Prognostic value of a 25-gene assay in patients with gastric cancer after curative resection

Identification of differentially expressed mRNAs as novel predictive biomarkers for gastric cancer diagnosis and prognosis

BACKGROUND

Gastric cancer (GC) has a high mortality rate worldwide. Despite significant progress in GC diagnosis and treatment, the prognosis for affected patients still remains unfavorable.

AIM

To identify important candidate genes related to the development of GC and identify potential pathogenic mechanisms through comprehensive bioinformatics analysis.

METHODS

The Gene Expression Omnibus database was used to obtain the GSE183136 dataset, which includes a total of 135 GC samples. The limma package in R software was employed to identify differentially expressed genes (DEGs). Thereafter, enrichment analyses of Gene Ontology (GO) terms and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were performed for the gene modules using the clusterProfile package in R software. The protein-protein interaction (PPI) networks of target genes were constructed using STRING and visualized by Cytoscape software. The common hub genes that emerged in the cohort of DEGs that was retrieved from the GEPIA database were then screened using a Venn Diagram. The expression levels of these overlapping genes in stomach adenocarcinoma samples and non-tumor samples and their association with prognosis in GC patients were also obtained from the GEPIA database and Kaplan-Meier curves. Moreover, real-time quantitative polymerase chain reaction (RT-qPCR) and western blotting were performed to determine the mRNA and protein levels of glutamic-pyruvic transaminase (GPT) in GC and normal immortalized cell lines. In addition, cell viability, cell cycle distribution, migration and invasion were evaluated by cell counting kit-8, flow cytometry and transwell assays. Furthermore, we also conducted a retrospective analysis on 70 GC patients diagnosed and surgically treated in Wenzhou Central Hospital, Dingli Clinical College of Wenzhou Medical University, The Second Affiliated Hospital of Shanghai University between January 2017 to December 2020. The tumor and adjacent normal samples were collected from the patients to determine the potential association between the expression level of GPT and the clinical as well as pathological features of GC patients.

RESULTS

We selected 19214 genes from the GSE183136 dataset, among which there were 250 downregulated genes and 401 upregulated genes in the tumor samples of stage III-IV in comparison to those in tumor samples of stage I-II with a P-value < 0.05. In addition, GO and KEGG results revealed that the various upregulated DEGs were mainly enriched in plasma membrane and neuroactive ligand-receptor interaction, whereas the downregulated DEGs were primarily enriched in cytosol and pancreatic secretion, vascular smooth muscle contraction and biosynthesis of the different cofactors. Furthermore, PPI networks were constructed based on the various upregulated and downregulated genes, and there were a total 15 upregulated and 10 downregulated hub genes. After a comprehensive analysis, several hub genes, including runt-related transcription factor 2 (RUNX2), salmonella pathogenicity island 1 (SPI1), lysyl oxidase (LOX), fibrillin 1 (FBN1) and GPT, displayed prognostic values. Interestingly, it was observed that GPT was downregulated in GC cells and its upregulation could suppress the malignant phenotypes of GC cells. Furthermore, the expression level of GPT was found to be associated with age, lymph node metastasis, pathological staging and distant metastasis (P < 0.05).

CONCLUSION

RUNX2, SPI1, LOX, FBN1 and GPT were identified key hub genes in GC by bioinformatics analysis. GPT was significantly associated with the prognosis of GC, and its upregulation can effectively inhibit the proliferative, migrative and invasive capabilities of GC cells.

Establishment of a 7-gene expression panel to improve the prognosis classification of gastric cancer patients

Gastric cancer (GC) ranks fifth in incidence and fourth in mortality worldwide. The high death rate in patients with GC requires new biomarkers for improving survival estimation. In this study, we performed a transcriptome-based analysis of five publicly available cohorts to identify genes consistently associated with prognosis in GC. Based on the ROC curve, patients were categorized into high and low-expression groups for each gene using the best cutoff point. Genes associated with survival (AUC > 0.5; univariate and multivariate Cox regressions, p < 0.05) were used to model gene expression-based scores by weighted sum using the pooled Cox β regression coefficients. Cox regression (p < 0.05), AUC > 0.5, sensitivity > 0.5, and specificity > 0.5 were considered to identify the best scores. Gene set enrichment analysis (KEGG, REACTOME, and Gene Ontology databases), as well as microenvironment composition and stromal cell signatures prediction (CIBERSORT, EPIC, xCell, MCP-counter, and quanTIseq web tools) were performed. We found 11 genes related to GC survival in the five independent cohorts. Then, we modeled scores by calculating all possible combinations between these genes. Among the 2,047 scores, we identified a panel based on the expression of seven genes. It was named GES7 and is composed of CCDC91, DYNC1I1, FAM83D, LBH, SLITRK5, WTIP, and NAP1L3 genes. GES7 features were validated in two independent external cohorts. Next, GES7 was found to recategorize patients from AJCC TNM stages into a best-fitted prognostic group. The GES7 was associated with activation of the TGF-β pathway and repression of anticancer immune cells. Finally, we compared the GES7 with 30 previous proposed scores, finding that GES7 is one of the most robust scores. As a result, the GES7 is a reliable gene-expression-based signature to improve the prognosis estimation in GC.

Bioinformatics Prediction and Machine Learning on Gene Expression Data Identifies Novel Gene Candidates in Gastric Cancer

Gastric cancer (GC) is one of the five most common cancers in the world and unfortunately has a high mortality rate. To date, the pathogenesis and disease genes of GC are unclear, so the need for new diagnostic and prognostic strategies for GC is undeniable. Despite particular findings in this regard, a holistic approach encompassing molecular data from different biological levels for GC has been lacking. To translate Big Data into system-level biomarkers, in this study, we integrated three different GC gene expression data with three different biological networks for the first time and captured biologically significant (i.e., reporter) transcripts, hub proteins, transcription factors, and receptor molecules of GC. We analyzed the revealed biomolecules with independent RNA-seq data for their diagnostic and prognostic capabilities. While this holistic approach uncovered biomolecules already associated with GC, it also revealed novel system biomarker candidates for GC. Classification performances of novel candidate biomarkers with machine learning approaches were investigated. With this study, AES, CEBPZ, GRK6, HPGDS, SKIL, and SP3 were identified for the first time as diagnostic and/or prognostic biomarker candidates for GC. Consequently, we have provided valuable data for further experimental and clinical efforts that may be useful for the diagnosis and/or prognosis of GC.

Transcriptomic profiling of single circulating tumor cells provides insight into human metastatic gastric cancer

Transcriptome analysis of circulating tumor cells (CTCs), which migrate into blood vessels from primary tumor tissues, at the single-cell level offers critical insights into the biology of metastasis and contributes to drug discovery. However, transcriptome analysis of single CTCs has only been reported for a limited number of cancer types, such as multiple myeloma, breast, hepatocellular, and prostate cancer. Herein, we report the transcriptome analysis of gastric cancer single-CTCs. We utilized an antigen-independent strategy for CTC isolation from metastatic gastric cancer patients involving a size-dependent recovery of CTCs and a single cell isolation technique. The transcriptomic profile of single-CTCs revealed that a majority of gastric CTCs had undergone epithelial-mesenchymal transition (EMT), and indicated the contribution of platelet adhesion toward EMT progression and acquisition of chemoresistance. Taken together, this study serves to employ CTC characterization to elucidate the mechanisms of chemoresistance and metastasis in gastric cancer.

hsa_circ_0060975 is highly expressed and predicts a poor prognosis in gastric cancer

Gastric cancer (GC) is the fifth most common cancer and GC has a high mortality rate worldwide. Circular (circ) RNAs serve an important role in cancer. The present study aimed to investigate the expression level of hsa_circ_0060975 in gastric cancer (GC) and to determine the clinical pathological significance of hsa_circ_0060975 in patients with GC. Reverse transcription-quantitative PCR was used to detect expression level of hsa_circ_0060975 in 192 GC and adjacent non-cancerous gastric tissues, in GC cell lines (MKN-45, HGC27 and AGS) and a human gastric epithelium cell line (GES-1), as well as in plasma samples from 126 patients with GC and 92 healthy volunteers. All plasma and tissue samples of were obtained from The First Affiliated Hospital of Anhui Medical University (Hefei, China). The relationship between hsa_circ_0060975 expression and clinical pathological factors was analyzed using the χ² test. The diagnostic value of hsa_circ_0060975 was analyzed using the receiver operating characteristic curve (ROC curve), while the Kaplan-Meier method was used to analyze the relationship of hsa_circ_0060975 expression with the survival of patients with GC as determined by log-rank tests. Univariate and multivariate Cox regression analyses were used to identify the prognostic factors, including hsa_circ_0060975 expression and clinical pathological factors. In addition, the potential function of hsa_circ_0060975 was evaluated via bioinformatics analysis. The expression level of hsa_circ_0060975 was higher in GC tissues compared with adjacent non-cancerous gastric tissues, GC cell lines compared with GES-1 and plasma samples from patients with GC compared with plasma samples from healthy volunteers. In addition, higher hsa_circ_0060975 expression was associated with histological grade, pathological stage and tumor (T) classification in GC tissues and plasma samples (P<0.05). The area under the ROC curves of hsa_circ_0060975, the combination with hsa_circ_0060975 and carcinoembryonic antigen (CEA) or CEA alone were 0.804 (sensitivity, 0.746; specificity, 0.783; P<0.001); 0.931 (sensitivity, 0.937; specificity, 0.870; P<0.001) and 0.924 (sensitivity, 0.937; sspecificity, 0.804; P<0.001) respectively. The Kaplan-Meier survival analysis revealed that the overall survival (OS) and disease-free survival (DFS) time of patients with higher hsa_circ_0060975 expression were shorter compared with those in patients with lower hsa_circ_0060975 expression. Univariate and multivariate Cox regression analyses in OS and DFS time determined that the expression level of hsa_circ_0060975, histological grade and pathological stage were independent prognostic factors for patients with GC. In addition, the bioinformatics analysis results suggested that the abnormal expression of hsa_circ_0060975 may serve an important role in tumorigenesis. Hence, hsa_circ_0060975 expression may be an independent prognostic factor for patients with GC and may be a potential marker for biological malignancy.

Genome-Scale Analysis Identified NID2, SPARC, and MFAP2 as Prognosis Markers of Overall Survival in Gastric Cancer

BACKGROUND Gastric cancer is the most common gastrointestinal tumor, and the rates of recurrence and metastasis are high. Research results on molecular biomarkers used for prognosis of gastric cancer remain inconclusive. This study aimed to explore the gene expression module of gastric cancer and to determine potential prognostic biomarkers. MATERIAL AND METHODS Three microarray datasets (GSE13911, GSE79973, and GSE29272) from Gene Expression Omnibus (GEO), including 206 pairs of gastric tumors and adjacent normal samples, were used for analysis of differentially expressed genes (DEGs). The 3 microarray datasets yielded 144 genes associated with the progression and prognosis of gastric cancer. After this, a risk score model was developed for result validation using an independent dataset from The Cancer Genome Atlas. RESULTS The validation of the independent dataset showed significantly increased NID2, SPARC, and MFAP2 expression in gastric tumor tissues, which were associated with poor outcomes in gastric cancer patients. Moreover, the high risk score obtained was associated with poor overall survival (HR: 1.787; 1.069-2.986; P=0.027). Subgroup analyses revealed that these significant prognostic values were detected in patients aged <65.0 years, tumors in the antrum/distal colon, grade 3 tumors, or TNM-M0 stages of cancer. CONCLUSIONS The findings of this study show that NID2, SPARC, and MFAP2 are upregulated in gastric tumor tissues and are significantly associated with poor overall survival. Therefore, the predictive values of the risk score model employed for the prognosis of gastric cancer could be improved by using these 3 upregulated DEGs.

Clinically applicable 53-Gene prognostic assay predicts chemotherapy benefit in gastric cancer: A multicenter study

BACKGROUND

We previously established a 53-gene prognostic signature for overall survival (OS) of gastric cancer patients. This retrospective multi-center study aimed to develop a clinically applicable gene expression detection assay and to investigate the prognostic value of this signature.

METHODS

A TCGA gastric adenocarcinoma cohort (TCGA-STAD) was used for comparing 53-gene signature with other gene signatures. A high-throughput mRNA hybridization gene expression assay was developed to quantify the expression of 53-genes in formalin-fixed paraffin-embedded tissues of 540 patients enrolled from three hospitals. 180 patents were randomly selected from two hospitals to build a prognostic prediction model based on the 53-gene signature using leave-p-out (one-third out) cross-validation method together with Cox regression and Kaplan-Meier analysis, and the model was assessed on three validation cohorts.

FINDINGS

In the evaluation phase, studies based on TCGA-STAD showed that the 53-gene signature was significantly superior to other three prognostic signatures and was independent of TCGA molecular subtypes and clinical factors. For clinical validation and utility, the prognostic scores were generated using the newly developed assay, which was reliable and sensitive, in 100 sampling training sets and were significantly associated with OS in 100 sampling validation sets. The scores were significantly associated with OS in three independent and combined validation cohorts, and in patients with stages II and III/IV. The multivariate Cox regression demonstrated that the prognostic power of the score was independent of clinical factors, consistent with those findings in the TCGA dataset. Finally, patients with good prognostic scores exhibited significantly a better 5-year OS rate from adjuvant FOLFOX chemotherapy after surgery than from other chemotherapies.

INTERPRETATION

The 53-gene prognostic score system is clinically applicable for predicting the OS of patients independent of clinical factors in gastric cancers, which could also be a promising predictive biomarker for FOLFOX regimen.

FUNDING

Chinese National Science and Technology, National Natural Science Foundation and Natural Science Foundation of Jiangsu Province.

Systematic Review of Prognostic Gene Signature in Gastric Cancer Patients

Gastric cancer (GC) is the second leading cause of cancer mortality and remains the fourth common cancer worldwide. The effective and feasible methods for predicting the possible outcomes for GC patients are still lacking. While genetic profiling might be suitable in some way, the application of gene expression signatures has been show to be a robust tool. Here, by performing a comprehensive search in PubMed, we provided an up-to-date summary of 39 prognostic gene signatures for GC patients, and described the processing procedure of the selection, calculation and construction of gene signature. We also reviewed current web tools including PROGgene and SurvExpress that can be used to analyze the prognostic value of multiple genes for GC. This review will aid in comprehensive understanding of the current prognostic gene signatures to accurately predict the outcome of GC patients, and may guide the future clinical management when the reliability of these signatures is validated in clinics.

Correlation among postoperative uncertainty, social support, and quality of life in patients with esophageal cancer in a tertiary hospital

BACKGROUND

Esophageal cancer is one of the most common malignant tumors. It is easy to cause patients to change their dietary structure after surgery, which leads to decreased quality of life. The decrease of quality of life affects the patients' psychosocial mood to some extent, and the degree of social support is one of the important factors affecting the quality of life of cancer patients.

AIM

To investigate the correlation among the feeling of disease uncertainty, social support, and quality of life in patients with esophageal cancer after operation in a tertiary hospital.

METHODS

A total of 120 patients admitted to Yiwu Central Hospital of Zhejiang Province from February 2017 to September 2019 after esophageal cancer surgery were selected as study subjects. After admission, the patients were investigated and analyzed using the scales of disease uncertainty, social support status, and quality of life. SPSS 23.0 software was used to analyze the data, and the correlation between postoperative quality of life and social support was analyzed by multiple regression.

RESULTS

The disease uncertainty scores of patients with esophageal cancer were 68-126 (102.65 ± 8.76). Among the four dimensions, the score of uncertainty dimension was 3.56 ± 0.46, the complexity score was 2.87 ± 0.52, the score of unpredictability of prognosis was 3.12 ± 0.35, and the score of lack of information was 3.62 ± 0.34. The postoperative social support score of patients with esophageal cancer was 41.87 ± 8.96, which was significantly higher than the norm score (33.75 ± 4.12; P < 0.05). The scores of body function, role function, and overall health dimensions of the postoperative quality of life scale in patients with esophageal cancer were positively correlated with social support (r = 0.724, 0.602, and 0.654, respectively). Physical function and overall health were positively correlated with subjective support (r = 0.712, 0.625). Body function was positively correlated with objective support (r = 0.528). Role function and overall health were positively correlated with paired support utilization (r = 0.538, 0.526).

CONCLUSION

The overall postoperative quality of life of patients with esophageal cancer is positively correlated with three dimensions of social support, indicating that social support plays an important role in patients' quality of life.

Whole Genome Messenger RNA Profiling Identifies a Novel Signature to Predict Gastric Cancer Survival

Molecular prognostic biomarkers for gastric cancer (GC) are still limited. We aimed to identify potential messenger RNAs (mRNAs) associated with GC prognosis and further establish an mRNA signature to predict the survival of GC based on the publicly accessible databases.

A molecular and staging model predicts survival in patients with resected non-small cell lung cancer

Background

The current TNM staging system is far from perfect in predicting the survival of individual non-small cell lung cancer (NSCLC) patients. In this study, we aim to combine clinical variables and molecular biomarkers to develop a prognostic model for patients with NSCLC.

Methods

Candidate molecular biomarkers were extracted from the Gene Expression Omnibus (GEO), and Cox regression analysis was performed to determine significant prognostic factors. The survival prediction model was constructed based on multivariable Cox regression analysis in a cohort of 152 NSCLC patients. The predictive performance of the model was assessed by the Area under the Receiver Operating Characteristic Curve (AUC) and Kaplan–Meier survival analysis.

Results

The survival prediction model consisting of two genes (TPX2 and MMP12) and two clinicopathological factors (tumor stage and grade) was developed. The patients could be divided into either high-risk group or low-risk group. Both disease-free survival and overall survival were significantly different among the diverse groups (P < 0.05). The AUC of the prognostic model was higher than that of the TNM staging system for predicting survival.

Conclusions

We developed a novel prognostic model which can accurately predict outcomes for patients with NSCLC after surgery.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-4881-9) contains supplementary material, which is available to authorized users.

Identification and validation of a Hedgehog pathway-based 3-gene prognostic signature for gastric cancers

Developing prognostic factors for patients with gastric cancer (GC) is crucial for the accurate identification of subgroups with distinct clinical outcomes and the development of effective treatment strategies. The aim of this study was to determine novel gene expression signatures from the hedgehog (Hh) signaling pathway as predictors of risk with biological significance. The Cancer Genome Atlas (TCGA) GC (STAD) cohort was used as the training dataset to select for significant prognostic Hh genes. Three Hh genes, indian hedgehog (IHH), patched 1 (PTCH1) and smoothened frizzled class receptor (SMO), were identified to be significant prognostic factors. On this basis, a 3-Hh-gene set was constructed and the high-risk patients of the training cohort were distinguished against low-risk cases [hazard ratio (HR)=1.73, 95% confidence interval (CI)=1.26-2.39, P=0.00069]. Then the gene signature was externally validated in a combined dataset from Gene Expression Omnibus (n=631), and experimentally confirmed in an independent cohort of 126 clinical GC samples by immunohistochemistry (IHC). Validation in the combined GEO dataset yielded consistent results (HR=1.45, 95% CI=1.17-1.81, P=0.00068), and remained significant for stages I-III, HER2-positive and surgery alone subgroups. Subsequently, we further demonstrated that this mRNA-based gene set could be successfully transferred to an IHC-based signature in our local cohort (HR=2.04, 95% CI=1.09-3.82, P=0.02). In addition, this signature served as an independent prognostic indicator for overall survival in the multivariate Cox analysis (HR=2.133, 95% CI=1.110-4.099, P=0.02). In conclusion, we successfully generated a stable III-Hh-gene model with the ability to separate patients into prognostic subgroups, which may have notable biological importance and be easily utilized clinically.