Prognosis Prediction Of Gastric Cancer Research Articles

Retraction Note: Hedgehog signaling-related genomics signature for the accurate progress and prognosis prediction in gastric cancer.

Retraction Note: Hedgehog signaling-related genomics signature for the accurate progress and prognosis prediction in gastric cancer.

Exosomal circ50547 as a potential marker and promotor of gastric cancer progression via miR-217/HNF1B axis

BackgroundExosomes, one of small extracellular vesicles, play a vital role in cell to cell communication and contribute to the advancement of tumors through their cargo molecules. Exosomal circRNAs have emerged as significant players in various types of tumors. Thus, this study aimed to investigate how exosomal circRNAs are involved in the diagnosis and progression of gastric cancer (GC). MethodsSerum exosomes were characterized using transmission electron microscopy, nanoparticle tracking analysis and Western blot. CCK-8, colony formation and transwell assays were conducted to study the function of hsa_circ_0050547 (named as circ50547). qRT-PCR was used to quantify the expression of circ50547 in GC tissues and serum exosomes. Fluorescence in situ hybridization was applied to detect the cellular distribution of circ50547. Stemness and drug-resistance were detected by sphere formation, WB, flow cytometry and half-maximal inhibitory concentration analyses. Bioinformatic analyses, luciferase experiments, qRT-PCR and WB were used to investigate molecular mechanisms. ResultsWe discovered for the first time a new type of GC-derived exosomal circRNA, circ50547. We found that circ50547 is highly expressed in both GC tissues and serum exosomes. Interestingly, we observed that the diagnostic value of exosomal circ50547 is superior to that of serum circ50547. Circ50547 overexpression enhanced the proliferation, migration, invasion, stemness and drug resistance of GC cells, while knockdown of circ50547 showed the opposite effect. Mechanistically, circ50547 acted as a sponge for miR-217 to regulate the expression of HNF1B, which promoted gastric cancer progression. ConclusionExosomal circ50547 may be a promising marker for the diagnosis and prognosis prediction of GC. These findings suggest that it plays an oncogenic role through miR-217/HNF1B signaling pathway in GC.

Present State and Recent Developments of Artificial Intelligence and Machine Learning in Gastric Cancer Diagnosis and Prognosis: A Systematic Review

Objective: The objective of this study is to thoroughly investigate the use of artificial intelligence (AI) and machine learning (ML) techniques for diagnosing and predicting prognosis in gastric cancer, utilizing the latest available data. Methods: Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)guidelines, a systematic review investigated AI and ML applications in gastric cancer diagnosis and prognostic prediction. PubMed and Google Scholar were searched from February 2019 to January 2024 using specific syntax. Eligible trials were selected based on inclusion criteria including recent publication, focus on AI and ML in gastric cancer, and reporting diagnostic or prognostic outcomes. Data were extracted and quality assessed independently, with discrepancies resolved through discussion. Due to design heterogeneity, detailed analysis was omitted, and descriptive summaries of included articles were provided. Results: This review included a total of 8 articles. AI and ML techniques, including convolutional neural networks (CNN) and deep learning models, have played pivotal roles in accurately diagnosing chronic atrophic gastritis, predicting postoperative gastric cancer prognosis, and identifying peritoneal metastasis in gastric cancer patients. These technologies offer potential advantages such as streamlining diagnostic procedures, guiding treatment decisions, and enhancing patient outcomes in gastric cancer management. Conclusion: In the near future, AI applications may have a significant role in the diagnosis and prognosis prediction of gastric cancer.

Galectin-1-mediated high NCAPG expression correlates with poor prognosis in gastric cancer.

Galectin-1 (Gal1) and non-SMC condensin I complex, subunit G (NCAPG) are associated with metastasis in several malignant tumors. However, their precise roles in gastric cancer (GC) remain uncertain. This study explored the clinical significance and relationship of Gal1 and NCAPG in GC. Gal1 and NCAPG expressions were significantly up-regulated in GC compared to adjacent non-cancerous tissues by immunohistochemistry (IHC) and Western blotting. Besides, methods including stable transfection, quantitative real-time reverse transcription PCR, Western blotting, Matrigel invasion and wound-healing assays in vitro, were also conducted. IHC scores for Gal1 and NCAPG had a positive correlation in GC tissues. High Gal1 or NCAPG expression significantly correlated with poor prognosis in GC, and Gal1 combined with NCAPG had a synergetic effect on the prediction of GC prognosis. Gal1 overexpression in vitro enhanced NCAPG expression, cell migration, and invasion in SGC-7901 and HGC-27 cells. Simultaneous Gal1 overexpression and NCAPG knockdown in GC cells partly rescued the migrative and invasive abilities. Thus, Gal1 promoted GC invasion through increased NCAPG expression. The present study demonstrated the prognostic significance of the combination of Gal1 and NCAPG in GC for the first time.

Hedgehog signaling-related genomics signature for the accurate progress and prognosis prediction in gastric cancer.

The Hedgehog pathway is thought to be closely associated with the progression of GC; however, a specific link between the Hedgehog pathway on the prognosis and immune infiltration of gastric cancer is still lacking. This study collected Hedgehog pathway-related genes. The Hedgehog pathway-related pattern were identified by consensus cluster analysis. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and gene set enrichment analysis (GSEA) were used to identify the biological functions which were significantly altered between predefined Cluster1 and Cluster2 in consensus clustering. The risk model of gastric cancer based on Hedgehog signaling pathway was constructed by univariate and multivariate COX regression, and the nomogram was constructed. The results showed that there were significant differences in the expression of Hedgehog pathway-related genes between the two groups. In addition, the constructed risk model was significantly correlated with the clinical prognosis and immune cell infiltration level of patients with gastric cancer. The model effectively predicted the efficacy of chemotherapy in GC patients and the sensitivity of drug treatment between groups. We systematically revealed the mechanism of Hedgehog pathway in gastric cancer and selected biomarkers with biological significance from a new perspective, providing potential direction for the treatment of gastric cancer.

The Number of Positive Tumor Markers (NPTM) Achieves Higher Value in the Prognosis Prediction of Gastric Cancer.

The clinical application of combined tumor markers is still limited. We aim to explore the value of the combination of multiple tumor markers in gastric cancer (GC) prognosis. The prognostic significance was evaluated using Kaplan-Meier log-rank survival analysis and multivariable Cox regression analysis. The estimated area under the curve (AUC) was compared to evaluate the discriminatory ability of different indicators. A nomogram was constructed based on the results of multivariable cox regression, and its performance was evaluated by Harrell's concordance index and calibration curve. NPTM (number of positive tumor markers) displayed independent prognostic significance whether in the whole cohort or in patients with different stages. Patients with the all-negative tumor markers had a worse prognosis after postoperative chemotherapy in all cohort (P = 0.023) or in age ≤60 subgroup (P = 0.012), while patients with positive tumor markers had a better prognosis after postoperative chemotherapy in stage III (P = 0.012). The AUC value of NPTM is higher than any individual tumor marker. The 1-, 3-, and 5-year AUC values of the CTNM (combination of NPTM and pTNM) increased by 5%, 4.8%, and 3.6%, respectively, compared with TNM staging system. The nomogram constructed including NPTM showed its high accuracy (C - index = 0.706) versus TNM staging system (C - index = 0.646) and CTNM (C - index = 0.681). NPTM was an independent predictor of gastric cancer prognosis, showing more accurate prognostic performance than individual tumor markers. Especially its significance in guiding postoperative adjuvant chemotherapy regimens and predicting prognosis by combination with TNM staging system may have a better clinical application value.

A novel cuproptosis-related lncRNA nomogram to improve the prognosis prediction of gastric cancer.

BackgroundCuproptosis is a copper-triggered modality of mitochondrial cell death and cuproptosis process may play important roles in gastric cancer development. However, little is known about cuproptosis-related lncRNAs in gastric adenocarcinoma (STAD). This study is aimed to investigate the potential prognostic signatures of cuproptosis-related lncRNAs in STAD.MethodsThe Cancer Genome Atlas (TCGA) database were used to obtain gene expression profiles, clinicopathological, and OS information for STAD. Cuproptosis-related genes were collected based on previous studies and cuproptosis-related lncRNAs were screened out by co-expression analysis. The nomogram constructed by Cox regression analysis with the minimum absolute contraction and selection operator (lasso) algorithm. In addition, the potential response of ICB therapy and immune evasion incidence were estimated with Tumor Immune Dysfunction and Exclusion (TIDE) algorithm. Immune checkpoint expressions associated with risk scores were also analyzed. The correlation of immune checkpoint CD209 and HAVCR2 expressions associated with risk scores were experimentally testified by RT-qPCR, Western Blot, and IHC. ResultsPatients were classified into high-risk and low-risk groups based on the risk score calculated in this model. The Kaplan–Meier survival curve analysis revealed that the high-risk group was associated with poor prognosis. Multivariate Cox regression analysis suggested that this lncRNA prediction model was an independent risk factor affecting the OS rate. Furthermore, ROC curve indicates that the nomogram was superior to traditional clinicopathological features in predicting STAD prognosis. Finally, functional enrichment analysis and immune checkpoint investigation revealed that the nomogram is notably associated with cholesterol metabolism and immune functions, RT-qPCR and Western Blotting demonstrated the co-expression relationship of LINC01150 with CD209 and HAVCR2.ConclusionA novel cuproptosis-related lncRNAs signature impacts on the prognosis and immunological features of GC.

RNA modification writers influence tumor microenvironment in gastric cancer and prospects of targeted drug therapy.

Background: RNA adenosine modifications are crucial for regulating RNA levels. N6-methyladenosine (m6A), N1-methyladenosine (m1A), adenosine-to-inosine RNA editing, and alternative polyadenylation (APA) are four major RNA modification types. Methods: We evaluated the altered mRNA expression profiles of 27 RNA modification enzymes and compared the differences in tumor microenvironment (TME) and clinical prognosis between two RNA modification patterns using unsupervised clustering. Then, we constructed a scoring system, WM_score, and quantified the RNA modifications in patients of gastric cancer (GC), associating WM_score with TME, clinical outcomes, and effectiveness of targeted therapies. Results: RNA adenosine modifications strongly correlated with TME and could predict the degree of TME cell infiltration, genetic variation, and clinical prognosis. Two modification patterns were identified according to high and low WM_scores. Tumors in the WM_score-high subgroup were closely linked with survival advantage, CD4- T-cell infiltration, high tumor mutation burden, and cell cycle signaling pathways, whereas those in the WM_score-low subgroup showed strong infiltration of inflammatory cells and poor survival. Regarding the immunotherapy response, a high WM_score showed a significant correlation with PD-L1 expression, predicting the effect of PD-L1 blockade therapy. Conclusion: The WM_scoring system could facilitate scoring and prediction of GC prognosis.

A novel ferroptosis-related lncRNA signature for prognosis prediction in gastric cancer

BackgroundGastric cancer (GC) is a common malignant cancer with a poor prognosis. Ferroptosis has been shown to play crucial roles in GC development. Long non-coding RNAs (lncRNAs) is also associated with tumor progression in GC. This study aimed to screen the prognostic ferroptosis-related lncRNAs and to construct a prognostic risk model for GC.MethodsFerroptosis-related lncRNAs from The Cancer Genome Atlas (TCGA) GC expression data was downloaded. First, single factor Cox proportional hazard regression analysis was used to select seven prognostic ferroptosis-related lncRNAs from TCGA database. And then, the selected lncRNAs were further included in the multivariate Cox proportional hazard regression analysis to establish the prognostic model. A nomogram was constructed to predict individual survival probability. Finally, we performed quantitative reverse transcription polymerase chain reaction (qRT-PCR) to verify the risk model.ResultsWe constructed a prognostic ferroptosis-related lncRNA signature in this study. Kaplan-Meier curve analysis revealed a significantly better prognosis for the low-risk group than for the high-risk group (P = 2.036e-05). Multivariate Cox proportional risk regression analysis demonstrated that risk score was an independent prognostic factor [hazard ratio (HR) = 1.798, 95% confidence interval (CI) =1.410–2.291, P < 0.001]. A nomogram, receiver operating characteristic curve, and principal component analysis were used to predict individual prognosis. Finally, the expression levels of AP003392.1, AC245041.2, AP001271.1, and BOLA3-AS1 in GC cell lines and normal cell lines were tested by qRT-PCR.ConclusionsThis risk model was shown to be a novel method for predicting prognosis for GC patients.

Is there a relationship between neural EGFL like 1 (NELL1) promoter hypermethylation and prognosis of gastric cancer?

We hypothesized that neural EGFL like 1 (NELL1) promoter hypermethylation might be associated with the prognosis of gastric cancer. Some studies considered NELL1 as a tumor suppressor gene and our research confirmed for the first time the hypermethylation in the promoter region of NELL1 by the application of mass spectrometry. Promoter hypermethylation can cause the silencing of tumor suppressor genes and promote tumor progression. Based on present studies and research results, we proposed that NELL1 promoter hypermethylation might be associated with cancer staging and the survival of gastric cancer patients and had prognostic value. We hoped that NELL1 promoter hypermethylation would be applied not only for early detection but also prognosis prediction of gastric cancer and would become a new prognostic biomarker.

Accurate diagnosis and prognosis prediction of gastric cancer using deep learning on digital pathological images: A retrospective multicentre study.

BackgroundTo reduce the high incidence and mortality of gastric cancer (GC), we aimed to develop deep learning-based models to assist in predicting the diagnosis and overall survival (OS) of GC patients using pathological images.Methods2333 hematoxylin and eosin-stained pathological pictures of 1037 GC patients were collected from two cohorts to develop our algorithms, Renmin Hospital of Wuhan University (RHWU) and the Cancer Genome Atlas (TCGA). Additionally, we gained 175 digital pictures of 91 GC patients from National Human Genetic Resources Sharing Service Platform (NHGRP), served as the independent external validation set. Two models were developed using artificial intelligence (AI), one named GastroMIL for diagnosing GC, and the other named MIL-GC for predicting outcome of GC.FindingsThe discriminatory power of GastroMIL achieved accuracy 0.920 in the external validation set, superior to that of the junior pathologist and comparable to that of expert pathologists. In the prognostic model, C-indices for survival prediction of internal and external validation sets were 0.671 and 0.657, respectively. Moreover, the risk score output by MIL-GC in the external validation set was proved to be a strong predictor of OS both in the univariate (HR = 2.414, P < 0.0001) and multivariable (HR = 1.803, P = 0.043) analyses. The predicting process is available at an online website (https://baigao.github.io/Pathologic-Prognostic-Analysis/).InterpretationOur study developed AI models and contributed to predicting precise diagnosis and prognosis of GC patients, which will offer assistance to choose appropriate treatment to improve the survival status of GC patients.FundingNot applicable.

A Novel Autophagy-Related lncRNA Prognostic Signature Associated with Immune Microenvironment and Survival Outcomes of Gastric Cancer Patients.

PurposeAutophagy plays a crucial role in the initiation and progression of gastric cancer (GC). However, the role of autophagy-related lncRNAs in GC remains unknown. This study aimed to investigate the prognostic value of the autophagy-related lncRNA signature and its role in the tumor immune microenvironment (TIME) of GC.MethodsRNA-sequencing (RNA-seq) and clinical data of GC patients were extracted from The Cancer Genome Atlas (TCGA) database. Univariate and multivariate Cox regression analyses were performed to identify the autophagy-related lncRNA prognostic signature which was validated in the test set and entire set. The survival and predictive performance were analyzed based on the Kaplan–Meier and ROC curves. Furthermore, the CIBERSORT algorithm was applied to explore the relationship between this signature and the immune cell infiltration. To elucidate the potential functions of autophagy-related lncRNAs in GC, we constructed the lncRNA-mRNA co-expression network and performed enrichment analysis. Principal component analysis (PCA) and Gene Set Enrichment Analysis (GSEA) were further performed to compare the different statuses between the high-risk and low-risk groups.ResultsWe identified 5 autophagy-related lncRNAs (AL355574.1, AC010768.2, AP000695.2, AC087286.2, and HAGLR) to construct a prognostic signature. This signature could be an independent prognostic indicator for GC patients and had a higher prediction efficiency than other clinicopathological parameters. Furthermore, patients in the high-risk score group had a stronger immunosuppressive microenvironment than the low-risk group. The enrichment analysis for mRNAs co-expressed with these lncRNAs indicated that autophagy-related signaling pathways were remarkably enriched. PCA and GSEA further revealed different autophagy and immune statuses in the high- and low-risk groups.ConclusionThe 5 autophagy-related lncRNA signature has significant clinical implications in prognosis prediction of GC. Meanwhile, our study elucidates the critical role of the autophagy-related lncRNA signature in the TIME of GC.

DZIP1 Expression as a Prognostic Marker in Gastric Cancer: A Bioinformatics-Based Analysis.

PurposeGastric cancer (GC) is a common type of cancer worldwide. It can relapse and metastasize even after standard treatment; therefore, it has a poor prognosis. Moreover, sensitive biomarkers for prognosis prediction in GC are lacking. In this study, using a bioinformatics approach, we aimed to examine the value of DAZ Interacting Protein 1 (DZIP1) as a prognostic predictor and therapeutic target in GC.MethodsWe explored the clinical relevance, function, and molecular role of DZIP1 in GC using MethSurv, cBioPortal, TIMER, Gene Expression Profiling Interactive Analysis, IMEx, ONCOMINE, MEXPRESS, and EWAS Atlas databases. The GSE118919 dataset was used to plot receiver operating characteristic curves. Using The Cancer Genome Atlas, we developed a Cox regression model and assessed the clinical significance of DZIPs. In addition, we used the “xCELL” algorithm to make reliable immune infiltration estimations. Western blot and immunohistochemistry were used to examine protein expression. The results were visualized with the ‘ggplot2ʹ and “circlize” packages.ResultsIn GC patients, DZIP1 was over-expressed at both the mRNA and protein levels. High levels of DZIP1 were found to be associated with poor survival in patients with GC. Our results indicated that DZIP1 could be involved in multiple cancer-related pathways such as the PI3K-Akt signaling pathway, WNT signaling pathway, and RAS signaling pathway, and its expression was correlated with the infiltration of activated myeloid dendritic cells, naive CD4+ T cells, and naive CD8+ T cells. Furthermore, we found that mutations in DZIP1 were correlated with a good prognosis in GC patients. Finally, we demonstrated a correlation between hypomethylation of the DZIP1 gene promoter and a poor prognosis in GC.ConclusionThis study is the first to demonstrate a significant correlation between high levels of DZIP1 and a poor prognosis in GC patients. Our results clarify multiple potential mechanisms that could contribute to this correlation and may thus provide novel insights into the clinical diagnosis and treatment of GC.

Construction on of a Ferroptosis-Related lncRNA-Based Model to Improve the Prognostic Evaluation of Gastric Cancer Patients Based on Bioinformatics.

BackgroundGastric cancer is one of the most serious gastrointestinal malignancies with bad prognosis. Ferroptosis is an iron-dependent form of programmed cell death, which may affect the prognosis of gastric cancer patients. Long non-coding RNAs (lncRNAs) can affect the prognosis of cancer through regulating the ferroptosis process, which could be potential overall survival (OS) prediction factors for gastric cancer.MethodsFerroptosis-related lncRNA expression profiles and the clinicopathological and OS information were collected from The Cancer Genome Atlas (TCGA) and the FerrDb database. The differentially expressed ferroptosis-related lncRNAs were screened with the DESeq2 method. Through co-expression analysis and functional annotation, we then identified the associations between ferroptosis-related lncRNAs and the OS rates for gastric cancer patients. Using Cox regression analysis with the least absolute shrinkage and selection operator (LASSO) algorithm, we constructed a prognostic model based on 17 ferroptosis-related lncRNAs. We also evaluated the prognostic power of this model using Kaplan–Meier (K-M) survival curve analysis, receiver operating characteristic (ROC) curve analysis, and decision curve analysis (DCA).ResultsA ferroptosis-related “lncRNA–mRNA” co-expression network was constructed. Functional annotation revealed that the FOXO and HIF-1 signaling pathways were dysregulated, which might control the prognosis of gastric cancer patients. Then, a ferroptosis-related gastric cancer prognostic signature model including 17 lncRNAs was constructed. Based on the RiskScore calculated using this model, the patients were divided into a High-Risk group and a low-risk group. The K-M survival curve analysis revealed that the higher the RiskScore, the worse is the obtained prognosis. The ROC curve analysis showed that the area under the ROC curve (AUC) of our model is 0.751, which was better than those of other published models. The multivariate Cox regression analysis results showed that the lncRNA signature is an independent risk factor for the OS rates. Finally, using nomogram and DCA, we also observed a preferable clinical practicality potential for prognosis prediction of gastric cancer patients.ConclusionOur prognostic signature model based on 17 ferroptosis-related lncRNAs may improve the overall survival prediction in gastric cancer.

Differentially expressed genes PCCA, ECHS1, and HADH are potential prognostic biomarkers for gastric cancer.

Gastric cancer (GC) is one of the most common malignant tumors in the world. As far as we know, no biomarker has been widely accepted for early diagnosis and prognosis prediction of GC. The purpose of this study is to find potential biomarkers to predict the prognosis of GC. The differentially expressed gene (DEG) was analyzed from GSE93774. Enrichr was used to analyze the gene ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway, the enrichment of transcription factors (TF), miRNA, and kinase. GO analysis showed DEGs was enriched in the process of amino acid metabolism. Pathway results showed DEGs was mainly enriched in cell cycle. Propionyl CoA carboxylase alpha (PCCA), Enoyl coenzyme A hydratase short chain 1 (ECHS1), and 3-hydroxyacyl-CoA dehydrogenase (HADH) have prognostic value in patients with GC. ECHS1 and HADH genes were significantly associated with disease-free survival. There was a significant correlation between PCCA and overall survival rate. The results of this study suggest that PCCA, ECHS1, and HADH may be new biomarkers for predicting the prognosis of GC.

The prognostic value of circular RNA regulatory genes in competitive endogenous RNA network in gastric cancer.

Accumulating evidence shows that circular RNA (circRNA) is an important regulator of many diseases, especially cancer. Gastric cancer (GC) is a malignant tumor of the digestive system. The regulatory role and potential mechanism of circRNAs in GC remain unknown. This study aims to explore the function and regulatory mechanism of circRNA-related competitive endogenous RNA (ceRNA) in GC. The circRNA expression profile was downloaded from the Gene Expression Omnibus (GEO) database. The RNA expression profile and clinical data were downloaded from The Cancer Genome Atlas (TCGA) database. Difference analysis was conducted after quality control. Based on CircInteractome, TargetScan, and miRDB databases, a circRNA-related ceRNA network was constructed. R package "clusterProfiler" was used for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Then, a univariate and multivariate Cox regression was used to construct a prognostic-related gene model to predict survival models. Finally, a gene set enrichment analysis (GSEA) analysis was performed to elucidate the function of genes related to prognosis. Altogether, 23 DEcircRNAs, 319 DEmiRNAs, and 14,541 DEmRNAs were identified. Based on ceRNA trends, the ceRNA network included 15 DEcircRNAs, 25 DEmiRNAs, and 1099 DEmRNAs in GC. Univariate and multivariate Cox proportional hazards regression analysis was used to establish a survival model with 11 prognosis-related genes and its AUC was 0.741, indicating good sensitivity and specificity in the prediction of GC prognosis. Finally, three prognostic-related genes were selected randomly to verify expression levels, which were consistent with the analysis result. The prognostic genes were significantly enriched in cancer-related biological processes, suggesting their roles in the onset and progression of GC. Our study constructs a prognostic model of GC, deepens our understanding of circRNA-related ceRNA networks in GC biology, and provided further implications for the diagnosis and treatment of GC.

Construction of a Nomogram Based on a Hypoxia-Related lncRNA Signature to Improve the Prediction of Gastric Cancer Prognosis.

BackgroundGastric cancer is one of the most common malignant tumors and has a poor prognosis. Hypoxia is related to the poor prognosis of cancer patients. We searched for hypoxia-related long non-coding RNAs (lncRNAs) to predict both overall survival (OS) and disease-free survival (DFS) of gastric cancer patients.MethodsWe obtained hypoxia-related lncRNA expression profiles and clinical follow-up data of patients with gastric cancer from The Cancer Genome Atlas and the Molecular Signatures Database. The patients were randomly divided into a training group, test group and combined group. The hypoxia-related prognostic signature was constructed by Lasso regression and Cox regression models, the prognoses in different groups were compared by Kaplan–Meier (K-M) analysis, and the accuracy of the prognostic model was assessed by receiver operating characteristic (ROC) analysis.ResultsA hypoxia-related prognostic signature comprising 10 lncRNAs was constructed to predict both OS and DFS in gastric cancer. In the training, test and combined groups, patients were divided into high- and low-risk groups according to the formula. Kaplan–Meier analysis showed that patients in the high-risk group have poor prognoses, and the difference was significant in the subgroup analyses. Receiver operating characteristic analysis revealed that the predictive power of the model prediction is more accurate than that of standard benchmarks. The signature differed across Helicobacter pylori (Hp) status and T stages. Multivariate Cox analysis showed that the signature is an independent risk factor for both OS and DFS. A clinically predictive nomogram combining the lncRNA signature and clinical features was constructed; the nomogram accurately predicted both OS and DFS and had high clinical application value. Weighted correlation network analysis combined with enrichment analysis showed that the primary pathways were the PI3K-Akt, JAK-STAT, and IL-17 signaling pathways. The target genes NOX4, COL8A1, and CHST1 were associated with poor prognosis in the Gene Expression Profiling Interactive Analysis, Gene Expression Omnibus, and K-M Plotter databases.ConclusionsOur 10-lncRNA prognostic signature and nomogram are accurate, reliable tools for predicting both OS and DFS in gastric cancer.

Identification of novel biomarkers, MUC5AC, MUC1, KRT7, GAPDH, CD44 for gastric cancer

Gastric cancer (GC) is one of the most common malignant tumors in the world, and it is also the third largest cause of cancer-related death in the world. As far as we know, no biomarker has been widely accepted for early diagnosis and prognosis prediction of gastric cancer. The purpose of this study is to find potential biomarkers to predict the prognosis of GC. The gene expression profiles of GSE2685 were downloaded from GEO database. Morpheus was used to calculate the differentially expressed genes (DEGs) between primary advanced gastric cancer tissues and noncancerous gastric tissues. The gene ontology (GO) and Kyoto Encyclopedia of Genes and Genomes pathway (KEGG) enrichment analyses were performed, and protein–protein interaction (PPI) network of DEGs was constructed. Kaplan–Meier Plotter was used to determine the overall survival (OS) outcomes of UC5AC, MUC1, KRT7, GAPDH, CD44, and GEPIA was used to determine the Pearson correlation analysis. In total, 710 DEGs were identified in GC, including 396 upregulated genes and 314 downregulated genes. GO enrichment revealed that they were mainly enriched in binding, catalytic activity, cellular process and cell. KEGG pathway revealed that they were mainly enriched in metabolic pathways, pathways in cancer and PI3K-Akt signaling pathway. MUC5AC, MUC1, KRT7, GAPDH, CD44 were identified from the PPI network. MUC5AC, MUC1, KRT7, GAPDH, CD44 were demonstrated to have prognostic value for patients with GC. MUC5AC, MUC1 exhibited low expression levels in GC tissues, KRT7, GAPDH, CD44 presented high expression levels in GC tissues. In particular, KRT7 is hardly expressed in normal gastric tissues. MUC5AC and MUC1 were negatively correlated with GAPDH, CD44, respectively; and GAPDH was positively correlated with CD44 and KRT7, respectively. Moreover. MUC5AC, MUC1, KRT7, GAPDH, and CD44 are not only related to GC but also to apoptosis pathway. Results from the present study suggested that MUC5AC, MUC1, KRT7, GAPDH, CD44 may represent novel prognostic biomarkers for GC.

A Pair-Wise Meta-Analysis Highlights Serum Amyloid Has A Potential Clinical Value for Diagnosis and Prognosis Prediction of Gastric Cancer.

This study sought to systematically assess the diagnostic and prognostic value of serum amyloid A (SAA) in gastric cancer (GC). PubMed, Embase, EBSCO, CNKI, and the Cochrane Library databases were searched for eligible studies. Extracted data were analyzed to determine diagnostic parameters and the summary receiver operating characteristic (SROC). Pooled hazard ratios (HR) and odds ratios (ORs) with their corresponding 95% confidence intervals (95% CIs) were calculated to summarize the effects. Six articles in English that met the inclusion criteria were identified. Meta-analysis of the included studies indicated high sensitivity of 0.84 (95% CI: 0.77 - 0.89) and moderate specificity of 0.61 (95% CI: 0.55 - 0.67) of SAA, with a diagnostic odds ratio (DOR) of 8.17 (95% CI: 4.82 - 13.86). The area under the receiver operating characteristic curve was 0.77. Survival analysis showed that SAA was associated with shorter survival time (HR = 4.42, p = 0.000; I2 = 0.0%). Stratified analyses showed that the assay of SAA from serum harbored higher efficacy than that from serum + plasma (AUC: 0.81 vs. 0.77), and SAA testing also achieved a better diagnostic performance in Asians than in Caucasians (AUC: 0.77 vs. 0.50). Collectively, our analyses suggest that SAA can be used as a clinical auxiliary reference index for the diagnosis and prognosis prediction of GC; however, this diagnostic method is not independently sufficient. Our findings require confirmation in a larger prospective study.

Development and Validation of a Scoring System Based on 9 Glycolysis-Related Genes for Prognosis Prediction in Gastric Cancer.

Gastric cancer is a malignant tumor with high morbidity and mortality worldwide. However, increasing evidences have revealed the correlation between the glycolysis process and tumorigenesis. This study is aim to develop a list of glycolysis-related genes for risk stratification in gastric cancer patients. We included 500 patients’ sample data from GSE62254 and GSE26942 datasets, and classified patients into training (n = 350) and testing sets (n = 150) at a ratio of 7: 3. Univariate and multivariate Cox regression analysis were performed to screen genes having prognostic value. Based on HALLMARK gene sets, we identified 9 glycolysis-related genes (BPNT1, DCN, FUT8, GMPPA, GPC3, LDHC, ME2, PLOD2, and UGP2). On the basis of risk score developed by the 9 genes, patients were classified into high- and low-risk groups. The survival analysis showed that the high-risk patients had a worse prognosis (p < 0.001). Similar finding was observed in the testing cohort and 2 independent cohorts (GSE13861 and TCGA-STAD, all p < 0.001). The multivariate Cox regression analysis showed that the risk score was an independent prognostic factor for overall survival (p < 0.001). Furthermore, we constructed a nomogram that integrated the risk score and tumor stage, age, and adjuvant chemotherapy. Through comparing the results of the receiver operating characteristic curves and decision curve analysis, we found that the nomogram had a superior predictive accuracy than conventional TNM staging system, suggesting that the risk score combined with other clinical factors (age, tumor stage, and adjuvant chemotherapy) can develop a robust prediction for survival and improve the individualized clinical decision making of the patient.In conclusion, we identified 9 glycolysis-related genes from hallmark glycolysis pathway. Based on the 9 genes, gastric cancer patients were separated into different risk groups related to survival.