provide new insights into the individualized evaluation of HCC prognosis.

Integrated analysis of 1804 samples of six centers to construct and validate a robust immune-related prognostic signature associated with stromal cell abundance in tumor microenvironment for gastric cancer

Background

Stromal cells in tumor microenvironment could promote immune escape through a variety of mechanisms, but there are lacking research in the field of gastric cancer (GC).

Methods

We identified differential expressed immune-related genes (DEIRGs) between the high- and low-stromal cell abundance GC samples in The Cancer Genome Atlas and GSE84437 datasets. A risk score was constructed basing on univariate cox regression analysis, LASSO regression analysis, and multivariate cox regression analysis in the training cohort (n=772). The median value of the risk score was used to classify patients into groups with high and low risk. We conducted external validation of the prognostic signature in four independent cohorts (GSE26253, n=432; GSE62254, n=300; GSE15459, n=191; GSE26901, n=109) from the Gene Expression Omnibus (GEO) database. The immune cell infiltration was quantified by the CIBERSORT method.

Results

The risk score contained 6 genes (AKT3, APOD, FAM19A5, LTBP3, NOV, and NOX4) showed good performance in predicting 5-year overall survival (OS) rate and 5-year recurrence-free survival (RFS) rate of GC patients. The risk death and recurrence of GC patients growing with the increasing risk score. The patients were clustered into three subtypes according to the infiltration of 22 kinds of immune cells quantified by the CIBERSORT method. The proportion of cluster A with the worst prognosis in the high-risk group was significantly higher than that in the low-risk group; the risk score of cluster C subtype with the best prognosis was significantly lower than that of the other two subtypes.

Conclusion

This study established and validated a robust prognostic model for gastric cancer by integrated analysis 1804 samples of six centers, and its mechanism was explored in combination with immune cell infiltration characterization.

A Ferroptosis and Pyroptosis Molecular Subtype-Related Signature Applicable for Prognosis and Immune Microenvironment Estimation in Hepatocellular Carcinoma

Background: Due to the heterogeneity of tumors and the complexity of the immune microenvironment, the specific role of ferroptosis and pyroptosis in hepatocellular carcinoma (HCC) is not fully understood, especially its impact on prognosis.

Methods: The training set (n = 609, merged by TCGA and GSE14520) was clustered into three subtypes (C1, C2, and C3) based on the prognosis-related genes associated with ferroptosis and pyroptosis. The intersecting differentially expressed genes (DEGs) among C1, C2, and C3 were used in univariate Cox and LASSO penalized Cox regression analysis for the construction of the risk score. The median risk score served as the unified cutoff to divide patients into high- and low-risk groups.

Results: Internal (TCGA, n = 370; GSE14520, n = 239) and external validation (ICGC, n = 231) suggested that the 12-gene risk score had high accuracy in predicting the OS, DSS, DFS, PFS, and RFS of HCC. As an independent prognostic indicator, the risk score could be applicable for patients with different clinical features tested by subgroup (n = 26) survival analysis. In the high-risk patients with a lower infiltration abundance of activated B cells, activated CD8 T cells, eosinophils, and type I T helper cells and a higher infiltration abundance of immature dendritic cells, the cytolytic activity, HLA, inflammation promotion, and type I IFN response in the high-risk group were weaker. The TP53 mutation rate, TMB, and CSC characteristics in the high-risk group were significantly higher than those in the low-risk group. Low-risk patients have active metabolic activity and a more robust immune response. The high- and low-risk groups differed significantly in histology grade, vascular tumor cell type, AFP, new tumor event after initial treatment, main tumor size, cirrhosis, TNM stage, BCLC stage, and CLIP score.

Conclusion: The ferroptosis and pyroptosis molecular subtype-related signature identified and validated in this work is applicable for prognosis prediction, immune microenvironment estimation, stem cell characteristics, and clinical feature assessment in HCC.

Identification and validation of a novel immune-related signature associated with macrophages and CD8 T cell infiltration predicting overall survival for hepatocellular carcinoma

Background

Although the effects of macrophages and CD8 T cell infiltration on clinical outcome have been widely reported, the association between immunity-associated gene with them for hepatocellular carcinoma (HCC) remains unclear.

Materials and methods

The ssGSEA served for quantifying the macrophages as well as CD8 T cell infiltration in the HCC samples obtained from TCGA database. Kaplan–Meier (KM) survival assay was used to determine the associations between macrophages and CD8 T cell infiltration with OS. LASSO Cox regressive method assisted in developing an immune gene signature as well as building a risk score. The performance was evaluated by the time-dependent ROC together with the KM survival analysis. The ICGC database were adopted for external verification. CIBERSORT was applied to the correlation analysis on the immune-related signature and the immunocyte infiltration. GSEA were employed exploring the underlying molecular mechanisms.

Results

Increased CD8+ T cell infiltration was associated with longer OS, whereas a greater infiltration of macrophages was related to shorter OS. There were 398 differential expression genes (DEGs) between the high- and low infiltration groups with the “edgeR” package. An prognostic signature consisted of 10 immune genes was built in TCGA and examined in ICGC. The uniform cutoff (0.927) was adopted for separating sufferers into the high-risk (HR) and low-risk (LR) groups. The ROC curves revealed that the AUC data for this signature predicting 1, 2, 3, 4 and 5 year were all above 0.7 in both TCGA and ICGC cohort and patients in the HR group exhibited an evidently weaker prognostic results compared with the LR group. The HR group presented evidently greater Tregs and Macrophage M0 relative to the LR group, whereas the LR group saw the enrichment of CD8 T cells.

Conclusion

The immune signature associated with macrophages as well as CD8 T cell infiltration has reliable prognostic and predictive value for HCC patients.