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

Peng, Ke; Chen, Erbao; Li, Wei; Cheng, Xi; Yu, Yiyi; Cui, Yuehong; Li, Qian; Wang, Yan; Xu, Xiaojing; Tang, Cheng; Gan, Lu; Yu, Shan; Liu, Tianshu

doi:10.1002/jcp.29511

Cited by 11 publications

(10 citation statements)

References 41 publications

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“…[49] In our study, we presented the batch effect of GSE84437 and the normalized data after overcoming this disadvantage, making our prognostic signature more robust and reliable. Although the 1-,3-,5-year AUC of our 6-URGs prognostic signature are lower than that of former studies [42][43][44][45][46][47][48] (most AUC > 0.7), this doesn't imply a lower prediction performance of it as a big sample size (406 and 431 in our case) can strongly affect the AUC of a model and AUC can even close to 0.5 when sample size is larger than 500. [50] It is necessary to search the underlying mechanisms of the 6 UPR genes identi ed in our study.…”

Section: Discussioncontrasting

confidence: 81%

“…There are several studies having established multi-molecule biomarkers for GC prognosis including mRNAs, non-coding RNAs, DNA methylation, and so on. [42][43][44][45][46][47][48] All these models demonstrated good prediction effect for OS or RFS (Recurrence free survival) of GC. But seldom of them provided a detailed description of normalization process with GEO array express data.…”

Section: Discussionmentioning

confidence: 99%

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A Novel Unfolded Protein Response (UPR) Related 6 Genes Signature for Prognosis of Human Gastric Cancer

Jia

Huang

Song

et al. 2020

Preprint

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Background: Unfolded protein response (UPR) pathway has attracted increasing attention in the last few years for its close relation to cancer process like proliferation and drug resistance. However, few studies reported its prognostic effect in gastric cancer (GC). The purpose of this study is to identify the UPR expression in gastric cancer and construct a prognostic gene signature for clinical practice. Method: TCGA-STAD (gastric cancer cohort) was set as the training group and GSE84437 for validation. GSVA and GSEA was performed to identify the high expressed pathway in patients with gastric tumor. Following that, cox proportional hazard regression was adopted to screen genes correlated with survival in UPR path. Then, risk score for each patients was computed (FKBP14 value * (0.267719774) + TUBB2A value * (0.193233442) + SLC1A4 value * (-0.246893982) + SEC11A value * (0.484354542) + IFIT1 value * (0.152379968) + IGFBP1 value * (0.127045661)) and survival analysis was conducted between patients in high and low risk group according to their median risk score. A nomogram was built accordingly to guide clinical practice.Result: The UPR path was identified as the most highly expressed hallmark in GC with β of 32.98. A 6 genes prognostic signature was constructed according to the Cox regression and survival analysis obtained significant result where patients in low risk group showed longer OS (overall survival). The signature demonstrated an independent prognostic effect cox regression with a HR of 1.8. The external GSE84437 data proved the nomogram performed well in predicting 5 years OS. Conclusion: Our study identified that UPR pathway was highly activated in GC and a UPR related 6 genes prognostic signature demonstrated good performance. The 6 UPR related genes may serve as reliable prognostic biomarkers and potential therapeutic targets for GC treatment.

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Section: Discussioncontrasting

confidence: 81%

Section: Discussionmentioning

confidence: 99%

A Novel Unfolded Protein Response (UPR) Related 6 Genes Signature for Prognosis of Human Gastric Cancer

Jia

Huang

Song

et al. 2020

Preprint

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“…Increasing evidence has demonstrated that these molecules can not only act as oncogenes or tumor suppressor genes but also participate in the occurrence and development of tumors through a complex mutual regulatory network ( Mirgayazova et al, 2019 ). Besides, their expressions are tumor-tissue specific and have the potential as diagnostic or prognostic markers ( Sim et al, 2018 ; Hou et al, 2020 ; Peng et al, 2020 ). Therefore, it is urgently needed to investigate the integration of multiple RNA expression profiles to identify molecular signatures.…”

Section: Introductionmentioning

confidence: 99%

A Combined RNA Signature Predicts Recurrence Risk of Stage I-IIIA Lung Squamous Cell Carcinoma

Sun

et al. 2021

Front. Genet.

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ObjectiveRecurrence remains the main cause of the poor prognosis in stage I-IIIA lung squamous cell carcinoma (LUSC) after surgical resection. In the present study, we aimed to identify the long non-coding RNAs (lncRNAs), microRNAs (miRNAs), and messenger RNAs (mRNAs) related to the recurrence of stage I-IIIA LUSC. Moreover, we constructed a risk assessment model to predict the recurrence of LUSC patients.MethodsRNA sequencing data (including miRNAs, lncRNAs, and mRNAs) and relevant clinical information were obtained from The Cancer Genome Atlas (TCGA) database. The differentially expressed lncRNAs, miRNAs, and mRNAs were identified using the “DESeq2” package of the R language. Univariate Cox proportional hazards regression analysis and Kaplan-Meier curve were used to identify recurrence-related genes. Stepwise multivariate Cox regression analysis was carried out to establish a risk model for predicting recurrence in the training cohort. Moreover, Kaplan-Meier curves and receiver operating characteristic (ROC) curves were adopted to examine the predictive performance of the signature in the training cohort, validation cohort, and entire cohort.ResultsBased on the TCGA database, we analyzed the differentially expressed genes (DEGs) among 27 patients with recurrent stage I-IIIA LUSC and 134 patients with non-recurrent stage I-IIIA LUSC, and identified 431 lncRNAs, 36 miRNAs, and 746 mRNAs with different expression levels. Out of these DEGs, the optimal combination of DEGs was finally determined, and a nine-joint RNA molecular signature was constructed for clinical prediction of recurrence, including LINC02683, AC244517.5, LINC02418, LINC01322, AC011468.3, hsa-mir-6825, AC020637.1, AC027117.2, and SERPINB12. The ROC curve proved that the model had good predictive performance in predicting recurrence. The area under the curve (AUC) of the prognostic model for recurrence-free survival (RFS) was 0.989 at 3 years and 0.958 at 5 years (in the training set). The combined RNA signature also revealed good predictive performance in predicting the recurrence in the validation cohort and entire cohort.ConclusionsIn the present study, we constructed a nine-joint RNA molecular signature for recurrence prediction of stage I-IIIA LUSC. Collectively, our findings provided new and valuable clinical evidence for predicting the recurrence and targeted treatment of stage I-IIIA LUSC.

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“…At present, the American Joint Committee on Cancer (AJCC) stage is still the most basic prognostic prediction tool for gastric cancer, and a high stage indicates a poor prognosis. However, due to the high heterogeneity of gastric cancer, patients with the same tumor-node-metastasis (TNM) stage may have different prognoses ( 16 ). Similarly, differences in responses to immunotherapy among patients may also be related to their genetic and molecular backgrounds.…”

Section: Introductionmentioning

confidence: 99%

A Tumor Progression Related 7-Gene Signature Indicates Prognosis and Tumor Immune Characteristics of Gastric Cancer

et al. 2021

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BackgroundGastric cancer is a common gastrointestinal malignancy. Since it is often diagnosed in the advanced stage, its mortality rate is high. Traditional therapies (such as continuous chemotherapy) are not satisfactory for advanced gastric cancer, but immunotherapy has shown great therapeutic potential. Gastric cancer has high molecular and phenotypic heterogeneity. New strategies for accurate prognostic evaluation and patient selection for immunotherapy are urgently needed.MethodsWeighted gene coexpression network analysis (WGCNA) was used to identify hub genes related to gastric cancer progression. Based on the hub genes, the samples were divided into two subtypes by consensus clustering analysis. After obtaining the differentially expressed genes between the subtypes, a gastric cancer risk model was constructed through univariate Cox regression, least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox regression analysis. The differences in prognosis, clinical features, tumor microenvironment (TME) components and immune characteristics were compared between subtypes and risk groups, and the connectivity map (CMap) database was applied to identify potential treatments for high-risk patients.ResultsWGCNA and screening revealed nine hub genes closely related to gastric cancer progression. Unsupervised clustering according to hub gene expression grouped gastric cancer patients into two subtypes related to disease progression, and these patients showed significant differences in prognoses, TME immune and stromal scores, and suppressive immune checkpoint expression. Based on the different expression patterns between the subtypes, we constructed a gastric cancer risk model and divided patients into a high-risk group and a low-risk group based on the risk score. High-risk patients had a poorer prognosis, higher TME immune/stromal scores, higher inhibitory immune checkpoint expression, and more immune characteristics suitable for immunotherapy. Multivariate Cox regression analysis including the age, stage and risk score indicated that the risk score can be used as an independent prognostic factor for gastric cancer. On the basis of the risk score, we constructed a nomogram that relatively accurately predicts gastric cancer patient prognoses and screened potential drugs for high-risk patients.ConclusionsOur results suggest that the 7-gene signature related to tumor progression could predict the clinical prognosis and tumor immune characteristics of gastric cancer.

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

Cited by 11 publications

References 41 publications

A Novel Unfolded Protein Response (UPR) Related 6 Genes Signature for Prognosis of Human Gastric Cancer

A Novel Unfolded Protein Response (UPR) Related 6 Genes Signature for Prognosis of Human Gastric Cancer

A Combined RNA Signature Predicts Recurrence Risk of Stage I-IIIA Lung Squamous Cell Carcinoma

A Tumor Progression Related 7-Gene Signature Indicates Prognosis and Tumor Immune Characteristics of Gastric Cancer

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