Clinical Outcome In Gastric Cancer Research Articles

Predictive value of peripheral blood leukocytes-based methylation of Long non-coding RNA MALAT1 and H19 in the chemotherapy effect and prognosis of gastric cancer

BackgroundThe predictive value of the methylation of Long non-coding RNA (lncRNA) metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) and H19 promoters in peripheral blood leukocytes as a non-invasive biomarker for the chemotherapy effect and prognosis gastric cancer (GC) is unclear. MethodsThe DNA methylation of H19 and MALAT1 between chemotherapy-sensitive and non-sensitive groups and between groups with better and worse survival of GC was compared using regression analyses. Several predictive nomograms were constructed. The genetic alteration of MALAT1 and H19 and the association between gene expression and immune status in GC were also investigated using bioinformatics analysis. ResultsHigher genetic methylations in peripheral blood were noticed in GC groups with poorer survival. The constructed nomograms presented strong predictive values for the chemotherapy effect and 3-year survival of disease-free survival, progression-free survival, and overall survival, with the area under the curve as 0.838, 0.838, 0.912, and 0.925, respectively. Significant correlations between MALAT1 or H19 expression and marker genes of immune checkpoints and immune pathways were noticed. The high infiltration of macrophages in H19-high and low infiltration of CD8+ T cells in MALAT1-high groups were associated with worse survival of GC. ConclusionsMALAT1 and H19 have the potential to predict the chemotherapy response and clinical outcomes of GC.

Single cell and bulk RNA sequencing identifies tumor microenvironment subtypes and chemoresistance-related IGF1+ cancer-associated fibroblast in gastric cancer

BackgroundThe tumor microenvironment (TME) significantly influences prognosis and drug resistance in various tumors, yet its heterogeneity and the mechanisms affecting therapeutic response remain unclear in gastric cancer (GC). MethodsThe heterogenous TME were explored with single-cell RNA-sequencing (scRNA-seq) data of 50 primary GC samples. We then identified four GC TME subtypes with nonnegative matrix factorization (NMF) and constructed a pearson nearest-centroid classifier based on subtype-specific upregulated genes. Genomic features and clinical significance of four subtypes were comprehensively evaluated. We reclustered fibroblasts to identify cancer-associated fibroblast (CAF) subtype associated with poor clinical outcomes. RT-qPCR and double immunofluorescence staining were applied to validate the findings. Cellchat analysis elucidated potential molecular mechanisms of the CAF subtype in GC disease progression and chemotherapy resistance. FindingsThe GC TME exhibited high heterogeneity, influencing chemo-sensitivity. Four TME-based subtypes predicting response to immunotherapy and chemotherapy were identified and validated in 1406 GC patients. Among which, ISG1 subtype displayed higher fibroblasts infiltration and heightened oncogenic pathways, and inferior response to chemotherapy with unfavorable prognosis. Microsatellite instability-high (MSI-H) GCs within four TME subtypes showed immunological heterogeneity. We then reported an IGF1-overexpressing CAF was associated with chemo-resistance and GC recurrence. Cell communication analysis revealed IGF1+ CAF may induce drug-resistant phenotypes in tumor cells through IGF1-α6β4 integrin ligand-receptor binding and activation of EMT biological process. InterpretationWe identified four TME-based subtypes with different clinical outcomes and IGF1+ CAFs contributing to poor clinical outcomes in GC, which might provide guidance for individualized treatment and facilitate the development of novel therapeutic targets.

Co-expression pattern of SLC transporter genes associated with the immune landscape and clinical outcomes in gastric cancer.

Solute carrier (SLC) transporters play a dual role in the occurrence and progression of tumours by acting as both suppressors and promoters. However, the overall impact of SLC transcriptome signatures on the tumour microenvironment, biological behaviour and clinical stratification of gastric cancer has not been thoroughly investigated. Therefore, we comprehensively analysed the expression profiles of the SLC transporter family members to identify novel molecular subtypes in gastric cancer. We identified two distinct SLC subtypes, SLC-S1 and SLC-S2, using non-negative matrix factorization. These subtypes were markedly linked with the tumour microenvironment landscape, biological pathway activation and distinct clinical features of gastric cancer. Furthermore, a new scoring model, the SLC score, was developed to quantify the SLC subtypes. High SLC scores indicated a pattern of 'SLC-S2', characterized by stromal infiltration and activation, poor prognosis and insensitivity to chemotherapy and immunotherapy, but high sensitivity to imatinib. The SLC score could serve as a supplement to the Tumour Node Metastasis (TNM) staging system to guide personalized treatment strategies and predict prognosis for patients with gastric cancer.

Bioinformatics Analysis Reveals the Vital Role of AKR1B1 in Immune Infiltration and Clinical Outcomes of Gastric Cancer.

Infiltrated immune cells are an important constitute of tumor microenvironment, which exert complex effects on gastric cancer (GC) pathogenesis and progression. By using weighted gene co-expression network analysis, integrating the data from The Cancer Genome Atlas-stomach adenocarcinoma and GSE62254, we identify Aldo-Keto Reductase Family 1 Member B (AKR1B1) as a hub gene for immune regulation in GC. Notably, AKR1B1 is associated with higher immune infiltration and worse histologic grade of GC. In addition, AKR1B1 is an independent factor for predicting the survival rate of GC patients. In vitro experiments further demonstrated that AKR1B1-overexpressed THP-1-derived macrophages promoted the proliferation and migration of GC cells. Taken together, AKR1B1 plays an important role in GC progression by regulating immune microenvironment, which could be a biomarker for predicting GC prognosis as well as a potential therapeutic target for GC treatment.

O002 Expression of MLN64 (lymph node metastasis 64) gene transcript in clinical gastric cancer and relationship with the ERBB4

Abstract Introduction MLN64 (Lymph Node Metastasis 64, or STARD3 (StAR Related Lipid Transfer Domain Containing 3)) has a role in the progression of certain cancers including breast and prostate cancers and connected to the pattern of ERBB2 (Her2). The present study explored the expression of MLN64 in clinical gastric cancer in connection with the ERBB family. Methods A gastric cancer cohort (n=322) was tested for the expression profile of MLN64 along with the ERBB family members. Comparisons were made between clinical, pathological subgroups and the clinical outcome together with ERBB expression status. Results Gastric tumours expressed significantly raised levels of MLN64 than normal tissues (p=0.0012) and showed a stepwise increase with increased tumour grade and TNM staging. However, there was no significant difference between node negative and node positive tumours. MLN64 alone had a weak value in evaluating the overall (OS) and disease free (DFS) survival. Amongst the four ERBB family members, MLN64 had a significant correlation with ERBB4 (Her4) (p=0.004). Stratification of patients by ERBB4 expression status indicates that in patients with low ERBB4 tumours, MLN64 presents a significant factor in assessing both disease free (DFS) p=0.027) and overall survival (OS) (p=0.048). Similarly, MLN64 expression identified a subpopulation of node positive patients with a most adverse clinical outcome (p=0.009 for DFS and p=0.048 for OS). Conclusion MLN64 expression is an indicator for the disease progression and clinical outcome in gastric cancer. Together with the expression pattern of ERBB4, it makes a prognostic factor for the patients.

Combined Inhibition of KIF11 and KIF15 as an Effective Therapeutic Strategy for Gastric Cancer.

Novel therapeutic strategies are urgently required to improve clinical outcomes of gastric cancer (GC). KIF15 cooperates with KIF11 to promote bipolar spindle assembly and formation, which is essential for proper sister chromatid segregation. Therefore, we speculated that the combined inhibition of KIF11 and KIF15 might be an effective strategy for GC treatment. Hence, to test this hypothesis, we aimed to evaluate the combined therapeutic effect of KIF15 inhibitor KIF15- IN-1 and KIF11 inhibitor ispinesib in GC. We validated the expression of KIF11 and KIF15 in GC tissues using immunohistochemistry and immunoblotting. Next, we determined the effects of KIF11 or KIF15 knockout on the proliferation of GC cell lines. Finally, we investigated the combined effects of the KIF11 and KIF15 inhibitors both in vitro and in vivo. KIF11 and KIF15 were overexpressed in GC tissues than in the adjacent normal tissues. Knockout of either KIF11 or KIF15 inhibited the proliferative and clonogenic abilities of GC cells. We found that the KIF15 knockout significantly increased ispinesib sensitivity in GC cells, while its overexpression showed the opposite effect. Further, using KIF15-IN-1 and ispinesib together had a synergistic effect on the antitumor proliferation of GC both in vitro and in vivo. This study shows that the combination therapy of inhibiting KIF11 and KIF15 might be an effective therapeutic strategy against gastric cancer.

Characterization of glycometabolism and tumor immune microenvironment for predicting clinical outcomes in gastric cancer.

Recent evidence demonstrates that the reprogramming of energy metabolism can interact with the tumor immune microenvironment, thereby participating in the progression of cancer. In this study, multi-omics data of 2471 gastric cancer samples were used to identify tumor glycometabolism and its correlation with tumor immune microenvironment. A series of bioinformatic approaches were performed to establish a scoring system to predict the survival and response of chemotherapy and immunotherapy. Three glycometabolic subtypes and two immune clustering subgroups of gastric cancer were determined. We further established a Gluco-Immune Scoring system to quantify the cancer glycometabolic status and immune infiltration of individual patients. Patients with low Gluco-Immune Score were sensitive to adjuvant chemotherapy, while patients with high Gluco-Immune Score may benefit from immunotherapy. Our results indicate that in gastric cancer, the assessment of tumor glucose metabolism and immune microenvironment has application value for the prediction of curative effects and the formulation of combined treatment strategies.

Development and Evaluation of a Novel Cuproptosis-Related lncRNA Signature for Gastric Cancer Prognosis.

According to a growing body of research, long noncoding RNAs (lncRNAs) participate in the progress of gastric cancer (GC). Cuproptosis is a distinct kind of programmed cell death, separating it from several other forms of programmed cell death that may be caused by genetic programming. Consequently, it is crucial to examine cuproptosis-related lncRNAs (CRLs) prognostic importance for the prognosis and treatment response in GC. The Cancer Genome Atlas (TCGA) database was used to retrieve RNA-seq data, pertinent clinical information, and somatic mutation data. A list of cuproptosis-related genes (CRGs) was obtained from prior work. We can distinguish prognostic CRLs using coexpression and univariate Cox analysis. Then, using CRLs, we developed a risk prediction model using multivariate Cox regression analysis and the least absolute shrinkage selection operator (LASSO) technique. To evaluate the diagnostic accuracy of this model, a Kaplan-Meier (K-M) survival analysis and a receiver operating characteristic (ROC) analysis were used. Moreover, the relationships between the risk model and immunological function, somatic mutation, and drug sensitivity were also investigated. Using the multivariate Cox analysis technique, we developed a signature based on cuproptosis-related four lncRNAs. We then classified patients into high-risk and low-risk groups based on the likelihood of unfavorable outcomes. The model was subjected to further testing, including K-M survival analysis, ROC analysis, and multivariate Cox regression analysis, all of which proved the model's exceptional robustness and predictive capacity. In addition, a nomogram that has a strong capacity for prediction ability was built. This nomogram included age, gender, clinical grade, pathologic stage, T stage, and risk score. Furthermore, we discovered substantial disparities in immune function and the number of mutations carried by tumors between the high-risk and low-risk groups. Moreover, this research also found that the IC50 values for 27 chemotherapeutic drugs varied widely across patients within high- and low-risk groups. The proposed 4-CRLs signature is a promising biomarker to predict clinical outcomes in GC.

Lipid metabolism characterization in gastric cancer identifies signatures to predict prognostic and therapeutic responses.

Purpose: Increasing evidence has elucidated the significance of lipid metabolism in predicting therapeutic efficacy. Obviously, a systematic analysis of lipid metabolism characterizations of gastric cancer (GC) needs to be reported. Experimental design: Based on two proposed computational algorithms (TCGA-STAD and GSE84437), the lipid metabolism characterization of 367 GC patients and its systematic relationship with genomic characteristics, clinicopathologic features, and clinical outcomes of GC were analyzed in our study. Differentially expressed genes (DEGs) were identified based on the lipid metabolism cluster. At the same time, we applied single-factor Cox regression and random forest to screen signature genes to construct a prognostic model, namely, the lipid metabolism score (LMscore). Next, we deeply explored the predictive value of the LMscore for GC. To verify the specific changes in lipid metabolism, a total of 90 serum, 30 tumor, and non-tumor adjacent tissues from GC patients, were included for pseudotargeted metabolomics analysis via SCIEX triple quad 5500 LC-MS/MS system. Results: Five lipid metabolism signature genes were identified from a total of 3,104 DEGs. The LMscore could be a prognosticator for survival in different clinicopathological GC cohorts. As well, the LMscore was identified as a predictive biomarker for responses to immunotherapy and chemotherapeutic drugs. Additionally, significant changes in sphingolipid metabolism and sphingolipid molecules were discovered in cancer tissue from GC patients by pseudotargeted metabolomics. Conclusion: In conclusion, multivariate analysis revealed that the LMscore was an independent prognostic biomarker of patient survival and therapeutic responses in GC. Depicting a comprehensive landscape of the characteristics of lipid metabolism may help to provide insights into the pathogenesis of GC, interpret the responses of gastric tumors to therapies, and achieve a better outcome in the treatment of GC. In addition, significant alterations of sphingolipid metabolism and increased levels of sphingolipids, in particular, sphingosine (d16:1) and ceramide, were discovered in GC tissue by lipidome pseudotargeted metabolomics, and most of the sphingolipid molecules have the potential to be diagnostic biomarkers for GC.

128P Survival outcome of gastric cancer patients in a tertiary care center: A 10-year single center retrospective study

Gastric cancer is the fifth most frequently diagnosed cancer and the third leading cause of cancer-related deaths in the world. Gastric cancer still constitutes a major global health problem in Asian countries; however there is limited data of the clinical outcome of gastric cancer in the Philippines. Thus this study aimed to determine the survival outcome of gastric adenocarcinoma patients in our institution.

Combined high NEDD9 expression and E-cadherin loss correlate with poor clinical outcome in gastric cancer.

Neural precursor cell expressed developmentally downregulated 9 (NEDD9) is a member of the Cas family. Previous studies have revealed that NEDD9 coordinates the focal adhesion kinase and Src signaling cascades that are involved in integrin-dependent adhesion and migration, invasion, cell apoptosis and life cycle, and survival, which may play a role in epithelial-mesenchymal transformation. The aim of this study was to analyze the expression of NEDD9 and E-cadherin in gastric cancer (GC) and evaluate their clinical significance. NEDD9 and E-cadherin expression was analyzed with immunohistochemistry using tissue microarray technique in 435 GC patients who underwent gastrectomy. The NEDD9 expression level was defined by the combination score, which was determined by multiplying the staining intensity score and the proportion score (≥5; NEDD9-high, <5; NEDD9-low). E-cadherin loss was defined as a total loss of staining. The clinicopathologic parameters, overall survival, and disease-free survival rates were analyzed according to the NEDD9 and E-cadherin expression status. The combined NEDD9 and E-cadherin expression status correlated with lymphatic invasion (P=0.001), vascular invasion (P=0.020), and T stage (P=0.001). Combined high NEDD9 expression and loss of E-cadherin expression status had a worse overall survival rate (P<0.001) and served as a poor prognostic factor (Hazard ratio 2.49, 95% CI 1.25-5, P=0.01). Immunohistochemical staining for NEDD9 and E-cadherin may function as a candidate prognostic marker for gastric cancer in everyday practice, especially when applied in combination.

Analyses of the expression and prognosis of ILDR1 in human gastric cancer

The worldwide mortality rate of gastric cancer worldwide remains high. Immunoglobulin-like domain containing receptor 1 (ILDR1) belongs to an evolutionarily conserved protein family, and little is known about this gene in gastric cancer. In this paper, we analyzed the expression of ILDR1 and its relationship with clinical outcomes in gastric cancer using publicly available databases. ONCOMINE, GEPIA2, UALCAN, Kaplan–Meier Plotter, cBioPortal, GeneMANIA and LinkedOmics databases were used to analyze the expression, prognostic values, mutations and functional networks of ILDR1 in gastric cancer. We observed that ILDR1 was overexpressed in gastric cancer than in normal tissues. ILDR1 expression was significantly higher in patients with gastric cancer than in normal controls during subgroup analysis based on cancer stage, patient’s race, sex, age, tumor grade, H. pylori infection, histological subtype, and nodal metastasis status. Survival analysis showed that upregulation of ILDR1 expression was significantly associated with poor prognosis. Genomic alterations in ILDR1 were analyzed using cBioPortal, protein–protein interaction (PPI) networks were constructed using GeneMANIA and the co-expressed genes, gene ontology, and pathways of ILDR1 were determined using the LinkedOmics web tool. ILDR1 showed significant differences in expression between gastric cancer and normal tissues and, thus, may be a promising prognostic biomarker for gastric cancer.

Bioinformatics Analysis Based on TCGA: MUC16 Mutation Correlates with Clinical Outcome in Gastric Cancer.

The prognosis of gastric cancer (GC) is difficult to predict due to the disease's complex genetic and phenotypic characteristics. MUC16 has been reported to be involved in the progression of several tumors. In this study, we aimed to explore whether MUC16 mutation had any impact on the prognosis or treatments of GC patients. Additionally, this analysis uncovered possible critical pathways related with these systems. On the cBioPortal, we were able to locate the pertinent data of patients with MUC16 mutations. And then, GSEA analysis identified differences in mRNA levels between mutant and wild-type MUC16 patients in terms of biological function annotation and pathways. The KEGG and GO analyses were also performed using the differentially expressed genes (DEGs). There were 139 individuals with GC who had the MUC16 mutation, which accounts for 32 percent, and the remaining patients had the MUC16 wild type. Survival assays revealed that patients with the MUC16 mutation had longer overall survival and disease-free survival. GSEA analysis revealed that cell cycle, cysteine and methionine metabolism, Huntington's disease, one carbon pool by folate, pyrimidine metabolism, pyruvate metabolism, RNA degradation, spliceosome, and valine leucine and isoleucine degradation were distinctly enriched in patients with MUC16 mutation type. Moreover, we identified 323 DEGs. Among them, 162 genes were upregulated, and 161 genes were downregulated. GO and KEGG assays indicated DEGs as enriched in pancreatic secretion, neuroactive ligand-receptor interaction, protein digestion and absorption, fat digestion and absorption, and glycerolipid metabolism. Overall, our data revealed that the MUC16 mutation in GC may affect the development of patients by altering several genes and pathways, indicating the importance of MUC16 mutation in the treatments of GC on an individual basis.

Identifying immune cells-related phenotype to predict immunotherapy and clinical outcome in gastric cancer.

BackgroundThe tumor microenvironment is mainly composed of tumor-infiltrating immune cells (TIICs), fibroblast, extracellular matrix, and secreted factors. TIICs are often associated with sensitivity to immunotherapy and the prognosis of multiple cancers, yet the predictive role of individual cells on tumor prognosis is limited.MethodsBased on single-sample gene set enrichment analysis, we combined three Gene Expression Omnibus (GEO) cohorts to build a TIIC model for risk stratification and prognosis prediction. The performance of the TIIC model was validated using our clinical cohort and the TCGA cohort. To assess the predictive power of the TIIC model for immunotherapy, we plotted the receiver operating characteristic curve with the IMvigor210 and GSE135222 cohorts.ResultsChemokines, tumor-infiltrating immune cells, and immunomodulators differed between the two TIIC groups. The TIIC model was vital for predicting the outcome of immunotherapy. In our clinical samples, we verified that the expression levels of PD-1 and PD-L1 were higher in the low TIIC score group than in the high TIIC score group, both in the tumor and stroma.ConclusionsCollectively, the TIIC model could provide a novel idea for immune cell targeting strategies in gastric cancer and predict the survival outcome of patients.

Identification of specific role of SNX family in gastric cancer prognosis evaluation

We here perform a systematic bioinformatic analysis to uncover the role of sorting nexin (SNX) family in clinical outcome of gastric cancer (GC). Comprehensive bioinformatic analysis were realized with online tools such as TCGA, GEO, String, Timer, cBioportal and Kaplan–Meier Plotter. Statistical analysis was conducted with R language or Perl, and artificial neural network (ANN) model was established using Python. Our analysis demonstrated that SNX4/5/6/7/8/10/13/14/15/16/20/22/25/27/30 were higher expressed in GC, whereas SNX1/17/21/24/33 were in the opposite expression profiles. GSE66229 was employed as verification of the differential expression analysis based on TCGA. Clustering results gave the relative transcriptional levels of 30 SNXs in tumor, and it was totally consistent to the inner relevance of SNXs at mRNA level. Protein–Protein Interaction map showed closely and complex connection among 33 SNXs. Tumor immune infiltration analysis asserted that SNX1/3/9/18/19/21/29/33, SNX1/17/18/20/21/29/31/33, SNX1/2/3/6/10/18/29/33, and SNX1/2/6/10/17/18/20/29 were strongly correlated with four kinds of survival related tumor-infiltrating immune cells, including cancer associated fibroblast, endothelial cells, macrophages and Tregs. Kaplan–Meier survival analysis based on GEO presented more satisfactory results than that based on TCGA-STAD did, and all the 29 SNXs were statistically significant, SNX23/26/28 excluded. SNXs alteration contributed to microsatellite instability (MSI) or higher level of MSI-H (hyper-mutated MSI or high level of MSI), and other malignancy encompassing mutation of TP53 and ARID1A, as well as methylation of MLH1.The multivariate cox model, visualized as a nomogram, performed excellently in patients risk classification, for those with higher risk-score suffered from shorter overall survival (OS). Compared to previous researches, our ANN models showed a predictive power at a middle-upper level, with AUC of 0.87/0.72, 0.84/0.72, 0.90/0.71 (GSE84437), 0.98/0.66, 0.86/0.70, 0.98/0.71 (GSE66229), 0.94/0.66, 0.83/0.71, 0.88/0.72 (GSE26253) corresponding to one-, three- and five-year OS and recurrence free survival (RFS) estimation, especially ANN model built with GSE66229 including exclusively SNXs as input data. The SNX family shows great value in postoperative survival evaluation of GC, and ANN models constructed using SNXs transcriptional data manifesting excellent predictive power in both OS and RFS prediction works as convincing verification to that.

Relationship Between CNVs and Immune Cells Infiltration in Gastric Tumor Microenvironment

Gastric cancer (GC) is a highly fatal and common malignancy of the digestive system. Recent therapeutic advancements have significantly improved the clinical outcomes in GC, but due to the unavailability of suitable molecular targets, a large number of patients do not respond to the immune checkpoint inhibitors (ICI) therapy. To identify and validate potential therapeutic and prognostic targets of gastric cancer, we used the “inferCNV” R package for analyzing single-cell sequencing data (GSE112302) of GC and normal epithelial cells. First, by using LASSO, we screened genes that were highly correlated with copy number variations (CNVs). Therefrom, five gene signature (CPVL, DDC, GRTP1, ONECUT2, and PRSS21) was selected by cross-validating the prognosis and risk management with the GC RNA-seq data obtained from GEO and TCGA. Moreover, the correlation analyses between CNVs of these genes and immune cell infiltration in gastric cancer identified CPVL as a potential prognostic marker. Finally, CPVL showed high expression in gastric cancer samples and cell lines, then siRNA-mediated silencing of CPVL expression in gastric cancer cells showed significant proliferation arrest in MGC803 cells. Here, we conclude that CNVs are key regulators of the immune cells infiltration in gastric TME as well as cancer development, and CPVL could potentially be used as a prognostic and therapeutic marker in gastric cancer.

Integrated Analysis of miR-7-5p-Related ceRNA Network Reveals Potential Biomarkers for the Clinical Outcome of Gastric Cancer.

Gastric cancer (GC) is the second leading cause of tumor-associated death and the fourth most commonly seen tumor across the world. Abnormal ncRNAs have been verified to be involved in potential metastasis via modulating epithelial-to-mesenchymal transition progression and are vital for the progression of cancers. Tumor-infiltrating immune cells (TICs) are a vital indicator of whether cancer patients will benefit from immunotherapy. Nonetheless, the association between ceRNAs and immune cells remained largely unclear. We used the ceRNA network combined with TICs for the prediction of the clinical outcome of GC patients based on TCGA datasets. The percentage of immunocytes in GC was speculated by the use of CIBERSORT. Via Lasso and multivariate assays, prognostic models were established applying survival-related genes and immune cells. Nomograms were developed, and the accuracy of the nomograms was determined using calibration curves. The association between ceRNAs and TICs was validated by the use of integration analysis. In this study, there were 2219 mRNAs (1308 increased and 911 decreased), 171 lncRNAs (51 decreased and 120 increased), and 123 miRNAs (55 decreased and 68 increased) differentially expressed between tumor groups and nontumor groups. Five lncRNAs, six miRNAs, and 64 mRNAs were used for ceRNA network construction. Eight genes including LOX, SPARC, MASTL, PI15, BMPR1B, ANKRD13B, PVT1, and miR-7-5p were applied for the development of the prognostic model. Survival assays suggested that tumor cases with high risk exhibited a shorter overall survival. In addition, we included T-cell CD4 memory activated, monocytes, and neutrophils for the development of a prognosis model. Eventually, our team demonstrated the possible associations between the ceRNA prognosis model and prognostic model based on immune cells. To sum up, the ceRNA network could be used for gene regulation and predict clinical outcomes of GC patients.

Dysregulation of let-7c-5p/Tyrosyl-DNA phosphodiesterase 1 axis indicates an unfavorable outcome in gastric cancer

Introduction Tyrosyl-DNA phosphodiesterase 1 (TDP1) can repair oxidative damage-caused 3′-phosphoglycolates and promote cancer progression. However, the clinical significance of TDP1 and its correlation with microRNAs (miRNAs) in gastric cancer (GC) remains unknown. Methods The relationship of TDP1 or let-7c-5p with the clinical outcomes of GC was determined by a tissue microarray and TCGA dataset. Cell viability and invasion were assessed by MTT and Transwell assays. Pearson correlation analysis, luciferase gene report, qRT-PCR, and Western blot analyses were used to analyze the interaction between TDP1 and let-7c-5p in GC tissues and cells. Results We found that TDP1 expression was elevated in GC tissues and associated with the dysregulation of let-7c-5p. Knockdown of TDP1 inhibited GC cell proliferation and invasion. let-7c-5p could be found to bind with TDP1, reduce its expression levels, and represent a predictive marker in GC. Conclusion Our findings demonstrated that dysregulation of let-7c-5p/TDP1 axis could predict a poor prognosis in GC.

Association of CSMD1 with Tumor Mutation Burden and Other Clinical Outcomes in Gastric Cancer.

BackgroundImmunotherapy is considered as a powerful and promising clinical approach for the treatment of gastric cancer (GC). However, it is still challenging to precisely screen patients who potentially benefit from immune checkpoint therapy (ICT). Identification of potential biomarkers for selecting patients sensitive to immunotherapy was urgently needed.MethodsPublic sequence data and corresponding clinical data were used to explore the potential biomarkers for immunotherapy.ResultsWe found that CSMD1 is the most frequently mutated gene and its mutation is highly correlated with prognosis in gastric cancer patients. Interestingly, patients with mutated CSMD1 exhibit a high mutation burden and upregulated PDL1 expression. The ratio of microsatellite instability (MSI) in the CSMD1 mutation cohort was higher than that in the cohort without CSMD1 mutation. Furthermore, patients with CSMD1 mutation have been found to possess a higher number of activated CD4+ T cells and neoantigens.ConclusionCSMD1 mutation may act as a novel biomarker for assessing the survival and immune therapy response in patients with gastric cancer.

Down-Regulated CLDN10 Predicts Favorable Prognosis and Correlates With Immune Infiltration in Gastric Cancer.

Background: CLDN10, an important component of the tight junctions of epithelial cells, plays a crucial role in a variety of tumors. The effect of CLDN10 expression in gastric cancer, however, has yet to be elucidated. Methods: Differential expression of CLDN10 at the mRNA and protein levels was evaluated using Oncomine, ULCAN, HPA and TIMER2.0 databases. Real-time polymerase chain reaction (RT-PCR) was utilized to further verify the expression of CLDN10 in vitro. Correlations between CLDN10 expression and clinical outcomes of gastric cancer were explored by Kaplan-Meier Plotter. Gene set enrichment analysis (GSEA) and protein-protein interaction (PPI) were performed via LinkedOmics and GeneMANIA. The correlations between CLDN10 expression and immune cell infiltration and somatic copy number alternations (SCNA) in gastric cancer were explored by TIMER2.0 and GEPIA2.0. Results: CLDN10 expression was lower in gastric cancer compared to adjacent normal tissues, and associated with better prognosis. CLDN10 also showed significant differences at different T stages, Lauren classification, treatments and HER2 status. PPI and GSEA analysis showed that CLDN10 might be involved in signal transmission, transmembrane transport and metabolism. In some major immune cells, low expression of CLDN10 was associated with increased levels of immune cell infiltration. In addition, it was found that different SCNA status in CLDN10 might affect the level of immune cell infiltration. Furthermore, the expression of CLDN10 was significantly associated with the expression of several immune cell markers, especially B cell markers, follicular helper T cell (Tfh) markers and T cell exhaustion markers. Conclusion: Down-regulated CLDN10 was associated with better overall survival (OS) in gastric cancer. And CLDN10 may serve as a potential prognostic biomarker and correlate to immune infiltration levels in gastric cancer.