Identification of a prognostic 5-Gene expression signature for gastric cancer

SGCE promotes breast cancer stemness by promoting the transcription of FGF-BP1 by Sp1

Breast cancer stem cells are mainly responsible for poor prognosis, especially in triple-negative breast cancer (TNBC). In a previous study, we demonstrated that ε-Sarcoglycan (SGCE), a type Ⅰ single-transmembrane protein, is a potential oncogene that promotes TNBC stemness by stabilizing EGFR. Here, we further found that SGCE depletion reduces breast cancer stem cells, partially through inhibiting the transcription of FGF-BP1, a secreted oncoprotein. Mechanistically, we demonstrate that SGCE could interact with the specific protein 1 transcription factor and translocate into the nucleus, which leads to an increase in the transcription of FGF-BP1, and the secreted FBF-BP1 activates FGF-FGFR signaling to promote cancer cell stemness. The novel SGCE-Sp1-FGF-BP1 axis provides novel potential candidate diagnostic markers and therapeutic targets for TNBC.

Characteristics of alpha-fetoprotein-positive gastric cancer revealed by analysis of cancer databases and transcriptome sequencing data

Gastric cancer is one of the most common malignant tumors in the world. Alpha fetoprotein (AFP)-positive gastric cancer (AFPP-GC) is considered a special entity among gastric cancers. There is still controversy regarding the clinicopathological characteristics and prognosis of AFPP-GC, and the potential mechanism underlying its high malignant potential is still unclear. A comprehensive description of AFPP-GC genomic characteristics and regulatory mechanisms is lacking. This study analyzed the pathological characteristics and prognosis of AFPP-GC by utilizing clinical samples. The results showed that AFPP-GC has a poor prognosis and a high of risk liver metastasis. Tissue transcriptome sequencing showed that genes with high expression in AFPP-GC were involved in the activation of various cancer pathways, and genes with low expression were involved in the immune response. Single-sample gene set enrichment analysis showed that overexpression of AFP in AFPP-GC significantly inhibited the infiltration of CD8+ T cells. To further explore the genomic characteristics of AFPP-GC, the signaling pathway by which AFP regulates the invasion and metastasis of AFPP-GC cells was discussed. The results showed that AFPP-GC may promote cell invasion by regulating the PTEN/AKT1/SOX5/CES1 signaling axis. This study reveals the molecular mechanism underlying the increased malignant potential of AFPP-GC vs. AFP-negative gastric cancer (AFPN-GC). This provides important information for individualized treatment of AFPP-GC.

Proteomic analysis of diabetes genetic risk scores identifies complement C2 and neuropilin-2 as predictors of type 2 diabetes: the Atherosclerosis Risk in Communities (ARIC) Study

AIMS/HYPOTHESIS

Genetic predisposition to type 2 diabetes is well-established, and genetic risk scores (GRS) have been developed that capture heritable liabilities for type 2 diabetes phenotypes. However, the proteins through which these genetic variants influence risk have not been thoroughly investigated. This study aimed to identify proteins and pathways through which type 2 diabetes risk variants may influence pathophysiology.

METHODS

Using a proteomics data-driven approach in a discovery sample of 7241 White participants in the Atherosclerosis Risk in Communities Study (ARIC) cohort and a replication sample of 1674 Black ARIC participants, we interrogated plasma levels of 4870 proteins and four GRS of specific type 2 diabetes phenotypes related to beta cell function, insulin resistance, lipodystrophy, BMI/blood lipid abnormalities and a composite score of all variants combined.

RESULTS

Twenty-two plasma proteins were identified in White participants after Bonferroni correction. Of the 22 protein-GRS associations that were statistically significant, 10 were replicated in Black participants and all but one were directionally consistent. In a secondary analysis, 18 of the 22 proteins were found to be associated with prevalent type 2 diabetes and ten proteins were associated with incident type 2 diabetes. Two-sample Mendelian randomisation indicated that complement C2 may be causally related to greater type 2 diabetes risk (inverse variance weighted estimate: OR 1.65 per SD; p=7.0 × 10-3), while neuropilin-2 was inversely associated (OR 0.44 per SD; p=8.0 × 10-3).

CONCLUSIONS/INTERPRETATION

Identified proteins may represent viable intervention or pharmacological targets to prevent, reverse or slow type 2 diabetes progression, and further research is needed to pursue these targets.

Coexistence of a novel STRN-ALK, NBEA-ALK double-fusion in an ovarian malignant mesothelioma patient: a case report and review

Primary ovarian mesothelioma (POM) is a rare malignant tumor with poor prognosis. Although anaplastic lymphoma kinase gene (ALK) double-fusion partners have been found in various tumors, it is rarely reported in mesothelioma. In this article, we describe the coexistence of a novel STRN-ALK, neurobeachin (NBEA)-ALK double-fusion in a patient with primary ovarian mesothelioma. A 30-year-old woman was found to have pelvic masses for more than a year. Color Doppler ultrasound showed mixed mass in the left ovary and multiple solid masses in the right ovary. the patient underwent laparoscopic surgery, including total hysterectomy, bilateral salpingo-oopherectomy, pelvic lymph node and abdominal aortic lymph node resection, omentum resection and abdominal focus resection. Pathologic examination revealed bilateral ovarian malignant mesothelioma and no evidence of malignancy in the resected bilateral round/broad ligaments, bilateral parametrial tissues, vaginal stump, bilateral fallopian tubes, pelvic and paraaortic lymph nodes. Immunohistochemistry showed that it was positive for Calretinin, VIM, WT1, PAX8, mesothelin, CK5/6, PCK, CK7, MLH1, PMS2, MSH2, MSH6, weakly positive for BAP1, while being negative for Napsin A, P504S, CEA, D2-40, GATA3. The sequencing analysis identified STRN-ALK (intron3:intron19) and NBEA-ALK (intron1:intron16) double-ALK fusion. To the best of our knowledge, this is the first report that a novel NBEA-ALK and EML4-ALK coexist in one patient with POM. The patient has completed 6 cycles of continuous chemotherapy and is in stable condition. Whether ALK inhibitors can bring promising benefits to POM patients in the future deserves further study.

Landscape of Druggable Molecular Pathways Downstream of Genomic CDH1/Cadherin-1 Alterations in Gastric Cancer

Loss of CDH1/Cadherin-1 is a common step towards the acquisition of an abnormal epithelial phenotype. In gastric cancer (GC), mutation and/or downregulation of CDH1/Cadherin-1 is recurrent in sporadic and hereditary diffuse GC type. To approach the molecular events downstream of CDH1/Cadherin-1 alterations and their relevance in gastric carcinogenesis, we queried public databases for genetic and DNA methylation data in search of molecular signatures with a still-uncertain role in the pathological mechanism of GC. In all GC subtypes, modulated genes correlating with CDH1/Cadherin-1 aberrations are associated with stem cell and epithelial-to-mesenchymal transition pathways. A higher level of genes upregulated in CDH1-mutated GC cases is associated with reduced overall survival. In the diffuse GC (DGC) subtype, genes downregulated in CDH1-mutated compared to cases with wild type CDH1/Cadherin-1 resulted in being strongly intertwined with the DREAM complex. The inverse correlation between hypermethylated CpGs and CDH1/Cadherin-1 transcription in diverse subtypes implies a common epigenetic program. We identified nonredundant protein-encoding isoforms of 22 genes among those differentially expressed in GC compared to normal stomach. These unique proteins represent potential agents involved in cell transformation and candidate therapeutic targets. Meanwhile, drug-induced and CDH1/Cadherin-1 mutation-related gene expression comparison predicts FIT, GR-127935 hydrochloride, amiodarone hydrochloride in GC and BRD-K55722623, BRD-K13169950, and AY 9944 in DGC as the most effective treatments, providing cues for the design of combined pharmacological treatments. By integrating genetic and epigenetic aspects with their expected functional outcome, we unveiled promising targets for combinatorial pharmacological treatments of 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.

Identification of TGF-β signaling-related molecular patterns, construction of a prognostic model, and prediction of immunotherapy response in gastric cancer

Background: TGF-β signaling pathway plays an essential role in tumor progression and immune responses. However, the link between TGF-β signaling pathway-related genes (TSRGs) and clinical prognosis, tumor microenvironment (TME), and immunotherapy in gastric cancer is unclear. Methods: Transcriptome data and related clinical data of gastric cancer were downloaded from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, and 54 TSRGs were obtained from the Molecular Signatures Database (MSigDB). We systematically analyzed the expression profile characteristics of 54 TSRGs in 804 gastric cancer samples and examined the differences in prognosis, clinicopathological features, and TME among different molecular subtypes. Subsequently, TGF-β-related prognostic models were constructed using univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analysis to quantify the degree of risk in each patient. Patients were divided into two high- and low-risk groups based on the median risk score. Finally, sensitivity to immune checkpoint inhibitors (ICIs) and anti-tumor agents was assessed in patients in high- and low-risk groups. Results: We identified two distinct TGF-β subgroups. Compared to TGF-β cluster B, TGF-β cluster A exhibits an immunosuppressive microenvironment with a shorter overall survival (OS). Then, a novel TGF-β-associated prognostic model, including SRPX2, SGCE, DES, MMP7, and KRT17, was constructed, and the risk score was demonstrated as an independent prognostic factor for gastric cancer patients. Further studies showed that gastric cancer patients in the low-risk group, characterized by higher tumor mutation burden (TMB), the proportion of high microsatellite instability (MSI-H), immunophenoscore (IPS), and lower tumor immune dysfunction and exclusion (TIDE) score, had a better prognosis, and linked to higher response rate to immunotherapy. In addition, the risk score and anti-tumor drug sensitivity were strongly correlated. Conclusion: These findings highlight the importance of TSRGs, deepen the understanding of tumor immune microenvironment, and guide individualized immunotherapy for gastric cancer patients.

A 9‑gene expression signature to predict stage development in resectable stomach adenocarcinoma

BACKGROUND

Stomach adenocarcinoma (STAD) is a highly heterogeneous disease and is among the leading causes of cancer-related death worldwide. At present, TNM stage remains the most effective prognostic factor for STAD. Exploring the changes in gene expression levels associated with TNM stage development may help oncologists to better understand the commonalities in the progression of STAD and may provide a new way of identifying early-stage STAD so that optimal treatment approaches can be provided.

METHODS

The RNA profile retrieving strategy was utilized and RNA expression profiling was performed using two large STAD microarray databases (GSE62254, n = 300; GSE15459, n = 192) from the Gene Expression Omnibus (GEO) and the RNA-seq database within the Cancer Genome Atlas (TCGA, n = 375). All sample expression information was obtained from STAD tissues after radical resection. After excluding data with insufficient staging information and lymph node number, samples were grouped into earlier-stage and later-stage. Samples in GSE62254 were randomly divided into a training group (n = 172) and a validation group (n = 86). Differentially expressed genes (DEGs) were selected based on the expression of mRNAs in the training group and the TCGA group (n = 156), and hub genes were further screened by least absolute shrinkage and selection operator (LASSO) logistic regression. Receiver operating characteristic (ROC) curves were used to evaluate the performance of the hub genes in distinguishing STAD stage in the validation group and the GSE15459 dataset. Univariate and multivariate Cox regressions were performed sequentially.

RESULTS

22 DEGs were commonly upregulated (n = 19) or downregulated (n = 3) in the training and TCGA datasets. Nine genes, including MYOCD, GHRL, SCRG1, TYRP1, LYPD6B, THBS4, TNFRSF17, SERPINB2, and NEBL were identified as hub genes by LASSO-logistic regression. The model achieved discrimination in the validation group (AUC = 0.704), training-validation group (AUC = 0.743), and GSE15459 dataset (AUC = 0.658), respectively. Gene Set Enrichment Analysis (GSEA) was used to identify the potential stage-development pathways, including the PI3K-Akt and Calcium signaling pathways. Univariate Cox regression indicated that the nine-gene score was a significant risk factor for overall survival (HR = 1.28, 95% CI 1.08-1.50, P = 0.003). In the multivariate Cox regression, only SCRG1 was an independent prognostic predictor of overall survival after backward stepwise elimination (HR = 1.21, 95% CI 1.11-1.32, P < 0.001).

CONCLUSION

Through a series of bioinformatics and validation processes, a nine-gene signature that can distinguish STAD stage was identified. This gene signature has potential clinical application and may provide a novel approach to understanding the progression of STAD.

Bayesian hierarchical lasso Cox model: A 9-gene prognostic signature for overall survival in gastric cancer in an Asian population

Objective

Gastric cancer (GC) is one of the most common tumour diseases worldwide and has poor survival, especially in the Asian population. Exploration based on biomarkers would be efficient for better diagnosis, prediction, and targeted therapy.

Methods

Expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Survival-related genes were identified by gene set enrichment analysis (GSEA) and univariate Cox. Then, we applied a Bayesian hierarchical lasso Cox model for prognostic signature screening. Protein-protein interaction and Spearman analysis were performed. Kaplan–Meier and receiver operating characteristic (ROC) curve analysis were applied to evaluate the prediction performance. Multivariate Cox regression was used to identify prognostic factors, and a prognostic nomogram was constructed for clinical application.

Results

With the Bayesian lasso Cox model, a 9-gene signature included TNFRSF11A, NMNAT1, EIF5A, NOTCH3, TOR2A, E2F8, PSMA5, TPMT, and KIF11 was established to predict overall survival in GC. Protein-protein interaction analysis indicated that E2F8 was likely related to KIF11. Kaplan-Meier analysis showed a significant difference between the high-risk and low-risk groups (P<0.001). Multivariate analysis demonstrated that the 9-gene signature was an independent predictor (HR = 2.609, 95% CI 2.017–3.370), and the C-index of the integrative model reached 0.75. Function enrichment analysis for different risk groups revealed the most significant enrichment pathway/term, including pyrimidine metabolism and respiratory electron transport chain.

Conclusion

Our findings suggested that a novel prognostic model based on a 9-gene signature was developed to predict GC patients in high-risk and improve prediction performance. We hope our model could provide a reference for risk classification and clinical decision-making.

Dysregulated transient receptor potential channel 1 expression and its correlation with clinical features and survival profile in surgical non-small-cell lung cancer patients

BACKGROUND

Transient receptor potential channel 1 (TRPC1) facilitates the tumor growth, metastasis, and chemoresistance in a series of neoplasms, while its correlation with clinical features and survival profile in NSCLC patients remains elusive. Hence, this study aimed to explore this topic.

METHODS

Totally, 192 NSCLC patients were enrolled. Protein and mRNA expression of TRPC1 in carcinoma tissue and para-carcinoma tissue were evaluated by immunohistochemistry (IHC) assay and reverse transcription quantitative polymerase chain reaction (RT-qPCR) assay, respectively.

RESULTS

Immunohistochemistry score and mRNA expression of TRPC1 were higher in carcinoma tissue compared with para-carcinoma tissue (both p < 0.001). Besides, increased TRPC1 IHC score (p = 0.004) and elevated TRPC1 mRNA overexpression (p = 0.016) were linked with occurrence of LYN metastasis; meanwhile, increased TRPC1 IHC score (p = 0.015) and raised TRPC1 mRNA expression (p = 0.009) were also linked with advanced TNM stage, whereas TRPC1 IHC score and TRPC1 mRNA expression were not correlated with other clinical features (all p > 0.05). Additionally, TRPC1 protein high (p = 0.007) and TRPC1 mRNA high (p = 0.015) were correlated with poor disease-free survival (DFS) but not correlated with overall survival (OS). Moreover, multivariate Cox's proportional hazards regression analysis showed that high TRPC1 protein expression (p = 0.046) and advanced TNM stage (p < 0.001) were independently correlated with poor DFS. However, TRPC1 protein and mRNA expression were not linked with OS (both p > 0.05), while poor differentiation (p = 0.003) and advanced TNM stage (p < 0.001) were independently associated with worse OS.

CONCLUSIONS

TRPC1 is unregulated in NSCLC tissue with its overexpression relating to the occurrence of LYN metastasis and worse DFS in NSCLC patients.

Identification and Validation of a Malignant Cell Subset Marker-Based Polygenic Risk Score in Stomach Adenocarcinoma Through Integrated Analysis of Bulk and Single-Cell RNA Sequencing Data

Objectives: The aim of the present study was to construct a polygenic risk score (PRS) for poor survival among patients with stomach adenocarcinoma (STAD) based on expression of malignant cell markers.

Methods: Integrated analyses of bulk and single-cell RNA sequencing (scRNA-seq) of STAD and normal stomach tissues were conducted to identify malignant and non-malignant markers. Analyses of the scRNA-seq profile from early STAD were used to explore intratumoral heterogeneity (ITH) of the malignant cell subpopulations. Dimension reduction, cell clustering, pseudotime, and gene set enrichment analyses were performed. The marker genes of each malignant tissue and cell clusters were screened to create a PRS using Cox regression analyses. Combined with the PRS and routine clinicopathological characteristics, a nomogram tool was generated to predict prognosis of patients with STAD. The prognostic power of the PRS was validated in two independent external datasets.

Results: The malignant and non-malignant cells were identified according to 50 malignant and non-malignant cell markers. The malignant cells were divided into nine clusters with different marker genes and biological characteristics. Pseudotime analysis showed the potential differentiation trajectory of these nine malignant cell clusters and identified genes that affect cell differentiation. Ten malignant cell markers were selected to generate a PRS: RGS1, AADAC, NPC2, COL10A1, PRKCSH, RAMP1, PRR15L, TUBA1A, CXCR6, and UPP1. The PRS was associated with both overall and progression-free survival (PFS) and proved to be a prognostic factor independent of routine clinicopathological characteristics. PRS could successfully divide patients with STAD in three datasets into high- or low-risk groups. In addition, we combined PRS and the tumor clinicopathological characteristics into a nomogram tool to help predict the survival of patients with STAD.

Conclusion: We revealed limited but significant intratumoral heterogeneity in STAD and proposed a malignant cell subset marker-based PRS through integrated analysis of bulk sequencing and scRNA-seq data.

The Epithelial and Stromal Immune Microenvironment in Gastric Cancer: A Comprehensive Analysis Reveals Prognostic Factors with Digital Cytometry

Gastric cancer (GC) is the third leading cause of cancer-related deaths worldwide. Tumor heterogeneity continues to confound researchers' understanding of tumor growth and the development of an effective therapy. Digital cytometry allows interpretation of heterogeneous bulk tissue transcriptomes at the cellular level. We built a novel signature matrix to dissect epithelium and stroma signals using a scRNA-seq data set (GSE134520) for GC and then applied cell mixture deconvolution to estimate diverse epithelial, stromal, and immune cell proportions from bulk transcriptome data in four independent GC cohorts (GSE62254, GSE15459, GSE84437, and TCGA-STAD) from the GEO and TCGA databases. Robust computational methods were applied to identify strong prognostic factors for GC. We identified an EMEC population whose proportions were significantly higher in patients with stage I cancer than other stages, and it was predominantly present in tumor samples but not typically found in normal samples. We found that the ratio of EMECs to stromal cells and the ratio of adaptive T cells to monocytes were the most significant prognostic factors within the non-immune and immune factors, respectively. The STEM score, which unifies these two prognostic factors, was an independent prognostic factor of overall survival (HR = 0.92, 95% CI = 0.89-0.94, p=2.05×10-9). The entire GC cohort was stratified into three risk groups (high-, moderate-, and low-risk), which yielded incremental survival times (p<0.0001). For stage III disease, patients in the moderate- and low-risk groups experienced better survival benefits from radiation therapy ((HR = 0.16, 95% CI = 0.06-0.4, p<0.0001), whereas those in the high-risk group did not (HR = 0.49, 95% CI = 0.14-1.72, p=0.25). We concluded that the STEM score is a promising prognostic factor for gastric cancer.

Identification of a Novel Signature Predicting Overall Survival in Head and Neck Squamous Cell Carcinoma

Background: Head and neck squamous cell carcinoma (HNSCC) is a highly heterogeneous tumor with a high incidence and poor prognosis. Therefore, effective predictive models are needed to evaluate patient outcomes and optimize treatment. Methods: Robust Rank Aggregation (RRA) method was used to identify highly robust differentially-expressed genes (DEGs) between HNSCC and normal tissue in 9 GEO and TCGA datasets. Univariate Cox regression analysis and Lasso Cox regression analysis were performed to identify DEGs related to the Overall survival (OS) and to construct a prognostic gene signature (HNSCCSig). External validation was performed using GSE65858 dataset. Moreover, comprehensive bioinformatics analyses were used to identify the association between HNSCCSig and tumor immune environment. Results: A total of 257 reliable DEGs were identified by differentially analysis result of TCGA and GSE65858 datasets. The HNSCCSig including 7 mRNAs (SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3) were developed and validated to identify high-risk group who had a worse OS than low-risk group in TCGA and GSE65858 datasets. Cox regression analysis showed that the HNSCCSig could independently predict OS in both the TCGA and the GSE65858 datasets. Further research demonstrated that the infiltration bundance of CD8 + T cells, B cells, neutrophils, and NK cells were significantly lower in the high-risk group. A nomogram was also constructed by combining the HNSCCSig and clinical characters. Conclusion: We established and validated the HNSCCSig consisting of SLURP1, SCARA5, CLDN10, MYH11, CXCL13, HLF, and ITGA3. A nomogram combining HNSCCSig and some clinical parameters was constructed to identify high-risk HNSCC-patients with poor prognosis.

A construction and comprehensive analysis of ceRNA networks and infiltrating immune cells in papillary renal cell carcinoma

Background

As the second most common malignancy in adults, papillary renal cell carcinoma (PRCC) has shown an increasing trend in both incidence and mortality. Effective treatment for advanced metastatic PRCC is still lacking. In this study, we aimed to establish competitive endogenous RNA (ceRNA) networks related to PRCC tumorigenesis, and analyze the specific role of differentially expressed ceRNA components and infiltrating immune cells in tumorigenesis.

Methods

CeRNA networks were established to identify the key ceRNAs related to PRCC tumorigenesis based on the 318 samples from The Cancer Genome Atlas database (TCGA), including 285 PRCC and 33 normal control samples. The R package, “CIBERSORT,” was used to evaluate the infiltration of 22 types of immune cells. Then we identified the significant ceRNAs and immune cells, based on which two nomograms were obtained for predicting the prognosis in PRCC patients. Finally, we investigated the co‐expression of PRCC‐specific immune cells and core ceRNAs via Pearson correlation test.

Results

COL1A1, H19, ITPKB, LDLR, TCF4, and WNK3 were identified as hub genes in ceRNA networks. Four prognostic‐related tumor‐infiltrating immune cells, including T cells CD4 memory resting, Macrophages M1, and Macrophages M2 were revealed. Pearson correlation test indicated that Macrophage M1 was negatively related with COL1A1 (p < 0.01) and LDLR (p < 0.01), while Macrophage M2 was positively related with COL1A1 (p < 0.01), TCF4 (p < 0.01), and H19 (p = 0.032). Two nomograms were conducted with favorable accuracies (area under curve of 1‐year survival: 0.935 and 0.877; 3‐year survival: 0.849 and 0.841; and 5‐year survival: 0.818 and 0.775, respectively).

Conclusion

The study constructed two nomograms suited for PRCC prognosis predicting. Moreover, we concluded that H19‐miR‐29c‐3p‐COL1A1 axis might promote the polarization of M2 macrophages and inhibit M1 macrophage activation through Wnt signaling pathway, collaborating to promote PRCC tumorigenesis and lead to poor overall survival of PRCC patients.

Identification of the Novel Methylated Genes' Signature to Predict Prognosis in INRG High-Risk Neuroblastomas

BACKGROUND

Neuroblastomas are the most frequent extracranial pediatric solid tumors. The prognosis of children with high-risk neuroblastomas has remained poor in the past decade. A powerful signature is required to identify factors associated with prognosis and improved treatment selection. Here, we identified a strong methylation signature that favored the earlier diagnosis of neuroblastoma in patients.

METHODS

Gene methylation (GM) data of neuroblastoma patients from the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) were analyzed using a multivariate Cox regression analysis (MCRA) and univariate Cox proportional hazards regression analysis (UCPHRA).

RESULTS

The methylated genes' signature consisting of eight genes (NBEA, DDX28, TMED8, LOC151174, EFNB2, GHRHR, MIMT1, and SLC29A3) was selected. The signature divided patients into low- and high-risk categories, with statistically significant survival rates (median survival time: 25.08 vs. >128.80 months, log-rank test, P < 0.001) in the training group, and the validation of the signature's risk stratification ability was carried out in the test group (log-rank test, P < 0.01, median survival time: 30.48 vs. >120.36 months). The methylated genes' signature was found to be an independent predictive factor for neuroblastoma by MCRA. Functional enrichment analysis suggested that these methylated genes were related to butanoate metabolism, beta-alanine metabolism, and glutamate metabolism, all playing different significant roles in the process of energy metabolism in neuroblastomas.

CONCLUSIONS

The set of eight methylated genes could be used as a new predictive and prognostic signature for patients with INRG high-risk neuroblastomas, thus assisting in treatment, drug development, and predicting survival.

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

Background

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

Method

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

Results

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

Conclusion

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

Identification of novel prognostic biomarkers by integrating multi-omics data in gastric cancer

BACKGROUND

Gastric cancer is a fatal gastrointestinal cancer with high morbidity and poor prognosis. The dismal 5-year survival rate warrants reliable biomarkers to assess and improve the prognosis of gastric cancer. Distinguishing driver mutations that are required for the cancer phenotype from passenger mutations poses a formidable challenge for cancer genomics.

METHODS

We integrated the multi-omics data of 293 primary gastric cancer patients from The Cancer Genome Atlas (TCGA) to identify key driver genes by establishing a prognostic model of the patients. Analyzing both copy number alteration and somatic mutation data helped us to comprehensively reveal molecular markers of genomic variation. Integrating the transcription level of genes provided a unique perspective for us to discover dysregulated factors in transcriptional regulation.

RESULTS

We comprehensively identified 31 molecular markers of genomic variation. For instance, the copy number alteration of WASHC5 (also known as KIAA0196) frequently occurred in gastric cancer patients, which cannot be discovered using traditional methods based on significant mutations. Furthermore, we revealed that several dysregulation factors played a hub regulatory role in the process of biological metabolism based on dysregulation networks. Cancer hallmark and functional enrichment analysis showed that these key driver (KD) genes played a vital role in regulating programmed cell death. The drug response patterns and transcriptional signatures of KD genes reflected their clinical application value.

CONCLUSIONS

These findings indicated that KD genes could serve as novel prognostic biomarkers for further research on the pathogenesis of gastric cancers. Our study elucidated a multidimensional and comprehensive genomic landscape and highlighted the molecular complexity of GC.

Utility of a metabolic-associated nomogram to predict the recurrence-free survival of stage I cervical cancer

Aims: To identify metabolism-associated genes (MAGs) that serve as biomarkers to predict prognosis associated with recurrence-free survival (RFS) for stage I cervical cancer (CC). Patients & methods: By analyzing the Gene Expression Omnibus (GEO) database for 258 cases of stage I CC via univariate Cox analysis, LASSO and multivariate Cox regression analysis, we unveiled 11 MAGs as a signature that was also validated using Kaplan-Meier and receiver operating characteristic analyses. In addition, a metabolism-related nomogram was developed. Results: High accuracy of this signature for prediction was observed (area under the curve at 1, 3 and 5 years was 0.964, 0.929 and 0.852 for the internal dataset and 0.759, 0.719 and 0.757 for the external dataset). The high-risk score group displayed markedly worse RFS than did the low-risk score group. The indicators performed well in our nomogram. Conclusions: We identified a novel signature as a biomarker for predicting prognosis and a nomogram to facilitate the individual management of stage I CC patients.

Development and validation of metabolism-related gene signature in prognostic prediction of gastric cancer

Gastric cancer is one of the most common malignant tumours in the world. As one of the crucial hallmarks of cancer reprogramming of metabolism and the relevant researches have a promising application in the diagnosis treatment and prognostic prediction of malignant tumours. This study aims to identify a group of metabolism-related genes to construct a prediction model for the prognosis of gastric cancer.

A large cohort of gastric cancer cases (1121 cases) from public database was included in our analysis and classified patients into training and testing cohorts at a ratio of 7: 3. After identifying a list of metabolism-related genes having prognostic value, we constructed a risk score based on metabolism-related genes using LASSO-COX method. According to the risk score, patients were divided into high- and low-risk groups. Our results revealed that high-risk patients had a significantly worse prognosis than low-risk patients in both the training (high-risk vs low-risk patients; five years overall survival: 37.2% vs 72.2%; p < 0.001) and testing cohorts (high-risk vs low-risk patients; five years overall survival: 42.9% vs 62.9%; p < 0.001). This observation was validated in the external validation cohort (high-risk vs. low-risk patients; five years overall survival: 30.2% vs 40.4%; p = 0.007).

To reinforce the predictive ability of the model, we integrated risk score, age, adjuvant chemotherapy, and TNM stage into a nomogram. According to the result of receiver operating characteristic curves and decision curves analysis, we found that the nomogram score had a superior predictive ability than conventional factors, indicating that the risk score combined with clinicopathological features can develop a robust prediction for survival and improve the individualized clinical decision making of the patient.

In conclusion, we identified a list of metabolic genes related to survival and developed a metabolism-based predictive model for gastric cancer. Through a series of bioinformatics and statistical analyses, the predictive ability of the model was confirmed.

POC1A acts as a promising prognostic biomarker associated with high tumor immune cell infiltration in gastric cancer

The effect of POC1 centriolar protein A (POC1A) on gastric cancer (GC) has not been clearly defined. In this study, POC1A expression and clinical information in patients with GC were analyzed. Multiple databases were used to investigate the genes that were co-expressed with POC1A and genes whose changes co-occurred with genetic alternations of POC1A. Moreover, the TISIDB and TIMER databases were used to analyze immune infiltration. The GSE54129 GC dataset and LASSO regression model (tumor vs. normal) were employed, and 6 significant differentially expressed genes (LAMP5, CEBPB, ARMC9, PAOX, VMP1, POC1A) were identified. POC1A was selected for its high expression in adjacent tissues, which was confirmed with IHC. High POC1A expression was related to better overall and recurrence-free survival. GO and KEGG analyses demonstrated that POC1A may regulate the cell cycle, DNA replication and cell growth. Furthermore, POC1A was found to be correlated with immune infiltration levels in GC according to the TISIDB and TIMER databases. These findings indicate that POC1A acts as a tumor suppressor in GC by regulating the cell cycle and cell growth. In addition, POC1A preferentially regulates the immune infiltration of GC via several immune genes. However, the specific mechanism requires further study.

Molecular subtyping of glioblastoma based on immune-related genes for prognosis

Glioblastoma (GBM) is associated with an increasing mortality and morbidity and is considered as an aggressive brain tumor. Recently, extensive studies have been carried out to examine the molecular biology of GBM, and the progression of GBM has been suggested to be correlated with the tumor immunophenotype in a variety of studies. Samples in the current study were extracted from the ImmPort and TCGA databases to identify immune-related genes affecting GBM prognosis. A total of 92 immune-related genes displaying a significant correlation with prognosis were mined, and a shrinkage estimate was conducted on them. Among them, the 14 most representative genes showed a marked correlation with patient prognosis, and LASSO and stepwise regression analysis was carried out to further identify the genes for the construction of a predictive GBM prognosis model. Then, samples in training and test cohorts were incorporated into the model and divided to evaluate the efficiency, stability, and accuracy of the model to predict and classify the prognosis of patients and to identify the relevant immune features according to the median value of RiskScore (namely, Risk-H and Risk-L). In addition, the constructed model was able to instruct clinicians in diagnosis and prognosis prediction for various immunophenotypes.

An Innovative Prognostic Model Based on Four Genes in Asian Patient with Gastric Cancer

Purpose

Gastric cancer (GC) has substantial biological differences between Asian and non-Asian populations, which makes it difficult to have a unified predictive measure for all people. We aimed to identify novel prognostic biomarkers to help predict the prognosis of Asian GC patients.

Materials and Methods

We investigated the differential gene expression between GC and normal tissues of GSE66229. Univariate, multivariate and Lasso Cox regression analyses were conducted to establish a four-gene-related prognostic model based on the risk score. The risk score was based on a linear combination of the expression levels of individual genes multiplied by their multivariate Cox regression coefficients. Validation of the prognostic model was conducted using The Cancer Genome Atlas (TCGA) database. A nomogram containing clinical characteristics and the prognostic model was established to predict the prognosis of Asian GC patients.

Results

Four genes (RBPMS2, RGN, PLEKHS1, and CT83) were selected to establish the prognostic model, and it was validated in the TCGA Asian cohort. Receiver operating characteristic analysis confirmed the sensitivity and specificity of the prognostic model. Based on the prognostic model, a nomogram containing clinical characteristics and the prognostic model was established, and Harrell’s concordance index of the nomogram for evaluating the overall survival significantly higher than the model only focuses on the pathologic stage (0.74 vs. 0.64, p < 0.001).

Conclusion

The four-gene-related prognostic model and the nomogram based on it are reliable tools for predicting the overall survival of Asian GC patients.

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.

Identification of a prognosis‑associated signature associated with energy metabolism in triple‑negative breast cancer

At present, a large number of exciting results have been found regarding energy metabolism within the triple-negative breast cancer (TNBC) field. Apart from aerobic glycolysis, a number of other catabolic pathways have also been demonstrated to participate in energy generation. However, the prognostic value of energy metabolism for TNBC currently remains unclear. In the present study, the association between gene expression profiles of energy metabolism and outcomes in patients with TNBC was examined using datasets obtained from the Gene Expression Omnibus and The Cancer Genome Atlas. In total, four robust TNBC subtypes were identified on the basis of negative matrix factorization clustering and gene expression patterns, which exhibited distinct immunological, molecular and prognostic (disease-free survival) features. The differentially expressed genes were subsequently identified from the subgroup that demonstrated the poorest prognosis compared with the remaining 3 subgroups, where their biological functions were assessed further by means of gene ontology enrichment analysis. Any signatures found to be associated with energy metabolism were then established using the Cox proportional hazards model to assess patient prognosis. According to results of Kaplan-Meier analysis, the constructed signature consisting of eight genes that were associated with energy metabolism distinguished patient outcomes into low- and high-risk groups. In addition, this signature, which was found to be markedly associated with the clinical characteristics of the patients, served as an independent factor in predicting TNBC patient prognosis. According to gene set enrichment analysis, the gene sets related to the high-risk group participated in the MAPK signal transduction pathway, focal adhesion and extracellular matrix receptor interaction, whilst those related to the low-risk group were revealed to be mainly associated with mismatch repair and propanoate metabolism. Findings from the present study shed new light on the role of energy metabolism within TNBC, where the eight-gene signature associated with energy metabolism constructed can be utilized as a new prognostic marker for predicting survival in patients with TNBC.

TRPC1 exacerbate metastasis in gastric cancer via ciRS-7/miR-135a-5p/TRPC1 axis

Metastasis is frequently occurred in end-stage GC. Nevertheless, the initiation and progression of metastasis in GC remains unclear. The transient receptor potential canonical (TRPC) has been confirmed to be crucial for metastasis in many kinds of tumors, including GC. However, the molecular mechanisms regulating TRPC1 is unclear. Therefore, we investigated the role and mechanisms of TRPC1 in GC metastasis. We first evaluated the role of TRPC1 in GC by searching the public database, and tested the expression of TRPC1 in 50 paired GC tissues by qRT-PCR and IHC assays. Then, we generated BGC-823-shTRPC1 cells and MKN-45-TRPC1 cells to investigate the effects of TRPC1 on metastasis in vitro. For the mechanism study, we applied luciferase reporter assay, RNA pull-down assay, as well as RIP assay to validate the interation of ciRS-7, miR-135a-5p and TRPC1 in GC cells. This study, we showed that TRPC1 exacerbate EMT in gastric cancer via ciRS-7/miR-135a-5p/TRPC1 axis, and target TRPC1 could be beneficial for end-stage GC patients.

Prediction of gastric cancer prognosis in the next-generation sequencing era

Gastric cancer (GC) is one of the most commonly diagnosed malignancies worldwide, and is caused by complex interactions of multiple risk factors such as environmental (Helicobacter pylori and Epstein–Barr Virus), hereditary (genetic alterations and epigenetic modifications), as well as dietary and lifestyle factors. GC is usually detected at an advanced stage, with a dismal prognosis. Even for patients with similar clinical or pathologic stage receiving similar treatment, the outcomes are still uneven and unpredictable. To better incorporate genetic and epigenetic profiles into GC prognostic predication, gene expression signatures have been developed to predict GC outcomes. More recently, the advancement of high-throughput sequencing technology, also known as next-generation sequencing (NGS) technology, and analysis has provided the basis for accurate molecular classification of GC tumors. Here, we summarized and updated the literature related to NGS studies of GC, including whole-genome sequencing, whole-exome sequencing, RNA sequencing, and targeted sequencing, and discussed current progresses. NGS has facilitated the identification of genetic/epigenetic targets for screening as well as development of targeted agent therapy, thus enabling individualized patient management and treatment.

The exploration of disease-specific gene regulatory networks in esophageal carcinoma and stomach adenocarcinoma

BACKGROUND

Feed-forward loops (FFLs), consisting of miRNAs, transcription factors (TFs) and their common target genes, have been validated to be important for the initialization and development of complex diseases, including cancer. Esophageal Carcinoma (ESCA) and Stomach Adenocarcinoma (STAD) are two types of malignant tumors in the digestive tract. Understanding common and distinct molecular mechanisms of ESCA and STAD is extremely crucial.

RESULTS

In this paper, we presented a computational framework to explore common and distinct FFLs, and molecular biomarkers for ESCA and STAD. We identified FFLs by combining regulation pairs and RNA-seq data. Then we constructed disease-specific co-expression networks based on the FFLs identified. We also used random walk with restart (RWR) on disease-specific co-expression networks to prioritize candidate molecules. We identified 148 and 242 FFLs for these two types of cancer, respectively. And we found that one TF, E2F3 was related to ESCA, two genes, DTNA and KCNMA1 were related to STAD, while one TF ESR1 and one gene KIT were associated with both of the two types of cancer.

CONCLUSIONS

This proposed computational framework predicted disease-related biomolecules effectively and discovered the correlation between two types of cancers, which helped develop the diagnostic and therapeutic strategies of Esophageal Carcinoma and Stomach Adenocarcinoma.

A 21‑gene Support Vector Machine classifier and a 10‑gene risk score system constructed for patients with gastric cancer

Gastric cancer (GC) ranks fifth in terms of incidence and third in terms of tumor mortality worldwide. The present study was designed to construct a Support Vector Machine (SVM) classifier and risk score system for GC. The GSE62254 (training set) and GSE26253 (validation set 2) datasets were downloaded from the Gene Expression Omnibus database. Furthermore, the gene expression profile of GC (validation set 1) was obtained from The Cancer Genome Atlas database. Differentially expressed genes (DEGs) between recurrent and non‑recurrent samples were determined using the limma package. The feature genes were selected using the Caret package, and an SVM classifier was built using the e1071 package. Using the penalized package, the optimal predictive genes for constructing a risk score system were screened. Finally, stratification analysis of clinical factors and pathway enrichment analysis were performed using Gene Set Enrichment Analysis. A total of 239 DEGs were identified in GSE62254, among which 114 DEGs were significantly associated with both recurrence‑free survival and overall survival. Subsequently, 21 feature genes were screened from the 114 DEGs, and an SVM classifier was built. A risk score system for survival prediction was constructed, following the selection of 10 optimal genes, including A‑kinase anchoring protein 12, angiopoietin‑like protein 1, cysteine‑rich sequence 1, myeloid/lymphoid or mixed‑lineage leukemia, translocated to chromosome 11, neuron navigator 3, neurobeachin, nephroblastoma overexpressed, pleiotrophin, tumor suppressor candidate 3 and zinc finger and SCAN domain containing 18. The stratification analysis revealed that pathological stage was an independent prognostic clinical factor in the high‑risk group. Additionally, eight significant pathways were associated with the 10‑gene signature. The SVM classifier and risk score system may be applied for classifying and predicting the prognosis of patients with GC, respectively.

Glycolysis promotes the progression of pancreatic cancer and reduces cancer cell sensitivity to gemcitabine

Previous studies have reported that increased glycolytic activity enhances chemotherapy resistance in some types of malignancies. However, whether glycolysis influences the curative effect of gemcitabine (GEM) on pancreatic cancer (PC) cells remains unclear. The aim of this study was to investigate the status of glycolysis in PC and its association with tolerance to GEM. Data from The Cancer Genome Atlas (TCGA) were used to analyze the correlation between glycolysis-related gene (GRG) expression and PC progression and prognosis. 2-Deoxy-D-glucose (2-DG) was applied to assess the effect of glycolysis inhibition on PC cell death and GEM tolerance. Expression of some GRGs, such as HK1, GAPDH, PKM2, and LDHA, was significantly associated with the prognosis of PC. Furthermore, HK1, PKLR, and LDHA expression correlated positively with PC progression. Further analysis revealed that cancer cell death was markedly enhanced following glycolysis inhibition and that the sensitivity of cancer cells to GEM was notably increased in the presence of 2-DG. Our findings indicate that abnormally increased glycolytic activity promotes the development of PC and enhances drug tolerance to GEM. 2-DG combined with GEM is a potential therapy for PC.

High-level expression of PRSS3 correlates with metastasis and poor prognosis in patients with gastric cancer

BACKGROUND AND OBJECTIVES

Serine protease-3 (PRSS3) is a known contributor to the genesis and development of malignant tumors, although its role in gastric cancer (GC) is still unclear.

METHODS

PRSS3 expression in GC tissue samples and its relationship with clinicopathological features were analyzed. Effects of GC cellular responses to the introduction of small interfering RNA (siRNA)-mediated and short hairpin RNA (shRNA)-mediated interference with tumor PRSS3 expression were also assessed.

RESULTS

PRSS3 was significantly upregulated in GC tissues, and PRSS3 protein levels were higher in tumors that developed metastases soon after the surgery compared with those that remained metastasis-free. High expression of PRSS3 was associated with tumor N staging and independently predictive of postoperative prognosis in patients with GC. The V1 variant of PRSS3 was primarily detected in GC tissue and cell lines, the others (V2-V4) being scarcely detectable. Methylation and demethylation drugs had no impact on expression levels of any PRSS3 transcriptional variant. The downregulated PRSS3 expression suppressed GC cell growth, migration, and invasion in vitro and in vivo.

CONCLUSIONS

PRSS3 appears to act as an oncogene of GC. High PRSS3 expression portends postoperative metastasis, serving as an effective biomarker of poor therapeutic outcomes.

Screening responsive or resistant biomarkers of immune checkpoint inhibitors based on online databases

Immune checkpoint inhibitors are a promising strategy in the treatment of cancer, especially advanced types. However, not all patients are responsive to immune checkpoint inhibitors. The response rate depends on the immune microenvironment, tumor mutational burden (TMB), expression level of immune checkpoint proteins, and molecular subtypes of cancers. Along with the Cancer Genome Project, various open access databases, including The Cancer Genome Atlas and Gene Expression Omnibus, provide large volumes of data, which allow researchers to explore responsive or resistant biomarkers of immune checkpoint inhibitors. In this review, we introduced some methodologies on database selection, biomarker screening, current progress of immune checkpoint blockade in solid tumor treatment, possible mechanisms of drug resistance, strategies of overcoming resistance, and indications for immune checkpoint inhibitor therapy.

Prognostic value of gastric cancer-associated gene signatures: Evidence based on a meta-analysis using integrated bioinformatics methods

Selecting differentially expressed genes (DEGs) based on integrated bioinformatics analyses has been used in previous studies to explore potential biomarkers in gastric cancer (GC) with microarray and RNA sequencing data. However, the genes obtained may be inaccurate because of noisy data and errors, as well as insufficient clinical sample sizes. Thus, we aimed to find robust and strong DEGs with prognostic value for GC, where the robust rank aggregation method was employed to select significant DEGs from eight Gene Expression Omnibus data sets with a total of 140 up‐regulated and 206 down‐regulated genes. Network data mining was then used to screen hub genes, and 11 genes were filtered using Fisher's exact test. Based on these results, we built a prognostic signature with seven genes (FBN1,MMP1,PLAU,SPARC,COL1A2,COL2A1 and ATP4A) using stepwise multivariate Cox proportional hazard regression. According to the risk score for each patient, we found that high‐risk group patients had significantly worse survival results compared with those in the low‐risk group (log‐rank test P‐value < 0.001). This seven‐gene signature was then validated with an external data set. Thus, we established a signature based on seven DEGs with prognostic value for GC patients using multi‐steps bioinformatics methods, which may provide novel insights and potential biomarkers for prognosis, as well as possibly serving as new therapeutic targets in clinical applications.

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.

TIMP2 is a Poor Prognostic Factor and Predicts Metastatic Biological Behavior in Gastric Cancer

To explore the prognostic related factors and mechanisms of gastric cancer (GC), we performed the systematic analysis with integrated bioinformatics tools based on multiple on-line datasets. With uni-variate COX analysis, we screened out 37 survival hazardous genes in GC. Further GO assays disclosed that the signatures related with extracellular matrix and structure, and the functions of "cell adhesion molecule binding" and "integrin binding" were the vital mechanisms of disease progression, and tissue inhibitor of metalloproteinase-2 (TIMP2) was the potential biomarker for prognosis. Based on GSEA, GSVA and GCN, TIMP2 was demonstrated to interact with multiple integrin pathways and involve in the regulation of EMT, cell adhesion, and angiogenesis of GC. The associations of TIMP2 expression with reduced OS and RFS of patients were declared by Kaplan-Meier analysis, and further confirmed by 1000 internal bootstrap replications and external KM plotter analysis. With multi-variate COX regression and time-dependent ROC analysis, we validated the prediction independency and capacity of TIMP2 for prognosis. The relationships of TIMP2 with clinicopathological characteristics were also uncovered. Taken together, our findings identify TIMP2 as the novel candidate biomarker for poorer outcome of GC patients, and revealed the underlying functions of TIMP2 and the potential mechanisms for GC progression.

Identification of a novel gene pairs signature in the prognosis of gastric cancer

Current prognostic signatures need to be improved in identifying high-risk patients of gastric cancer (GC). Thus, we aimed to develop a reliable prognostic signature that could assess the prognosis risk in GC patients. Two microarray datasets of GSE662254 (n = 300, training set) and GSE15459 (n = 192, test set) were included into analysis. Prognostic genes were screened to construct prognosis-related gene pairs (PRGPs). Then, a penalized Cox proportional hazards regression model identified seven PRGPs, which constructed a prognostic signature and divided patients into high- and low-risk groups according to the signature score. High-risk patients showed a poorer prognosis than low-risk patients in both the training set (hazard ratios [HR]: 6.086, 95% confidence interval [CI]: 4.341-8.533) and test set (1.773 [1.107-2.840]). The PRGPs signature also achieved a higher predictive accuracy (concordance index [C-index]: 0.872, 95% CI: 0.846-0.897) than two existing molecular signatures (0.706 [0.667-0.744] for a 11-gene signature and 0.684 [0.642-0.726] for a 24-lncRNA signature) and TNM stage (0.764 [0.715-0.814]). In conclusion, our study identified a novel gene pairs signature in the prognosis of GC.