GeneExpressScore Signature: a robust prognostic and predictive classifier in gastric cancer

A novel nomogram for predicting overall survival in peripheral T cell lymphoma patients.. [DOI: 10.21203/rs.3.rs-2823604/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background The prognosis of peripheral T cell lymphomas (PTCLs) varies greatly. This study aimed at generating a prognostic nomogram based on differentially expressed genes (DEGs).Methods Firstly, we collected RNA transcripts from Gene Expression Omnibus and identified DEGs. Secondly we used univariate Cox regression, Least absolute shrinkage and selection operator (LASSO) to screen the independent risk factors to construct nomogram in the training cohort. Thirdly, we evaluate its prediction accuracy via decision curves analysis (DCA), receiver operating characteristic (ROC) and calibration rate to confirm its performance on survival in training and validation cohort. Then we carried out subgroup analysis in training and validation to eliminate the effects of age, gender, and pathological subtype. Lastly, to verify feasibility of nomogram in practice, we applied immunohistochemistry to clinical samples and analyzed the relationship between IHC scores and prognosis.Results The 702 DEGs between 40 PTCLs and 20 non-tumor patients were identified. Then ANGPTL2, CPSF4, CLIC4 and OTUD6B were screened out as independent risk factors via univariate Cox regression and LASSO. The DCA, ROC, Harrell’s concordance index (c-index) and calibration rate showed nomogram predicting more accurately than any single specific transcript. The results showed PTCLs with higher nomogram-score had a longer survival, regardless of age, gender and pathological subtype. Finally, the high expression level of ANGPTL2, CPSF4 and OTUD6B related to poor prognosis. Higher expression of CLIC4 related to longer survival.Conclusion This nomogram showed the favorable clinical applicability, regardless of age, gender and pathological subtype.

Preoperative 18F-FDG PET/CT radiomics analysis for predicting HER2 expression and prognosis in gastric cancer

Background

We aimed to establish and validate 2 machine learning models using ¹⁸F-flurodeoxyglucose positron emission tomography/computed tomography (¹⁸F-FDG PET/CT) radiomic features to predict human epidermal growth factor receptor 2 (HER2) expression and prognosis in gastric cancer (GC) patients.

Methods

We retrospectively enrolled 90 patients diagnosed with GC, including their clinical information and the ¹⁸F-FDG PET/CT images. Patients were allocated to a training cohort of 72 patients and an independent validation cohort (IVC) of 18 patients. There were 2,100 radiomic features extracted from the ¹⁸F-FDG PET/CT scans. A sequential combination of multivariate and univariate feature selection was applied, including sequential forward selection and a redundancy-based analysis. The justification of the model performance was conducted by cross-validation analysis on the training set and an independent validation analysis.

Results

The machine learning models were developed using a balanced bagging approach for HER2 expression prediction and prognosis prediction, which differentiated HER2 positive expression from negative expression in the IVC with an area under the receiver operating characteristic curve (AUC) of 0.72, sensitivity of 0.85, and specificity of 0.80. The IVC for prognosis prediction achieved an AUC of 0.75, sensitivity of 0.82, and specificity of 0.71. We also conducted a reasonable interpretation for the selected features in each classification task from multiple aspects, including normalized feature importance analysis and statistical correlation analysis with the clinical features that were defaulted to be effective.

Conclusions

¹⁸F-FDG PET/CT radiomics analysis with a machine learning model provides a quantitative, efficient, and objective mechanism for predicting HER2 expression and prognosis in GC patients.

Construction and validation of a macrophage polarization-related prognostic index to predict the overall survival in patients with early-stage triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) is a highly heterogeneous disease and the current prognostic system cannot meet the clinical need. Interactions between immune responsiveness and tumor cells plays a key role in the progression of TNBC and macrophages are vital component of immune cells. A prognostic model based on macrophages may have great accuracy and clinical utility.

Methods

For model development, we screened early stage (without metastasis) TNBC patients from The Cancer Genome Atlas (TCGA) database. We extracted messenger RNA (mRNA) expression data and clinical data including age, race, tumor size, lymph node status and tumor stage. The follow up time and vital status were also retrieved for overall survival calculation. Cell-type Identification by Estimating Relative Subsets of RNA Transcripts (CIBERSORT) was used to calculate the immune cell composition of each sample. Weighted gene co-expression network analysis (WGCNA) was used to identify M1-like macrophage-related genes. Combining least absolute shrinkage and selection operator (LASSO) with multivariate Cox regression, the M1-like macrophage polarization-related prognostic index (MRPI) was established. We obtained TNBC patients in Gene Expression Omnibus (GEO) database through PAM50 method and retrieved the mRNA expression data and survival data. The Harrell's concordance index (CI), the area under the receiver operating characteristic (ROC) curves (AUCs) and the calibration curve were used to evaluate the developed model.

Results

We obtained 166 early TNBC cases and 113 normal tissue cases for model building, along with 76 samples from GSE58812 cohort for model validation. CIBERSORT analysis suggested obvious infiltration of macrophages, especially M1-like macrophages in early TNBC. Four genes were eventually identified for the construction of MPRI in the training set. The AUCs at 2 years, 3 years, and 5 years in the training cohort were 0.855, 0.881 and 0.893, respectively; and the AUCs at 2 years, 3 years, and 5 years in the validation cohort were 0.887, 0.792 and 0.722, respectively. Calibration curves indicated good predictive ability and high consistency of our model.

Conclusions

MRPI is a promising biomarker for predicting the prognosis of early-stage TNBC, which may indicate personalized treatment and follow-up strategies and thus may improve the prognosis.

Identification and validation of a novel survival prediction model based on the T-cell phenotype in the tumor immune microenvironment and peripheral blood for gastric cancer prognosis

BACKGROUND

The correlation and difference in T-cell phenotypes between peripheral blood lymphocytes (PBLs) and the tumor immune microenvironment (TIME) in patients with gastric cancer (GC) is not clear. We aimed to characterize the phenotypes of CD8⁺ T cells in tumor infiltrating lymphocytes (TILs) and PBLs in patients with different outcomes and to establish a useful survival prediction model.

METHODS

Multiplex immunofluorescence staining and flow cytometry were used to detect the expression of inhibitory molecules (IMs) and active markers (AMs) in CD8⁺TILs and PBLs, respectively. The role of these parameters in the 3-year prognosis was assessed by receiver operating characteristic analysis. Then, we divided patients into two TIME clusters (TIME-A/B) and two PBL clusters (PBL-A/B) by unsupervised hierarchical clustering based on the results of multivariate analysis, and used the Kaplan-Meier method to analyze the difference in prognosis between each group. Finally, we constructed and compared three survival prediction models based on Cox regression analysis, and further validated the efficiency and accuracy in the internal and external cohorts.

RESULTS

The percentage of PD-1⁺CD8⁺TILs, TIM-3⁺CD8⁺TILs, PD-L1⁺CD8⁺TILs, and PD-L1⁺CD8⁺PBLs and the density of PD-L1⁺CD8⁺TILs were independent risk factors, while the percentage of TIM-3⁺CD8⁺PBLs was an independent protective factor. The patients in the TIME-B group showed a worse 3-year overall survival (OS) (HR: 3.256, 95% CI 1.318-8.043, P = 0.006), with a higher density of PD-L1⁺CD8⁺TILs (P < 0.001) and percentage of PD-1⁺CD8⁺TILs (P = 0.017) and PD-L1⁺CD8⁺TILs (P < 0.001) compared to the TIME-A group. The patients in the PBL-B group showed higher positivity for PD-L1⁺CD8⁺PBLs (P = 0.042), LAG-3⁺CD8⁺PBLs (P < 0.001), TIM-3⁺CD8⁺PBLs (P = 0.003), PD-L1⁺CD4⁺PBLs (P = 0.001), and LAG-3⁺CD4⁺PBLs (P < 0.001) and poorer 3-year OS (HR: 0.124, 95% CI 0.017-0.929, P = 0.015) than those in the PBL-A group. In our three survival prediction models, Model 3, which was based on the percentage of TIM-3⁺CD8⁺PBLs, PD-L1⁺CD8⁺TILs and PD-1⁺CD8⁺TILs, showed the best sensitivity (0.950, 0.914), specificity (0.852, 0.857) and accuracy (κ = 0.787, P < 0.001; κ = 0.771, P < 0.001) in the internal and external cohorts, respectively.

CONCLUSION

We established a comprehensive and robust survival prediction model based on the T-cell phenotype in the TIME and PBLs for GC prognosis.

A Novel mRNA Signature Related to Immunity to Predict Survival and Immunotherapy Response in Hepatocellular Carcinoma

BACKGROUND AND AIMS

Hepatocellular carcinoma (HCC) is the most common primary liver cancer and the incidence and mortality rates are increasing. Given the limited treatments of HCC and promising application of immunotherapy for cancer, we aimed to identify an immune-related prognostic signature that can predict overall survival (OS) rates and immunotherapy response in HCC.

METHODS

The initial signature development was conducted using a training dataset from the Cancer Genome Atlas followed by independent internal and external validations from that resource and the Gene Expression Omnibus. A signature based nomogram was generated using multivariate Cox regression analysis. The associations of signature score with tumor immune phenotype and response to immunotherapy were analyzed using single-sample gene set enrichment analysis and tumor immune dysfunction and exclusion algorithm. A cohort from Zhongshan Hospital was employed to verify the predictive robustness of the signature regarding prognostic risk and immunotherapy response.

RESULTS

The prognostic signature, IGS_HCC, consisting of 22 immune-related genes, had independent prognostic ability, with training and validation cohorts. Also, IGS_HCC stratified HCC patients with different outcomes in subgroups. The prognostic accuracy of IGS_HCC was better than three reported prognostic signatures. The IGS_HCC-based nomogram had high accuracy and significant clinical benefits in predicting 3- and 5-year OS. IGS_HCC reflected distinct immunosuppressive phenotypes in low- and high-score groups. Patients with low IGS_HCC scores were more likely than those with high scores to benefit from immunotherapy.

CONCLUSIONS

IGS_HCC predicted HCC prognosis and response to immunotherapy, and contributed to individualized clinical management.

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 an EMT-Related Gene Signature for Predicting Overall Survival in Gastric Cancer

Background

It has been widely reported that epithelial-mesenchymal transition (EMT) is associated with malignant progression in gastric cancer (GC). Integration of the molecules related to EMT for predicting overall survival (OS) is meaningful for understanding the role of EMT in GC. Here, we aimed to establish an EMT-related gene signature in GC.

Methods

Transcriptional profiles and clinical data of GC were downloaded from The Cancer Genome Atlas (TCGA). We constructed EMT-related gene signature for predicting OS by using univariate Cox regression and least absolute shrinkage and selection operator (LASSO) regression analyses. Time-dependent receiver operating characteristic (ROC), Kaplan-Meier analysis were performed to assess its predictive value. A nomogram combining the prognostic signature with clinical characteristics for OS prediction was established. And its predictive power was estimated by concordance index (C-index), time-dependent ROC curve, calibration curve and decision curve analysis (DCA). GSE62254 dataset from Gene Expression Omnibus (GEO) was used for external validation. Quantitative real-time PCR (qRT-PCR) was used to detected the mRNA expression of the five EMT-related genes in human normal gastric mucosal and GC cell lines. To further understand the potential mechanisms of the signature, Gene Set Enrichment Analysis (GSEA), pathway enrichment analysis, predictions of transcription factors (TFs)/miRNAs were performed.

Results

A novel EMT-related gene signature (including ITGAV, DAB2, SERPINE1, MATN3, PLOD2) was constructed for OS prediction of GC. With external validation, ROC curves indicated the signature’s good performance. Patients stratified into high- and low-risk groups based on the signature yielded significantly different prognosis. Univariate and multivariate Cox regression suggested that the signature was an independent prognostic variable. Nomogram for prognostication including the signature presented better predictive accuracy and clinical usefulness than the similar model without risk score to some extent with external validation. The qRT-PCR assays suggested that high expression of the five EMT-related genes could be found in human GC cell lines compared with normal gastric mucosal cell line. GSEA and pathway enrichment analysis revealed that focal adhesion and ECM-receptor interaction might be the two important pathways to the signature.

Conclusion

Our EMT-related gene signature may have practical application as an independent prognostic factor in GC.

Combining gene expression signature with clinical features for survival stratification of gastric cancer

The AJCC staging system is considered as the golden standard in clinical practice. However, it remains some pitfalls in assessing the prognosis of gastric cancer (GC) patients with similar clinicopathological characteristics. We aim to develop a new clinic and genetic risk score (CGRS) to improve the prognosis prediction of GC patients. We established genetic risk score (GRS) based on nine-gene signature including APOD, CCDC92, CYS1, GSDME, ST8SIA5, STARD3NL, TIMEM245, TSPYL5, and VAT1 based on the gene expression profiles of the training set from the Asian Cancer Research Group (ACRG) cohort by LASSO-Cox regression algorithms. CGRS was established by integrating GRS with clinical risk score (CRS) derived from Surveillance, Epidemiology, and End Results (SEER) database. GRS and CGRS dichotomized GC patients into high and low risk groups with significantly different prognosis in four independent cohorts with different data types, such as microarray, RNA sequencing and qRT-PCR (all HR > 1, all P < 0.001). Both GRS and CGRS were prognostic signatures independent of the AJCC staging system. Receiver operating characteristic (ROC) analysis showed that area under ROC curve of CGRS was larger than that of the AJCC staging system in most cohorts we studied. Nomogram and web tool (http://39.100.117.92/CGRS/) based on CGRS were developed for clinicians to conveniently assess GC prognosis in clinical practice. CGRS integrating genetic signature with clinical features shows strong robustness in predicting GC prognosis, and can be easily applied in clinical practice through the web application.

The prediction of survival in Gastric Cancer based on a Robust 13-Gene Signature

Gastric cancer represents a major public health problem. Owing to the great heterogeneity of GC, conventional clinical characteristics are limited in the accurate prediction of individual outcomes and survival. This study aimed to establish a robust gene signature to predict the prognosis of GC based on multiple datasets. Initially, we downloaded raw data from four independent datasets of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO), and performed univariate Cox proportional hazards regression analysis to identify prognostic genes associated with overall survival (OS) from each dataset. Thirteen common genes from four datasets were screened as candidate prognostic signatures. Then, a risk score model was developed based on this 13‑gene signature and validated by four independent datasets and the entire cohort. Patients with a high-risk score had poorer OS and recurrence-free survival (RFS). Multivariate regression and stratified analysis revealed that the 13-gene signature was not only an independent predictive factor but also associated with recurrence when adjusting for other clinical factors. Furthermore, in the high-risk group, gene set enrichment analysis (GSEA) showed that the mTOR signaling pathway and MAPK signaling pathway were significantly enriched. The present study provided a robust and reliable gene signature for prognostic prediction of both OS and RFS of patients with GC, which may be useful for delivering individualized management of patients.

A Nomogram Combining a Four-Gene Biomarker and Clinical Factors for Predicting Survival of Melanoma

Background

Currently there is no effective prognostic indicator for melanoma, the deadliest skin cancer. Thus, we aimed to develop and validate a nomogram predictive model for predicting survival of melanoma.

Methods

Four hundred forty-nine melanoma cases with RNA sequencing (RNA-seq) data from TCGA were randomly divided into the training set I (n = 224) and validation set I (n = 225), 210 melanoma cases with RNA-seq data from Lund cohort of Lund University (available in GSE65904) were used as an external test set. The prognostic gene biomarker was developed and validated based on the above three sets. The developed gene biomarker combined with clinical characteristics was used as variables to develop and validate a nomogram predictive model based on 379 patients with complete clinical data from TCGA (Among 470 cases, 91 cases with missing clinical data were excluded from the study), which were randomly divided into the training set II (n = 189) and validation set II (n = 190). Area under the curve (AUC), concordance index (C-index), calibration curve, and Kaplan-Meier estimate were used to assess predictive performance of the nomogram model.

Results

Four genes, i.e., CLEC7A, CLEC10A, HAPLN3, and HCP5 comprise an immune-related prognostic biomarker. The predictive performance of the biomarker was validated using tROC and log-rank test in the training set I (n = 224, 5-year AUC of 0.683), validation set I (n = 225, 5-year AUC of 0.644), and test set I (n = 210, 5-year AUC of 0.645). The biomarker was also significantly associated with improved survival in the training set (P < 0.01), validation set (P < 0.05), and test set (P < 0.001), respectively. In addition, a nomogram combing the four-gene biomarker and six clinical factors for predicting survival in melanoma was developed in the training set II (n = 189), and validated in the validation set II (n = 190), with a concordance index of 0.736 ± 0.041 and an AUC of 0.832 ± 0.071.

Conclusion

We developed and validated a nomogram predictive model combining a four-gene biomarker and six clinical factors for melanoma patients, which could facilitate risk stratification and treatment planning.

Development of a macrophages-related 4-gene signature and nomogram for the overall survival prediction of hepatocellular carcinoma based on WGCNA and LASSO algorithm

BACKGROUND

Immune system instability and poor prognosis are the two major clinical performance of hepatocellular carcinoma (HCC). Abnormal expression of MiR-424-5p has been reported to accelerate the progression of liver cancer, but it mediated immune cell infiltration imbalance is still unknown. We aim to mine the immune-related genes (IRGs) targeted by miR-424-5p and construct a multi-gene signature to improve the prognostic prediction of HCC.

METHODS

The HCC-related data of the cancer genome atlas (TCGA) database and the GSE14520 dataset of the gene expression omnibus (GEO) database were downloaded as the discovery dataset and the validation dataset, respectively. Weighted gene co-expression network analysis (WGCNA), the deconvolution algorithm of CIBERSORT and LASSO algorithm participated in the identification of IRGs and the development of prognostic signature and nomogram.

RESULTS

Our study found that the abundance of macrophages M0, M1 and M2 are all drastically changed during the cancerous process. A total of 920 macrophages infiltration-related LRGs were identified and a novel 4-gene signature (CDCA8, CBX2, UCK2 and SOCS2) with superior prognostic independence was established. The prognostic signature based risk score has superior capability to identify high-risk patients and predict overall survival (p < 0.001; AUC = 0.798 for 1 year; AUC = 0.748 for 3 years; AUC = 0.721 for 5 years). And it (C-index = 0.726) has a better prognostic potential than the TNM stage (C-index = 0.619), which is widely adopted in clinical practice. Additionally, the nomogram formed by combining the risk score and TNM stage further improved the accuracy of survival prediction (C-index = 0.733).

CONCLUSION

In summary, the immune landscape with abnormal infiltration of macrophages may be one of the prelude to the cancerous process. The novel macrophages-related 4-gene signature is expected to become a potential prognostic marker in liver cancer.

Molecular characterization of breast cancer: a potential novel immune-related lncRNAs signature

Background

Accumulating evidence has demonstrated that immune-related lncRNAs (IRLs) are commonly aberrantly expressed in breast cancer (BC). Thus, we aimed to establish an IRL-based tool to improve prognosis prediction in BC patients.

Methods

We obtained IRL expression profiles in large BC cohorts (N = 911) from The Cancer Genome Atlas (TCGA) database. Then, in light of the correlation between each IRL and recurrence-free survival (RFS), we screened prognostic IRL signatures to construct a novel RFS nomogram via a Cox regression model. Subsequently, the performance of the IRL-based model was evaluated through discrimination, calibration ability, risk stratification ability and decision curve analysis (DCA).

Results

A total of 52 IRLs were obtained from TCGA. Based on multivariate Cox regression analyses, four IRLs (A1BG-AS1, AC004477.3, AC004585.1 and AC004854.2) and two risk parameters (tumor subtype and TNM stage) were utilized as independent indicators to develop a novel prognostic model. In terms of predictive accuracy, the IRL-based model was distinctly superior to the TNM staging system (AUC: 0.728 VS 0.673, P = 0.010). DCA indicated that our nomogram had favorable clinical practicability. In addition, risk stratification analysis showed that the IRL-based tool efficiently divided BC patients into high- and low-risk groups (P < 0.001).

Conclusions

A novel IRL-based model was constructed to predict the risk of 5-year RFS in BC. Our model can improve the predictive power of the TNM staging system and identify high-risk patients with tumor recurrence to implement more appropriate treatment strategies.

The calcium pump PMCA4 prevents epithelial-mesenchymal transition by inhibiting NFATc1-ZEB1 pathway in gastric cancer

Epithelial-mesenchymal transition (EMT) is considered as the key mechanism involved in cancer metastasis. Several studies showed that various cell membrane calcium channels play different roles in cancer metastasis. In the present study, the potential role of ATPase plasma membrane Ca²⁺ transporting 4 (PMCA4) in regulating EMT in gastric cancer (GC) was investigated. GC patients who underwent radical surgery were enrolled in this study. In vitro human GC cell lines MKN45 and NCI-N87 were used, and MKN45 cells were injected in nude mice to evaluate tumor development. Our results showed that low PMCA4 expression was associated with advanced TNM stage and poor prognosis in GC patients. Knockdown of PMCA4 suppressed E-cadherin, grainyhead like 2 (GRHL2) and ovo-like 1 (OVOL1) expression, up-regulated vimentin expression, increased migration and invasion ability, and promoted the resistance to cytotoxic drug. Furthermore, GC cells displayed an elongated fibroblastoid morphology when PMCA4 was knockdown. PMCA4 overexpression resulted in an up-regulated E-cadherin expression and decreased migration and invasion ability. In vivo metastasis assay showed that PMCA4 overexpression resulted in a decreased incidence of lung metastasis. PMCA4 inhibition increased ZEB1 expression and nuclear accumulation of nuclear factor of activated T-cell isoform c1 (NFATc1). EMT induced by PMCA4 inhibition could be prevented by the knockdown of NFATc1 or ZEB1. In addition, cyclosporine A prevented EMT induced by PMCA4 inhibition by suppressing the NFATc1-ZEB1 pathway. Our data identified a novel mechanism in the regulation of EMT in GC, and provided a novel target in the treatment of EMT subtype in GC.

A transcriptomic study for identifying cardia- and non-cardia-specific gastric cancer prognostic factors using genetic algorithm-based methods

Gastric cancer (GC) is a heterogeneous tumour with numerous differences of epidemiologic and clinicopathologic features between cardia cancer and non-cardia cancer. However, few studies were performed to construct site-specific GC prognostic models. In this study, we identified site-specific GC transcriptomic prognostic biomarkers using genetic algorithm (GA)-based support vector machine (GA-SVM) and GA-based Cox regression method (GA-Cox) in the Cancer Genome Atlas (TCGA) database. The area under time-dependent receive operating characteristic (ROC) curve (AUC) regarding 5-year survival and concordance index (C-index) was used to evaluate the predictive ability of Cox regression models. Finally, we identified 10 and 13 prognostic biomarkers for cardia cancer and non-cardia cancer, respectively. Compared to traditional models, the addition of these site-specific biomarkers could notably improve the model preference (cardia: AUCtraditional vs AUCcombined  = 0.720 vs 0.899, P = 8.75E-08; non-cardia: AUCtraditional vs AUCcombined  = 0.798 vs 0.994, P = 7.11E-16). The combined nomograms exhibited superior performance in cardia and non-cardia GC survival prediction (C-indexcardia  = 0.816; C-indexnoncardia  = 0.812). We also constructed a user-friendly GC site-specific molecular system (GC-SMS, https://njmu-zhanglab.shinyapps.io/gc_sms/), which is freely available for users. In conclusion, we developed site-specific GC prognostic models for predicting cardia cancer and non-cardia cancer survival, providing more support for the individualized therapy of 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.

Prognostic and Predictive Value of a Long Non-coding RNA Signature in Glioma: A lncRNA Expression Analysis

The current histologically based grading system for glioma does not accurately predict which patients will have better outcomes or benefit from adjuvant chemotherapy. We proposed that combining the expression profiles of multiple long non-coding RNAs (lncRNAs) into a single model could improve prediction accuracy. We included 1,094 glioma patients from three different datasets. Using the least absolute shrinkage and selection operator (LASSO) Cox regression model, we built a multiple-lncRNA-based classifier on the basis of a training set. The predictive and prognostic accuracy of the classifier was validated using an internal test set and two external independent sets. Using this classifier, we classified patients in the training set into high- or low-risk groups with significantly different overall survival (OS, HR = 8.42, 95% CI = 4.99–14.2, p < 0.0001). The prognostic power of the classifier was then assessed in the other sets. The classifier was an independent prognostic factor and had better prognostic value than clinicopathological risk factors. The patients in the high-risk group were found to have a favorable response to adjuvant chemotherapy (HR = 0.4, 95% CI = 0.25–0.64, p < 0.0001). We built a nomogram that integrated the 10-lncRNA-based classifier and four clinicopathological risk factors to predict 3 and 5 year OS. Gene set variation analysis (GSVA) showed that pathways related to tumorigenesis, undifferentiated cancer, and epithelial–mesenchymal transition were enriched in the high-risk groups. Our classifier built on 10-lncRNAs is a reliable prognostic and predictive tool for OS in glioma patients and could predict which patients would benefit from adjuvant chemotherapy.

A 16-mRNA signature optimizes recurrence-free survival prediction of Stages II and III gastric cancer

High-throughput messenger RNA (mRNA) analysis has become a powerful tool for exploring tumor recurrence or metastasis mechanisms. Here, we constructed a signature to predict the recurrence risk of Stages II and III gastric cancer (GC) patients. A least absolute shrinkage and selection operator method Cox regression model was utilized to construct the signature. Using this method, a 16-mRNA signature was identified to be associated with the relapse-free survival of Stages II and III GCs in training dataset GSE62254 (n = 194). Then this signature was validated in an independent Gene Expression Omnibus cohort GSE26253 (n = 297) and a dataset of The Cancer Genome Atlas (TCGA; n = 235). This classifier could successfully screen out the high-risk Stages II and III GCs in the training cohort (hazard ratio [HR] = 40.91; 95% confidence interval [CI] = 5.58-299.7; p < .0001). Analysis in two independent validation cohorts yielded consistent results (GSE26253: HR = 1.69, 95% CI = 1.17-2.43,; p = .0045; TCGA: HR = 2.01, 95% CI = 1.13-3.56, p = .0146). Cox regression analyses revealed that the risk score derived from this signature was an independent risk factor in Stages II and III GCs. Besides, a nomogram was constructed to serve clinical practice. Through gene set variation analysis, we found several gene sets associated with chemotherapeutic drug resistance and tumor metastasis significantly enriched in high-risk patients. In summary, this 16-mRNA signature can be used as a powerful tool for prognostic evaluation and help clinicians identify high-risk patients.

The discovery of a novel eight-mRNA-lncRNA signature predicting survival of hepatocellular carcinoma patients

Increasing evidence indicates that the expressions of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs) undergo a frequent and aberrant change in carcinogenesis and cancer development. But some research was carried out on mRNA-lncRNA signatures for prediction of hepatocellular carcinoma (HCC) prognosis. We aimed to establish an mRNA-lncRNA signature to improve the ability to predict HCC patients' survival. The subjects from the cancer genome atlas (TCGA) data set were randomly divided into two parts: training data set (n = 246) and testing data set (n = 124). Using computational methods, we selected eight gene signatures (five mRNAs and three lncRNAs) to generate the risk score model, which were significantly correlated with overall survival of patients with HCC in both training and testing data set. The signature had the ability to classify the patients in training data set into a high-risk group and low-risk group with significantly different overall survival (hazard ratio = 4.157, 95% confidence interval = 2.648-6.526, P < 0.001). The prognostic value was further validated in testing data set and the entire data set. Further analysis revealed that this signature was independent of tumor stage. In addition, Gene Set Enrichment Analysis suggested that high risk score group was associated with cell proliferation and division related pathways. Finally, we developed a well-performed nomogram integrating the prognostic signature and other clinical information to predict 3- and 5-year overall survival. In conclusion, the prognostic mRNAs and lncRNAs identified in our study indicate their potential role in HCC biogenesis. The risk score model based on the mRNA-lncRNA may be an efficient classification tool to evaluate the prognosis of patients' with HCC.