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

SIVA-1 interaction with PCBP1 serves as a predictive biomarker for cisplatin sensitivity in gastric cancer and its inhibitory effect on tumor growth in vivo

Background: SIVA-1 has been reported to play a key role in cell apoptosis and gastric cancer (GC) chemoresistance in vitro. Nevertheless, the clinical significance of SIVA-1 in GC chemotherapy remains unclear. Methods and results: Immunohistochemistry and histoculture drug response assays were used to determine SIVA-1 expression and the inhibition rate (IR) of agents to GC and to further analyze the relationship between these two phenomena. Additionally, cisplatin (DDP)-resistant GC cells were used to elucidate the role and mechanism of SIVA-1 in vivo. The results demonstrated that SIVA-1 expression was positively correlated with the IR of DDP to GC but not with those of 5-fluorouracil (5-FU) or adriamycin (ADM). Furthermore, SIVA-1 overexpression with DDP treatment synergistically inhibited tumor growth in vivo by increasing PCBP1 and decreasing Bcl-2 and Bcl-xL expression. Conclusions: Our study demonstrated that SIVA-1 may serve as an indicator of the GC sensitivity to DDP, and the mechanism of SIVA-1 in GC resistance to DDP was preliminarily revealed.

ImmuneScore of eight-gene signature predicts prognosis and survival in patients with endometrial cancer

Background

Endometrial cancer (EC) is a common gynecological cancer worldwide and the sixth most common female malignant tumor. A large number of studies conducted through database mining have identified many biomarkers that may be related to survival and prognosis. However, the predictive ability of single-gene biomarkers is not sufficiently accurate. In recent years, tumors have been shown to interact closely with their microenvironment, and tumor-infiltrating immune cells in the tumor microenvironment were associated with therapeutic effects. Furthermore, sequencing technology has evolved and allowed the identification of genetic signatures that may improve prediction results. The purpose of this research was to discover the Cancer Genome Atlas (TCGA) data to evaluate new genetic features that can predict the prognosis of EC.

Methods

mRNA expression profiling was analyzed in patients with EC identified in the TCGA database (n = 530). Differentially expressed genes at different stages of EC were screened using the immune cell enrichment score (ImmuneScore). Univariate and multivariate Cox regression analyses was applied to evaluate genes significantly related to overall survival and establish the prognostic risk parameter formula. Kaplan-Meier survival curves and the logarithmic rank method were applied to verify the importance of risk parameters for the prognostic forecast. The accuracy of survival prediction was confirmed using the nomogram and Receiver Operating Characteristic (ROC) curve analysis. The mRNA expression of eight genes were measured by qRT-PCR. According to COX and HR values, NBAT1, a representative gene among 8 genes, was selected for CCK-8 assay, colony formation assay and transwell invasion assay to verify the effect on survival.

Results

Eight related genes (NBAT1, GFRA4, PTPRT, DLX4, RANBP3L, UNQ6494, KLRB1, and PRAC1) were discovered to be significantly associated with the overall survival rate. According to these eight-gene signatures, 530 patients with EC were assigned to high- and low-risk subgroups. The prognostic capability of the eight-gene signature was not influenced by other elements.

Conclusions

Eight related gene markers were identified using ImmuneScore, which could predict prognosis and survival in patients with EC. These findings provide a basis for understanding the application of biological information in tumors and identifying the poor prognosis of EC.

The Role of Alternative Splicing Factors, DDB2-Related Ageing and DNA Damage Repair in the Progression and Prognosis of Stomach Adenocarcinoma Patients

DNA damage response is a key signal transduction pathway in triggering ageing and tumor progression. Abnormal alternative splicing (AS) is associated with tumors and ageing. However, the role of AS factors associated with DNA damage repair and ageing in stomach adenocarcinoma (STAD) remains unclear. We downloaded the percentage of splicing (PSI) values for AS in STAD from the TCGA SpliceSeq database. The PSI values of DNA repair gene AS events were integrated with STAD patient survival data for Cox regression analysis. The prediction model for the overall survival (OS) was constructed by the clinical traits. The tumor immune microenvironment was analyzed by CIBERSORT and ESTIMATE. We detected 824 AS events originating from 166 DNA repair genes. Cox regression analysis provided 21 prognostic AS events connected with OS statistically, and a prognostic prediction model was constructed. The expression of these AS factors was higher in STAD tumors. DDB2 high senescence levels were associated with active immune responses and better survival in STAD patients. We built a novel prognostic model founded on DNA repair genes with AS events and identified that DDB2 may be a potential biomarker to apply in clinics.

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.

A novel signature to predict thyroid cancer prognosis and immune landscape using immune-related LncRNA pairs

BACKGROUND

Thyroid cancer (TC) is the most common endocrine malignancy worldwide. The incidence of TC is high and increasing worldwide due to continuous improvements in diagnostic technology. Therefore, identifying accurate prognostic predictions to stratify TC patients is important.

METHODS

Raw data were downloaded from the TCGA database, and pairwise comparisons were applied to identify differentially expressed immune-related lncRNA (DEirlncRNA) pairs. Then, we used univariate Cox regression analysis and a modified Lasso algorithm on these pairs to construct a risk assessment model for TC. We further used qRT‒PCR analysis to validate the expression levels of irlncRNAs in the model. Next, TC patients were assigned to high- and low-risk groups based on the optimal cutoff score of the model for the 1-year ROC curve. We evaluated the signature in terms of prognostic independence, predictive value, immune cell infiltration, immune status, ICI-related molecules, and small-molecule inhibitor efficacy.

RESULTS

We identified 14 DEirlncRNA pairs as the novel predictive signature. In addition, the qRT‒PCR results were consistent with the bioinformatics results obtained from the TCGA dataset. The high-risk group had a significantly poorer prognosis than the low-risk group. Cox regression analysis revealed that this immune-related signature could predict prognosis independently and reliably for TC. With the CIBERSORT algorithm, we found an association between the signature and immune cell infiltration. Additionally, immune status was significantly higher in low-risk groups. Several immune checkpoint inhibitor (ICI)-related molecules, such as PD-1 and PD-L1, showed a negative correlation with the high-risk group. We further discovered that our new signature was correlated with the clinical response to small-molecule inhibitors, such as sunitinib.

CONCLUSIONS

We have constructed a prognostic immune-related lncRNA signature that can predict TC patient survival without considering the technical bias of different platforms, and this signature also sheds light on TC's overall prognosis and novel clinical treatments, such as ICB therapy and small molecular inhibitors.

Transcriptomic signature associated with RNA-binding proteins for survival stratification of laryngeal cancer

RNA-binding proteins (RBPs) have been suggested as important prognostic indicators in different human cancers. This study was designed to search the prognostic value of RBPs of laryngeal squamous cell carcinoma (LSCC). Differentially expressed RBPs (DERBPs) were screened via The Cancer Genome Atlas (TCGA). Bioinformatics methods were used to identify prognostic DERBPs. Expression profiling of training cohort were calculated to develop a transcriptomic signature, which was validated by three independent cohorts (TCGA cohort, GSE65858 cohort and GSE27020 cohort). We identified DERBPs and a set of signatures (GTPBP3, KHDRBS3 and RBM38) were confirmed as prognosis-related hub DERBPs in LSCC, which was also tested and verified by bioinformatics method and molecular biology experiment. The role of immune cell infiltration and drug resistance between subgroups was explored. Furthermore, the risk score based on transcriptomic signature was turned out to be an independent prognostic indicator for LSCC. Finally, a nomogram for further clinical application was established. Our study demonstrated that the transcriptomic signature we constructed could serve as a novel therapeutic target and biomarker for LSCC from the perspective of RBPs.

Molecular Typing of Gastric Cancer Based on Invasion-Related Genes and Prognosis-Related Features

Background

This study aimed to construct a prognostic stratification system for gastric cancer (GC) using tumour invasion-related genes to more accurately predict the clinical prognosis of GC.

Methodology

Tumour invasion-related genes were downloaded from CancerSEA, and their expression data in the TCGA-STAD dataset were used to cluster samples via non-negative matrix factorisation (NMF). Differentially expressed genes (DEGs) between subtypes were identified using the limma package. KEGG pathway and GO functional enrichment analyses were conducted using the WebGestaltR package (v0.4.2). The immune scores of molecular subtypes were evaluated using the R package ESTIMATE, MCPcounter and the ssGSEA function of the GSVA package. Univariate, multivariate and lasso regression analyses of DEGs were performed using the coxph function of the survival package and the glmnet package to construct a RiskScore model. The robustness of the model was validated using internal and external datasets, and a nomogram was constructed based on the model.

Results

Based on 97 tumour invasion-related genes, 353 GC samples from TCGA were categorised into two subtypes, thereby indicating the presence of inter-subtype differences in prognosis. A total of 569 DEGs were identified between the two subtypes; of which, four genes were selected to construct the risk model. This four-gene signature was robust and exhibited stable predictive performance in different platform datasets (GSE26942 and GSE66229), indicating that the established model performed better than other existing models.

Conclusion

A prognostic stratification system based on a four-gene signature was developed with a desirable area under the curve in the training and independent validation sets. Therefore, the use of this system as a molecular diagnostic test is recommended to assess the prognostic risk of patients with GC.

Identification of a novel m6A-related lncRNA pair signature for predicting the prognosis of gastric cancer patients

Background

Accumulating studies have demonstrated that lncRNAs play vital roles in the prognosis of gastric cancer (GC); however, the prognostic value of N6-methyladenosine-related lncRNAs has not been fully reported in GC. This study aimed to construct and validate an m6A-related lncRNA pair signature (m6A-LPS) for predicting the prognosis of GC patients.

Methods

GC cohort primary data were downloaded from The Cancer Genome Atlas. We analysed the coexpression of m6A regulators and lncRNAs to identify m6A-related lncRNAs. Based on cyclical single pairing along with a 0-or-1 matrix and least absolute shrinkage and selection operator-penalized regression analyses, we constructed a novel prognostic signature of m6A-related lncRNA pairs with no dependence upon specific lncRNA expression levels. All patients were divided into high-risk and low-risk group based on the median risk score. The predictive reliability was evaluated in the testing dataset and whole dataset with receiver operating characteristic (ROC) curve analysis. Gene set enrichment analysis was used to identify potential pathways.

Results

Fourteen m6A-related lncRNA pairs consisting of 25 unique lncRNAs were used to construct the m6A-LPS. Kaplan–Meier analysis showed that the high-risk group had poor prognosis. The area under the curve for 5-year overall survival was 0.906, 0.827, and 0.882 in the training dataset, testing dataset, and whole dataset, respectively, meaning that the m6A-LPS was highly accurate in predicting GC patient prognosis. The m6A-LPS served as an independent prognostic factor for GC patients after adjusting for other clinical factors (p < 0.05). The m6A-LPS had more accuracy and a higher ROC value than other prognostic models for GC. Functional analysis revealed that high-risk group samples mainly showed enrichment of extracellular matrix receptor interactions and focal adhesion. Moreover, N-cadherin and vimentin, known biomarkers of epithelial–mesenchymal transition, were highly expressed in high-risk group samples. The immune infiltration analysis showed that resting dendritic cells, monocytes, and resting memory CD4 T cells were significantly positively related to the risk score. Thus, m6A-LPS reflected the infiltration of several types of immune cells.

Conclusions

The signature established by pairing m6A-related lncRNAs regardless of expression levels showed high and independent clinical prediction value in GC patients.

Combining Algorithms to Find Signatures That Predict Risk in Early-Stage Stomach Cancer

This study applied two mathematical algorithms, lattice up-stream targeting (LUST) and D-basis, to the identification of prognostic signatures from cancer gene expression data. The LUST algorithm looks for metagenes, which are sets of genes that are either overexpressed or underexpressed in the same patients. Whereas LUST runs unsupervised by clinical data, the D-basis algorithm uses implications and association rules to relate gene expression to clinical outcomes. The D-basis selects a small subset of the metagene (a signature) to predict survival. The two algorithms, LUST and D-basis, were combined and applied to mRNA expression and clinical data from The Cancer Genome Atlas (TCGA) for 203 stage 1 and 2 stomach cancer patients. Two small (four-gene) signatures effectively predict survival in early-stage stomach cancer patients. These signatures could be used as a guide for treatment. The first signature (DU4) consists of genes that are underexpressed on the long-survival/low-risk group: FLRT2, KCNB1, MYOC, and TNXB. The second signature consists of genes that are overexpressed on the short-survival/high-risk group: ASB5, SFRP1, SMYD1, and TACR2. Another nine-gene signature (REC9) predicts recurrence: BNC2, CCDC8, DPYSL3, MOXD1, MXRA8, PRELP, SCARF2, TAGLN, and ZNF423. Each patient is assigned a score that is a linear combination of the expression levels for the genes in the signature. Scores below a selected threshold predict low-risk/long survival, whereas high scores indicate a high risk of short survival. The metagenes associate with TCGA cluster C1. Both our signatures and cluster C1 identify tumors that are genomically silent, and have a low mutation load or mutation count. Furthermore, our signatures identify tumors that are predominantly in the WHO classification of poorly cohesive and the Lauren class of diffuse samples, which have a poor prognosis.

Identification and Validation of a Five-Gene Signature Associated With Overall Survival in Breast Cancer Patients

BACKGROUND

Recent years, the global prevalence of breast cancer (BC) was still high and the underlying molecular mechanisms remained largely unknown. The investigation of prognosis-related biomarkers had become an urgent demand.

RESULTS

In this study, gene expression profiles and clinical information of breast cancer patients were downloaded from the TCGA database. The differentially expressed genes (DEGs) were estimated by Gene Ontology (GO) analysis and Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis. A risk score formula involving five novel prognostic associated biomarkers (EDN2, CLEC3B, SV2C, WT1, and MUC2) were then constructed by LASSO. The prognostic value of the risk model was further confirmed in the TCGA entire cohort and an independent external validation cohort. To explore the biological functions of the selected genes, in vitro assays were performed, indicating that these novel biomarkers could markedly influence breast cancer progression.

CONCLUSIONS

We established a predictive five-gene signature, which could be helpful for a personalized management in breast cancer patients.

Prognostic and predictive role of a metabolic rate-limiting enzyme signature in hepatocellular carcinoma

OBJECTIVES

Abnormal expression of metabolic rate-limiting enzymes drives the occurrence and progression of hepatocellular carcinoma (HCC). This study aimed to elucidate the comprehensive model of metabolic rate-limiting enzymes associated with the prognosis of HCC.

MATERIALS AND METHODS

HCC animal model and TCGA project were used to screen out differentially expressed metabolic rate-limiting enzyme. Cox regression, least absolute shrinkage and selection operation (LASSO) and experimentally verification were performed to identify metabolic rate-limiting enzyme signature. The area under the receiver operating characteristic curve (AUC) and prognostic nomogram were used to assess the efficacy of the signature in the three HCC cohorts (TCGA training cohort, internal cohort and an independent validation cohort).

RESULTS

A classifier based on three rate-limiting enzymes (RRM1, UCK2 and G6PD) was conducted and serves as independent prognostic factor. This effect was further confirmed in an independent cohort, which indicated that the AUC at year 5 was 0.715 (95% CI: 0.653-0.777) for clinical risk score, whereas it was significantly increased to 0.852 (95% CI: 0.798-0.906) when combination of the clinical with signature risk score. Moreover, a comprehensive nomogram including the signature and clinicopathological aspects resulted in significantly predict the individual outcomes.

CONCLUSIONS

Our results highlighted the prognostic value of rate-limiting enzymes in HCC, which may be useful for accurate risk assessment in guiding clinical management and treatment decisions.

A united risk model of 11 immune‑related gene pairs and clinical stage for prediction of overall survival in clear cell renal cell carcinoma patients

Clear cell renal cell carcinoma (ccRCC) is the most common subtype of renal cancer. Currently, we lack effective risk models for the prognosis of ccRCC patients. Given the significant role of cancer immunity in ccRCC, we aimed to establish a novel united risk model including clinical stage and immune-related gene pairs (IRGPs) to assess the prognosis. The gene expression profile and clinical data of ccRCC patients from The Cancer Genome Atlas and Arrayexpress were divided into training cohort (n = 381), validation cohort 1 (n = 156), and validation cohort 2 (n = 101). Through univariate Cox regression analysis and Least Absolute Shrinkage and Selection Operator analysis, 11 IRGPs were obtained. After further analysis, it was found that clinical stage could be an independent prognostic factor; hence, we used it to construct a united prognostic model with 11 IRGPs. Based on this model, patients were divided into high-risk and low-risk groups. In Kaplan–Meier analysis, a significant difference was observed in overall survival (OS) among all three cohorts (p < 0.001). The calibration curve revealed that the signature model is in high accordance with the observed values of each data cohort. The 1-year, 3-year, and 5-year receiver operating characteristic curves of each data cohort showed better performance than only IRGP signatures. The results of immune infiltration analysis revealed significantly (p < 0.05) higher abundance of macrophages M0, T follicular helper cells, and other tumor infiltrating cells. In summary, we successfully established a united prognostic risk model, which can effectively assess the OS of ccRCC patients.

Identification of novel antioxidant gene signature to predict the prognosis of patients with gastric cancer

Background

Gastric cancer (GC) commonly relates to dismal prognosis and lacks efficient biomarkers. This study aimed to establish an antioxidant-related gene signature and a comprehensive nomogram to explore novel biomarkers and predict GC prognosis.

Methods

Clinical and expression data of GC patients were extracted from The Cancer Genome Atlas database. Univariate and multivariate Cox analyses were utilized to construct a score-based gene signature and survival analyses were conducted between high- and low-risk groups. Furthermore, we established a prognostic nomogram integrating clinical variables and antioxidant-related gene signature. Its predictive ability was validated by Harrell' concordance index and calibration curves and an independent internal cohort verified the consistency of the antioxidant gene signature-based nomogram.

Results

Four antioxidant-related genes (CHAC1, GGT5, GPX8, and PXDN) were significantly associated with overall survival of GC patients but only two genes, CHAC1 (HR = 0.803, P < 0.05) and GPX8 (HR = 1.358, P < 0.05), were confirmed as independent factors. A score-based signature was constructed and could act as an independent prognosis predictor (P < 0.05). Patients with lower scores showed significantly better prognosis (P < 0.05). Comprehensive nomogram combining the antioxidant-related gene signature and clinical parameters (age, gender, grade, and stage) was established and effectively predicted overall survival of GC patients [3-year survival AUC = 0.680, C index = 0.665 (95% CI 0.614–0.716)]. The independent internal validation cohort verified the reliability and good consistency of the model [3-year survival AUC = 0.703, C index = 0.706 (95% CI 0.612–0.800)].

Conclusions

Innovative antioxidant-related gene signature and nomogram performed well in assessing GC prognoses. This study enlightened further investigation of antioxidant system and provided novel tools for GC patient management.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12957-021-02328-w.

A novel immune-related gene pair prognostic signature for predicting overall survival in bladder cancer

Background

Bladder cancer (BC) is the ninth most common malignant tumor. We constructed a risk signature using immune-related gene pairs (IRGPs) to predict the prognosis of BC patients.

Methods

The mRNA transcriptome, simple nucleotide variation and clinical data of BC patients were downloaded from The Cancer Genome Atlas (TCGA) database (TCGA-BLCA). The mRNA transcriptome and clinical data were also extracted from Gene Expression Omnibus (GEO) datasets (GSE31684). A risk signature was built based on the IRGPs. The ability of the signature to predict prognosis was analyzed with survival curves and Cox regression. The relationships between immunological parameters [immune cell infiltration, immune checkpoints, tumor microenvironment (TME) and tumor mutation burden (TMB)] and the risk score were investigated. Finally, gene set enrichment analysis (GSEA) was used to explore molecular mechanisms underlying the risk score.

Results

The risk signature utilized 30 selected IRGPs. The prognosis of the high-risk group was significantly worse than that of the low-risk group. We used the GSE31684 dataset to validate the signature. Close relationships were found between the risk score and immunological parameters. Finally, GSEA showed that gene sets related to the extracellular matrix (ECM), stromal cells and epithelial-mesenchymal transition (EMT) were enriched in the high-risk group. In the low-risk group, we found a number of immune-related pathways in the enriched pathways and biofunctions.

Conclusions

We used a new tool, IRGPs, to build a risk signature to predict the prognosis of BC. By evaluating immune parameters and molecular mechanisms, we gained a better understanding of the mechanisms underlying the risk signature. This signature can also be used as a tool to predict the effect of immunotherapy in patients with BC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08486-0.

Immune-Related lncRNA Signature for Predicting the Immune Landscape of Head and Neck Squamous Cell Carcinoma

Background: Long non-coding RNA (lncRNA) plays a significant role in the development, establishment, and progression of head and neck squamous cell carcinoma (HNSCC). This article aims to develop an immune-related lncRNA (irlncRNA) model, regardless of expression levels, for risk assessment and prognosis prediction in HNSCC patients. Methods: We obtained clinical data and corresponding full transcriptome expression of HNSCC patients from TCGA, downloaded GTF files to distinguish lncRNAs from Ensembl, discerned irlncRNAs based on co-expression analysis, distinguished differentially expressed irlncRNAs (DEirlncRNAs), and paired these DEirlncRNAs. Univariate Cox regression analysis, LASSO regression analysis, and stepwise multivariate Cox regression analysis were then performed to screen lncRNA pairs, calculate the risk coefficient, and establish a prognosis model. Finally, the predictive power of this model was validated through the AUC and the ROC curves, and the AIC values of each point on the five-year ROC curve were calculated to select the maximum inflection point, which was applied as a cut-off point to divide patients into low- or high-risk groups. Based on this methodology, we were able to more effectively differentiate between these groups in terms of survival, clinico-pathological characteristics, tumor immune infiltrating status, chemotherapeutics sensitivity, and immunosuppressive molecules. Results: A 13-irlncRNA-pair signature was built, and the ROC analysis demonstrated high sensitivity and specificity of this signature for survival prediction. The Kaplan-Meier analysis indicated that the high-risk group had a significantly shorter survival rate than the low-risk group, and the chi-squared test certified that the signature was highly related to survival status, clinical stage, T stage, and N stage. Additionally, the signature was further proven to be an independent prognostic risk factor via the Cox regression analyses, and immune infiltrating analyses showed that the high-risk group had significant negative relationships with various immune infiltrations. Finally, the chemotherapeutics sensitivity and the expression level of molecular markers were also significantly different between high- and low-risk groups. Conclusion: The signature established by paring irlncRNAs, with regard to specific expression levels, can be utilized for survival prediction and to guide clinical therapy in HNSCC.

Discovery and Verification of an Immune-Related Gene Pairs Signature for Predicting Prognosis in Head and Neck Squamous Cell Carcinoma

The study of IRGPs to construct the prognostic signature in head and neck squamous cell carcinoma (HNSCC) has not yet elucidated. The objective of this study was to explore a novel model to predict the prognosis of HNSCC patients. The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) datasets were set as training and validation cohorts, respectively. The least absolute shrinkage and selection operator (LASSO) and time-dependent ROC were employed to screen the highest frequency immune-related gene pairs (IRGPs) and their best cut-off value. Survival analysis, Cox regression analysis were applied to discover the effects of selected IRGPs signature on survival outcomes. The immune cell proportions were deconvoluted by the CIBERSORT method. After a couple of filtering, we obtained 22 highest frequency IRGPs. The overall survival time of HNSCC patients with a high score of IRGPs was shorter as compared to the ones with a low score in two independent datasets (P < 0.001). Six kinds of immune cells were found to be differentially distributed in the two different risk groups of HNSCC patients (P < 0.001). GO and GSEA analysis showed these differentially expressed genes enriched in multiple molecular functions. The new IRGPs signature probably confers a new insight into the prognosis prediction of HNSCC patients.

WGCNA identification of TLR7 as a novel diagnostic biomarker, progression and prognostic indicator, and immunotherapeutic target for stomach adenocarcinoma

Stomach adenocarcinoma (STAD) is a malignant tumor with high histological heterogeneity. However, the potential mechanism of STAD tumorigenesis remains to be elucidated. The purpose of our research was to identify candidate genes associated with the diagnosis, progression, prognosis, and immunotherapeutic targets of STAD. Based on tumor samples from the GSE28541 dataset, weighted gene co-expression network analysis revealed 16 modules related to STAD stage and grade. The salmon module emerged as the most relevant module (cor = 0.34), and functional enrichment analysis showed that the genes in the salmon were primarily related to major histocompatibility complex, immune response, and cell differentiation. Toll-like receptor 7 (TLR7) was recognized as the real hub gene in the salmon module. Compared to normal stomach tissues, the transcriptional and translational levels of TLR7 were significantly elevated in STAD. Receiver operating characteristic curves verified that TLR7 displayed remarkable sensitivity and specificity for the diagnosis of STAD. The functions of TLR7 were primarily enriched in the regulation of Toll-like receptor signaling pathway, pattern recognition receptor signaling pathway, and innate immune response. Overexpression of TLR7 tended to indicate more advanced STAD, higher degree of STAD, and poorer prognosis of STAD. In addition, TLR7 expression was positively correlated with immune cell infiltration and immune checkpoint expression. Somatic copy number alteration of TLR7 was also significantly related to immune cell infiltration. In conclusion, this study revealed the crucial role of TLR7 in STAD and provided new perspectives for the selection of biomarkers, progression and prognosis indicators, and immunotherapeutic targets for STAD.

Identification of Novel Gene Signature Associated with Cell Glycolysis to Predict Survival in Hepatocellular Carcinoma Patients

Purpose

As hepatocellular carcinoma (HCC) is a complex disease, it is hard to classify HCC with a specific biomarker. This study used data from TCGA to create a genetic signature for predicting the prognosis of HCC patients.

Methods

In a group of HCC patients (n = 424) from TCGA, mRNA profiling was carried out. To recognize gene sets that differed significantly between HCC and normal tissues, an enrichment study of genes was carried out. Cox relative hazard regression models have been used to identify genes that are significantly associated with overall survival. To test the function of a prognostic risk parameter, the following multivariate Cox regression analysis was used. The log-rank test and Kaplan–Meier survival estimates were used to test the significance of risk parameters for predictive prognoses.

Results

Eight genes have been identified as having a significant link to overall survival (PAM, NUP155, GOT2, KDELR3, PKM, NSDHL, ENO1, and SRD5A3). The 377 HCC patients were divided into eight-gene signature-based high/low-risk subgroups. The eight-gene signature's prognostic ability was unaffected by a number of factors.

Conclusion

To predict the survival of patients with HCC, an eight-gene signature associated with cellular glycolysis was then identified. The findings shed light on cellular glycolysis processes and the diagnosis of patients with low HCC prognoses.

Identification of a Novel Epithelial-Mesenchymal Transition Gene Signature Predicting Survival in Patients With HNSCC

Head and neck squamous cell cancer (HNSCC) is one of the most common types of cancer worldwide. There have been many reports suggesting that biomarkers explored via database mining plays a critical role in predicting HNSCC prognosis. However, a single biomarker for prognostic analysis is not adequate. Additionally, there is growing evidence indicating that gene signature could be a better choice for HNSCC prognosis. We performed a comprehensive analysis of mRNA expression profiles using clinical information of HNSCC patients from The Cancer Genome Atlas (TCGA). Gene Set Enrichment Analysis (GSEA) was performed, and we found that a set of genes involved in epithelial mesenchymal transition (EMT) contributed to HNSCC. Cox proportional regression model was used to identify a four-gene (WIPF1, PPIB, BASP1, PLOD2) signature that were significantly associated with overall survival (OS), and all the four genes were significantly upregulated in tumor tissues. We successfully classified the patients with HNSCC into high-risk and low-risk groups, where in high-risk indicated poorer patient prognosis, indicating that this gene signature might be a novel potential biomarker for the prognosis of HNSCC. The prognostic ability of the gene signature was further validated in an independent cohort from the Gene Expression Omnibus (GEO) database. In conclusion, we identified a four-EMT-based gene signature which provides the potentiality to serve as novel independent biomarkers for predicting survival in HNSCC patients, as well as a new possibility for individualized treatment of HNSCC.

A signature of 17 immune-related gene pairs predicts prognosis and immune status in HNSCC patients

Head and neck squamous cell carcinoma (HNSCC) is an invasive malignancy with high worldwide mortality. Growing evidence has indicated a pivotal correlation between HNSCC prognosis and immune signature. This study investigated an immune-related gene pairs (IRGPs) signature to predict the prognostic value of HNSCC patients. We constructed IRGPs via integrating multiple IRG expression data sets. Moreover, we established the predictive model base on the IRGPs for HNSCC, and utilized multidimensional bioinformatics methods to validate the robustness of prognostic value of the IRGPs signature. In addition, we explored the relationship between the IRGPs model and immune status. Seventeen IRGPs signature was built as the predictive model which predicted prognosis independently and reliably for HNSCC. Compared to the high-risk group, the low-risk group demonstrated a distinctly favorable prognosis including overall survival (OS), disease-specific survival (DSS), and progression-free survival (PFS). The low-risk group showed higher-immune score and lower-tumor purity than the high-risk group. In addition, the low-risk group exhibited higher expression of Programmed cell death 1 ligand 1 (PD-L1) and Microsatellite instability (MSI) score, and lower expression of Tumor Immune Dysfunction and Exclusion (TIDE), which indicated the low-risk group was much more sensitive to immunotherapy. Lastly, the IRGs signature has achieved a higher accuracy than clinical properties for estimation of survival. The IRGPs model is an independent biomarker for estimating the prognosis, and could be also used to predict immunotherapeutic response in HNSCC patients. These findings may provide new ideas for novel biomarkers and may be helpful to formulate personalized immunotherapy strategy.

Identification of novel prognostic biomarkers in renal cell carcinoma

Objective: To identify novel prognostic biomarkers in renal cell carcinoma (RCC).

Results: 12 coding genes and one miRNA were finally identified as prognostic biomarkers. All of them were related to a poor prognosis. Lower expression levels of the coding genes were observed in higher clinical stages. Prognostic signatures including 7 biomarkers were identified. Patients in the high-risk group had worse survival than those in the low-risk group. The areas under the curves in different years indicated that it was a valuable signature in prognosis. It was found that elevated WDR72 inhibited the survival and invasion of 786-O and 769P cells in vitro.

Conclusions: Thirteen prognostic biomarkers of RCC were identified. Among them, 7 biomarkers comprised a signature to evaluate the RCC prognosis. WDR72 was a cancer suppressor and a potential therapeutic target in RCC.

Methods: Differentially expressed genes/miRNAs (DEGs/DEMs) and prognosis-related genes/miRNAs were acquired from public database. Prognostic biomarkers were identified by overlapping the significant DEGs/DEMs and prognosis-related genes/miRNAs. The associations between these biomarkers and the clinical stages were analyzed. All of these prognostic biomarkers were further investigated with multi-variable Cox regression. Finally, the inhibitory effect of WDR72 on the growth and invasion of RCC cells was studied.

A signature of 18 immune-related gene pairs to predict the prognosis of pancreatic cancer patients

Pancreatic cancer is one of the most lethal malignancies. With the promising prospects conveyed by immunotherapy in cancers, we aimed to construct an immune‐related gene pairs (IRGPs) signature to predict the prognosis of pancreatic cancer patients. We downloaded clinical and transcriptional data of pancreatic cancer patients from The Cancer Genome Atlas data set as the training group and GSE57495 data set as the verification group. We filtered immune‐related transcriptional data by IMMPORT. With the assistance of lasso penalized Cox regression, we constructed our prognostic IRGPs signature and divided all samples into high‐/low‐risk groups by receiver operating characteristic curve for further comparisons. The comparisons between high‐ and low‐risk groups including survival rate, multivariate, and univariate Cox proportional‐hazards analysis, infiltration of immune cells, and Gene Set Enrichment Analysis (GSEA). Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) are facilitated to analyze the proceedings in which our IRGPs signature may involve in. The results revealed that 18 IRGPs were defined as our prognostic signature. The prognostic value of this IRGPs signature was verified from the GSE57495 data set. We further demonstrated the independent prognostic value of this IRGPs signature. The contents of six immune cells between high‐/low‐risk groups were different, which was associated with the progression of diverse cancers. Results from GO, KEGG, and GSEA revealed that this IRGPs signature was involved in extracellular space, immune response, cancer pathways, cation channel, and gated channel activities. Evidently, this IRGPs signature will provide remarkable value for the therapy of pancreatic cancer 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.

Seven-Gene Signature Based on Glycolysis Is Closely Related to the Prognosis and Tumor Immune Infiltration of Patients With Gastric Cancer

Background: Gastric cancer (GC) is one of the most common malignancies worldwide, exhibiting a high morbidity, and mortality. As the various treatment methods for gastric cancer are limited by disadvantages, many efforts to improve the efficacy of these treatments are being taken. Metabolic recombination is an important characteristic of cancer and has gradually caused a recent upsurge in research. However, systematic analysis of the interaction between glycolysis and GC patient prognosis and its potential associations with immune infiltration is lacking but urgently needed.

Methods: We obtained the gene expression data and clinical materials of GC derived from The Cancer Genome Atlas (TCGA) dataset. Univariate and multivariate Cox proportional regression analyses were performed to select the optimal prognosis-related genes for subsequent modeling. We then validated our data in the GEO database and further verified the gene expression using the Oncomine database and PCR experiments. Besides, Gene set variation analysis (GSVA) analysis was employed to further explore the differences in activation status of biological pathways between the high and low risk groups. Furthermore, a nomogram was adopted to predict the individualized survival rate of GC patients. Finally, a violin plot and a TIMMER analysis were performed to analyse the characteristics of immune infiltration in the microenvironment.

Results: A seven-gene signature, including STC1, CLDN9, EFNA3, ZBTB7A, NT5E, NUP50, and CXCR4, was established. Based on this seven-gene signature, the patients in the training set and testing sets could be divided into high-risk and low-risk groups. In addition, a nomogram based on risk and age showed good calibration and moderate discrimination. The results proved that the seven-gene signature had a strong capacity to predict the GC patient prognosis. Collectively, the violin plot and TIMMER analysis demonstrated that an immunosuppressive tumor microenvironment caused by hyperglycolysis led to poor prognosis.

Conclusion: Taken together, these results established a genetic signature for gastric cancer based on glycolysis, which has reference significance for the in-depth study of the metabolic mechanism of gastric cancer and the exploration of new clinical treatment strategies.

Analysis of the expression, function, prognosis and co-expression genes of DDX20 in gastric cancer

DDX20 (DEAD-box polypeptide 20) is implicated in many cellular processes involving alteration of RNA secondary structure. The role of DDX20 in gastric cancer is still unknown. In the research, the expression of DDX20 and the functional roles of DDX20 in gastric cancer were detected. The increased DDX20 expression in gastric cancer tissue compared with normal gastric tissue was observed. Functional experiments indicated that DDX20 promoted gastric cancer MGC-803 and AGS cells growth, migration, and invasion in vitro. Surprisingly, survival analysis showed that high expression of DDX20 is a favorable prognostic factor for patients with gastric cancer. In addition, enrichment analysis revealed that there is a positive correlation between DDX20 expression and T cell activation in gastric cancer. but not in normal gastric tissues. Furthermore, we found that DDX20 expression level has significant positive correlations with activated CD8 + T cells and activated CD4 + T cells in gastric cancer. Therefore, we hypothesize that the prognostic role of DDX20 in gastric cancer patients may be due to patients with high DDX20 expression contained better immune activation. Taken together, these findings suggest that DDX20 can promote the progression of gastric cancer in vitro and its prognostic value in gastric cancer may be related to many factors, including immune activation.

miR-502-5p inhibits the proliferation, migration and invasion of gastric cancer cells by targeting SP1

Gastric cancer (GC) is the third most common cause of cancer-associated mortality in China. Aberrant microRNA (miR) expression can occur through multiple biological processes and has been implicated in cancer development. However, to the best of our knowledge, the function of miR-502-5p in GC is currently unclear. In the present study, the expression and function of miR-502-5p in GC was evaluated. Reverse transcription-quantitative (RT-q) PCR was used to measure the expression levels of miR-502-5p in GC tissues, normal adjacent tissues, a normal human gastric epithelial cell line (GES-1) and two GC cell lines. miR-502-5p expression levels were significantly lower in GC tissues and GC cell lines compared with those in adjacent normal tissues and GES-1 cells, respectively. Subsequently, the target genes of miR-502-5p were predicted, and it was demonstrated that the transcription factor SP1 was a direct target. SP1 expression, cell viability, migration and invasion, and SP1 protein levels were examined using RT-qPCR, an MTT assay, Transwell assay and western blotting, respectively. Human GC cells were then transfected with an miR-502-5p mimic to emulate miR-502-5p overexpression, resulting in inhibition of the proliferation, migration and invasion capacities of human GC cells. Compared with the negative control, cells overexpressing miR-502-5p had decreased levels of SP1 mRNA and protein. These data suggest that miR-502-5p serves as a tumor suppressor gene by targeting SP1 to regulate the proliferation, migration and invasion of GC cells.

29 immune-related genes pairs signature predict the prognosis of cervical cancer patients

To screen the key immune genes in the development of cervical cancer, construct immune related gene pairs (IRGPs), and evaluate their influence on the prognosis of cervical cancer. Tumor Genome Atlas (TCGA) database and geo database were downloaded as training set and validation set respectively, and immune related gene data were downloaded from immport. IRGPs model is established by machine learning, and the model is analyzed and evaluated. Using the Uclcan to analyze the immune genes expression in cervical cancer, and to further explore the association with the expression level and the clinical stage and prognosis of cervical cancer. According to the analysis of training set, we identified 29 IRGPs as key gene pairs and constructed the model. The AUC value of the model was greater than 0.9, and the model group survival rate was conspicuous different (P < 0.001). The reliability of the model was confirmed in the validation group. Our IRGPs play an important role in the occurrence and development of cervical cancer, and can be used as a prognostic marker and potential new target of cervical cancer.

Development and verification of a personalized immune prognostic feature in breast cancer

IMPACT STATEMENT

Breast cancer is among the highest prevalent malignant tumors worldwide with a low survival ratio. Immune-related genes have great potential as prognostic indicator in many types of tumors. Therefore, we have attempted to develop immune-related gene markers to enhance the prognosis of breast cancer. 17-IRGPs signature was constructed as a newly developed prognostic indicator to predict the survival of BC 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.

A novel serum miRNA-pair classifier for diagnosis of sarcoma

Soft tissue sarcomas (STS) is a set of rare malignant tumor originated from mesoderm. For the prognosis of sarcoma, early diagnosis is important, however, currently no mature and non-invasive method for diagnosis exists. MicroRNAs (miRNAs) are a class of noncoding RNAs and their expression varies greatly, especially during tumor activity. The purpose of this study was to construct a predictive model for the diagnosis of sarcomas based on the relative expression level of miRNA in serum. miRNA array expression data of 677 samples including 402 malignant sarcoma samples and 275 healthy samples was used to construct the prediction model. Based on 6 gene pairs, random generalized linear model (RGLM) was constructed, with an accuracy of 100% in the internal test dataset and of 74.3% in the merged external dataset in prediction whether a serum sample was obtained from a sarcoma patient, with a specificity of 100% in the internal test dataset and 90.5% in the external dataset. In conclusion, our serum miRNA-pair classifier has the potential to be used for the screening of sarcoma with high accuracy and specificity.

Development and Validation of an Individualized Immune Prognostic Signature for Recurrent Prostate Cancer

BACKGROUND

Immune-related genes possess promising prognostic potential in multiple cancer types. Here, we describe the development of an immune-related prognostic signature for predicting prostate cancer recurrence.

METHODS

Prostate cancer gene expression profiles for 477 prostate cases, as well as accompanying follow-up information were downloaded from The Cancer Genome Atlas (TCGA) and GEO. The samples were divided into 3 groups and immune gene sets significantly associated with prognosis were identified by evaluating the relationship between the expression of 1039 immune genes and prognosis in the training set. Relative expression levels of these genes were used to identify prognostic gene pairs. LASSO was used for feature selection and robust biomarkers selected. Finally, the identified immune prognostic markers were validated using dataset and GEO validation dataset and their performance compared with existing prognostic models.

RESULTS

In total, 87 immune genes, significantly associated with prognosis, were identified and 2447 immune gene pairs (IRGPs) established. Univariate survival analysis identified 641 prognosis-associated immune gene pairs. 8-IRGPs were obtained via LASSO feature selection and an 8-IRGPs signature established. The 8-IRGPs signature exhibited an independent prognosis value in prostate cancer of the training set, test set, and external validation set (p = <0.001). The 5- year survival AUC in both the training set and the validation set was >0.7. The 8-IRGPs outperformed clinical tumor classification features, including T, N, radiation therapy (RT) and targeted molecular therapy (TMT) (p <0.01). In addition, we compared the prognostic characteristics of 8-IRGPs with 3 reported prostate cancers and found that 8-IRGPs achieved a high C index (0.85) and had the highest predictive performance within 10 years of follow-up (HR: 10.5). Finally, we integrated T, N, RT, TMT, and 8-IRGPs and generated a novel alignment chart to aid the prediction of prostate cancer recurrence in individual patients (p <0.01).

CONCLUSION

Here, we identified an 8-IRGP novel prognostic signature for the prediction of prostate cancer recurrence.

An Individualized Immune Prognostic Index is a Superior Predictor of Survival of Hepatocellular Carcinoma

Background

The tumor microenvironment is largely orchestrated by the immune cells. Considerable evidence has shown their excellent clinicopathological application value in assessment of clinical outcomes and immunotherapy efficacy. Hence, a moderate, individualized prognostic signature based on immune cells that can estimate prognosis and reflect the immune microenvironment in hepatocellular carcinoma (HCC) patients is greatly needed.

Material/Methods

Here, we systematically analyzed the expression differences and survival prediction value of tumor infiltrating immune cells by analyzing 638 HCC patients from 3 public cohorts, including 2 microarray datasets and 1 RNA sequencing dataset. CIBERSORT software, a computational algorithm, was used to calculate the relative levels of immune cells. Three immune microenvironment subtypes were defined via ConsensuClusterPlus package. Univariate and multivariate survival analyses were used to develop an individualized immune prognostic index based on immune cell pairs.

Results

Notably, HCC patients with higher immune signatures score, utterly appreciable, suffered inferior prognosis (hazard ratio=2.742; 95% confidence interval: 1.887–3.983; P<0.001). Subgroup analysis suggested that the prognostic signature did particularly well in early-stage patients. Furthermore, moderate survival prediction value was also confirmed in another two independent cohorts GSE14520 and GSE76427.

Conclusions

This study provides a systematic view of the immune cells characteristics in HCC and suggests their superior survival monitoring performance.

A signature of 33 immune-related gene pairs predicts clinical outcome in hepatocellular carcinoma

Objective

Hepatocellular carcinoma (HCC) has become the second most common tumor type that contributes to cancer‐related death worldwide. The study aimed to establish a robust immune‐related gene pair (IRGP) signature for predicting the prognosis of HCC patients.

Methods

Two RNA‐seq datasets (The Cancer Genome Atlas Program and International Cancer Genome Consortium) and one microarray dataset (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE14520) were included in this study. We used a series of immune‐related genes from the ImmPort database to construct gene pairs. Lasso penalized Cox proportional hazards regression was employed to develop the best prognostic signature. We assigned patients into two groups with low immune risk and high immune risk. Then, the prognostic ability of the signature was evaluated by a log‐rank test and a Cox proportional hazards regression model.

Results

After 1000 iterations, the 33‐immune gene pair model obtained the highest frequency. As a result, we chose the 33 immune gene pairs to establish the immune‐related prognostic signature. As we expected, the immune‐related signature accurately predicted the prognosis of HCC patients, and high‐risk groups showed poor prognosis in the training datasets and testing datasets as well as in the validation datasets. Furthermore, the immune‐related gene pair (IRGP) signature also showed higher predictive accuracy than three existing prognostic signatures.

Conclusion

Our prognostic signature, which reflects the link between the immune microenvironment and HCC patient outcome, is promising for prognosis prediction in HCC.

Development and Validation of an RNA-Seq-Based Prognostic Signature in Neuroblastoma

Objective: The stratification of neuroblastoma (NBL) prognosis remains difficult. RNA-based signatures might be able to predict prognosis, but independent cross-platform validation is still rare.

Methods: RNA-Seq-based profiles from NBL patients were acquired and then analyzed. The RNA-Seq prognostic index (RPI) and the clinically adjusted RPI (RCPI) were successively established in the training cohort (TARGET-NBL) and then verified in the validation cohort (GSE62564). Survival prediction was assessed using a time-dependent receiver operating characteristic (ROC) curve and area under the ROC curve (AUC). Functional enrichment analysis of the genes was conducted using bioinformatics methods.

Results: In the training cohort, 10 gene pairs were eventually integrated into the RPI. In both cohorts, the high-risk group had poor overall survival (OS) (P < 0.001 and P < 0.001, respectively) and favorable event-free survival (EFS) (P = 0.00032 and P = 0.06, respectively). ROC curve analysis also showed that the RPI predicted OS (60 month AUC values of 0.718 and 0.593, respectively) and EFS (60 month AUC values of 0.627 and 0.852, respectively) well in both the training and validation cohorts. Clinicopathological indicators associated with prognosis in the univariate and multivariate regression analyses were identified and added to the RPI to form the RCPI. The RCPI was also used to divide populations into different risk groups, and the high-risk group had poor OS (P < 0.001 and P < 0.001, respectively) and EFS (P < 0.05 and P < 0.05, respectively). Finally, the RCPI had higher accuracy than the RPI for the prediction of OS (60 month AUC values of 0.730 and 0.852, respectively) and EFS (60 month AUC values of 0.663 and 0.763, respectively) in both the training and validation cohorts. Moreover, these differentially expressed genes may be involved in certain NBL-related events.

Conclusions: The RCPI could reliably categorize NBL patients based on different risks of death.

Identification of novel cell glycolysis related gene signature predicting survival in patients with endometrial cancer

BACKGROUND

Endometrial cancer (EC) is one of the three major gynecological malignancies. Numerous biomarkers that may be associated with survival and prognosis have been identified through database mining in previous studies. However, the predictive ability of single-gene biomarkers is not sufficiently specific. Genetic signatures may be an improved option for prediction. This study aimed to explore data from The Cancer Genome Atlas (TCGA) to identify a new genetic signature for predicting the prognosis of EC.

METHODS

mRNA expression profiling was performed in a group of patients with EC (n = 548) from TCGA. Gene set enrichment analysis was performed to identify gene sets that were significantly different between EC tissues and normal tissues. Cox proportional hazards regression models were used to identify genes significantly associated with overall survival. Quantitative real-time-PCR was used to verify the reliability of the expression of selected mRNAs. Subsequent multivariate Cox regression analysis was used to establish a prognostic risk parameter formula. Kaplan-Meier survival estimates and the log-rank test were used to validate the significance of risk parameters for prognosis prediction.

RESULT

Nine genes associated with glycolysis (CLDN9, B4GALT1, GMPPB, B4GALT4, AK4, CHST6, PC, GPC1, and SRD5A3) were found to be significantly related to overall survival. The results of mRNA expression analysis by PCR were consistent with those of bioinformatics analysis. Based on the nine-gene signature, the 548 patients with EC were divided into high/low-risk subgroups. The prognostic ability of the nine-gene signature was not affected by other factors.

CONCLUSION

A nine-gene signature associated with cellular glycolysis for predicting the survival of patients with EC was developed. The findings provide insight into the mechanisms of cellular glycolysis and identification of patients with poor prognosis in EC.

High expression of fibronectin 1 indicates poor prognosis in gastric cancer

Fibronectin 1 (FN1) is involved in the occurrence and development of various tumors and is upregulated in multiple cancer types. FN1 has been demonstrated to promote cell proliferation and migration in gastric cancer cell lines. However, the relationship between the expression of FN1 and clinicopathological factors and prognosis is not clear in gastric cancer (GC). The aim of the present study was to investigate the association between FN1 expression and clinicopathology and prognosis of gastric cancer. In this study, 17 publicly available GC cohorts (n=2,376) with gene expression data from the Gene Expression Omnibus (GEO), The Cancer Genome Atlas (TCGA) and Oncomine databases were tested. In addition, FN1 protein expression was validated by immunohistochemistry in a separate cohort (n=190). The meta-analysis results demonstrated an increase in FN1 expression at the protein and mRNA level in GC tissues, and the FN1 gene was highly expressed at the mRNA level in the advanced T stage (T2 + T3 + T4) group compared with that in the early T stage (T1) group. In addition, the expression of epithelial FN1 at the protein level was positively correlated with tumor size. FN1 expression at the protein and mRNA level was a predictor of poor prognosis following radical resection of GC. In conclusion, the expression of FN1 in GC tissues is upregulated compared with adjacent normal tissues, and it is a potential biomarker of poor prognosis in patients with GC.

MicroRNA-424 regulates cisplatin resistance of gastric cancer by targeting SMURF1 based on GEO database and primary validation in human gastric cancer tissues

PURPOSE

Cisplatin (DDP) based chemotherapy regimens are widely used in advanced gastric cancer (GC). Drug resistance often limited the clinical benefits of cisplatin regimen. The mechanisms of cisplatin resistance have not been fully revealed. Therefore, further exploration of the relevant molecular mechanisms is urgently needed.

PATIENTS AND METHODS

DDP resistance associated miRNA of GC microarray dataset GSE86195 was obtained from the National Center for Biotechnology Information (NCBI) GEO database, GEO2R was applied to compare the samples in two different groups under the same experimental conditions. |log2(Fold Change) | (log2(FC)) was selected as the criteria to screen the statistically significant DE-miRNAs. StarBaseV3.0 was used to predict the target genes of the DE-miRNAs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses of target genes of DE-miRNAs were carried out using DAVID. The STRING database was applied to estimate the correlations between target genes. Analysis of hubgenes by coremine and The Human Protein Atlas (THPA). Initial expression validations of miR-424 and miR-491-5p, SMURF1 and BCL2L1 were carried out using clinical pathological specimens by RT-PCR.

RESULTS

A total of 13 Differential expression-miRNAs (DE-miRNAs) were identified in DDP chemoresistant cells, including 9 upregulated miRNAs and 4 downregulated miRNAs. SMURF1 and BCL2L1 were screened as the critical genes in DDP-resistant GC, which were regulated by miR-424 and miR-491-5p respectively. The results of validation of hub genes expression in GC tissues indicated that in DFS<1-year group, the expression of miR-424 decreased significantly, notably upregulated expression of SMURF1 was also detected.

CONCLUSION

Our results implied that miR-424, as a tumor suppressor, could deregulate SMURF1 in DDP-resistant GC cells.

A novel gene-pair signature for relapse-free survival prediction in colon cancer

Background

Colon cancer (CC) patients with early relapse usually have a poor prognosis. In this study, we aimed to identify a novel signature to improve the prediction of relapse-free survival (RFS) in CC.

Methods

Four microarray datasets were merged into a training set (n=1,045), and one RNA-sequencing dataset was used as a validation set (n=384). In the training set, microarray meta-analysis screened out 596 common RFS-related genes across datasets, which were used to construct 177,310 gene pairs. Then, the LASSO penalized generalized linear model identified 16 RFS-related gene pairs, and a risk score was calculated for each sample according to the model coefficients.

Results

The risk score demonstrated a good ability in predicting RFS (area under the curve [AUC] at 5 years: 0.724; concordance index [C-index]: 0.642, 95% CI: 0.615–0.669). High-risk patients showed a poorer prognosis than low-risk patients (HR: 3.519, 95% CI: 2.870–4.314). Subgroup analysis reached consistent results when considering multiple confounders. In the validation set, the risk score had a similar performance (AUC at 5 years: 0.697; C-index: 0.696, 95% CI: 0.627–0.766; HR: 2.926, 95% CI: 1.892–4.527). When compared with a 13-gene signature, a 15-gene signature, and TNM stage, the score showed a better performance (P<0.0001; P=0.0004; P=0.0125), especially for the patients with a longer follow-up (R2=0.988, P<0.0001). When the follow-up was >5 years (n=314), the score demonstrated an excellent performance (C-index: 0.869, 95% CI: 0.816–0.922; HR: 13.55, 95% CI: 7.409–24.78).

Conclusion

Our study identified a novel gene-pair signature for prediction of RFS in CC.

Co-expression network analysis identified CDH11 in association with progression and prognosis in gastric cancer

Background and aims

Gastric cancer (GC) is one of the most common cancers worldwide, and its pathogenesis is related to a complex network of gene interactions. The aims of our study were to find hub genes associated with the progression and prognosis of GC and illustrate the underlying mechanisms.

Methods

Weighted gene co-expression network analysis (WGCNA) was conducted using the microarray dataset and clinical data of GC patients from Gene Expression Omnibus (GEO) database to identify significant gene modules and hub genes associated with TNM stage in GC. Functional enrichment analysis and protein-protein interaction network analysis were performed using the significant module genes. We regarded the common hub genes in the co-expression network and protein-protein interaction (PPI) network as "real" hub genes for further analysis. Hub gene was validated in another independent dataset and The Cancer Genome Atlas (TCGA) dataset.

Results

In the significant purple module (R ²=0.35), a total of 12 network hub genes were identified, among which six were also hub nodes in the PPI network of the module genes. Functional annotation revealed that the genes in the purple module focused on the biological processes of system development, biological adhesion, extracellular structure organization and metabolic process. In terms of validation, CDH11 had a higher correlation with the TNM stage than other hub genes and was strongly correlated with biological adhesion based on GO functional enrichment analysis. Data obtained from the Gene Expression Profiling Interactive Analysis (GEPIA) showed that CDH11 expression had a strong positive correlation with GC stages (P<0.0001). In the testing set and Oncomine dataset, CDH11 was highly expressed in GC tissues (P<0.0001). Survival analysis indicated that samples with a high CDH11 expression showed a poor prognosis. Cox regression analysis demonstrated an independent predictor of CDH11 expression in GC prognosis (HR=1.482, 95% CI: 1.015-2.164). Furthermore, gene set enrichment analysis (GSEA) demonstrated that multiple tumor-related pathways, especially focal adhesion, were enriched in CDH11 highly expressed samples.

Conclusion

CDH11 was identified and validated in association with progression and prognosis in GC, probably by regulating biological adhesion and focal adhesion-related pathways.

Identification of a novel cell cycle-related gene signature predicting survival in patients with gastric cancer

Gastric cancer (GC) is one of the most fatal cancers in the world. Thousands of biomarkers have been explored that might be related to survival and prognosis via database mining. However, the prediction effect of single gene biomarkers is not specific enough. Increasing evidence suggests that gene signatures are emerging as a possible better alternative. We aimed to develop a novel gene signature to improve the prognosis prediction of GC. Using the messenger RNA (mRNA)-mining approach, we performed mRNA expression profiling in a large GC cohort (n = 375) from The Cancer Genome Atlas (TCGA) database. Gene Set Enrichment Analysis (GSEA) was performed, and we recovered genes related to the G2/M checkpoint, which we identified with a Cox proportional regression model. We identified a set of five genes (MARCKS, CCNF, MAPK14, INCENP, and CHAF1A), which were significantly associated with overall survival (OS) in the test series. Based on this five-gene signature, the test series patients could be classified into high-risk or low-risk subgroups. Multivariate Cox regression analysis indicated that the prognostic power of this five-gene signature was independent of clinical features. In conclusion, we developed a five-gene signature related to the cell cycle that can predict survival for GC. Our findings provide novel insight that is useful for understanding cell cycle mechanisms and for identifying patients with GC with poor prognoses.

LncRNA CASC11 promoted gastric cancer cell proliferation, migration and invasion in vitro by regulating cell cycle pathway

In this study, we aimed to investigate the effects of lncRNA CASC11 on gastric cancer (GC) cell progression through regulating miR-340-5p and cell cycle pathway. Expressions of lncRNA CASC11 in gastric cancer tissues and cell lines were determined by qRT-PCR. Differentially expressed lncRNAs, mRNAs and miRNAs were screened through microarray analysis. The relationship among CASC11, CDK1 and miR-340-5p was predicted by TargetScan and validated through dual luciferase reporter assay. Western blot assay examined the protein level of CDK1 and several cell cycle regulatory proteins. GO functional analysis and KEGG pathway analysis were used to predict the association between functions and related pathways. Cell proliferation was determined by CCK-8 assays. Cell apoptosis and cell cycle were detected by flow cytometry assay. CASC11 was highly expressed in GC tissues and cell lines. Knockdown of CASC11 inhibited GC cell proliferation, promoted cell apoptosis and blocked cell cycle. KEGG further indicated an enriched cell cycle pathway involving CDK1. QRT-PCR showed that miR-340-5p was down-regulated in GC cells tissues, while CDK1 was up-regulated. Furthermore, CASC11 acted as a sponge of miR-340-5p which directly targeted CDK1. Meanwhile, miR-340-5p overexpression promoted GC cell apoptosis and induced cell cycle arrest, while CDK1 overexpression inhibited cell apoptosis and accelerated cell cycle. Our study revealed the mechanism of CASC11/miR-340-5p/CDK1 network in GC cell line, and suggested that CASC11 was a novel facilitator that exerted a biological effect by activating the cell cycle signaling pathway. This finding provides a potential therapeutic target for GC.

miR‑195‑5p regulates multi‑drug resistance of gastric cancer cells via targeting ZNF139

Gastric cancer (GC) is one of the most common malignant tumors with a high mortality rate. Reversing the multi-drug resistance (MDR) of GC offers the potential for significant enhancement of the effect of chemotherapy and improvement of prognosis. Aberrant microRNA expression can attribute to the pathogenesis of GC. However, the effects of microRNA (miR)-195-5p on the MDR of GC cells remains to be fully elucidated. In the present study, the effect of miR-195-5p in regulating the MDR of GC cells was investigated. Reverse transcription quantitative-polymerase chain reaction was used to analyze the levels of miR-195-5p in GC cells. Western blot analysis was performed to analyze the protein levels of ZNF139, P-gp, BCL-2 and MRP1. The chemosensitivity of GC cells was determined by MTT. The results showed that the expression of miR-195-5p was decreased in poorly differentiated GC tissues with a higher chemosensitivity. The overexpression of miR-195-5p promoted the chemosensitivity of GC cells. Bioinformatics analysis indicated that Zing finger 139 (ZNF139) was a target of miR-195-5p. miR-195-5p negatively regulated the expression of ZNF139 by binding to its 3′-untranslated region. The silencing of ZNF139 promoted the chemosensitivity of GC cells, and the downregulation of ZNF139 reversed the effect of miR-195-5p inhibitor on the chemosensitivity of GC cells. In conclusion, miR-195-5p regulated the MDR of GC cells via targeting ZNF139.