Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal Cancer

Zhou, Leqi; Yu, Yue; Wen, Rongbo; Zheng, Kuo; Jiang, Siyuan; Zhu, Xiaoming; Sui, Jinke; Gong, Haifeng; Lou, Zheng; Hao, Liqiang; Yu, Guanyu; Zhang, Wei

doi:10.3389/fonc.2022.863094

Cited by 9 publications

(9 citation statements)

References 45 publications

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“…For example, Ma J et al identified 5 prognosis-related markers in triple-negative breast cancer and provided potential therapeutic targets through multiomics and bioinformatics (22). Zhou L et al developed an 8-gene signature to predict overall survival in colon cancer patients (23). However, similar research in gliomas was significantly less common than in other tumours, implying that more comprehensive and in-depth studies of gliomas are urgently needed.…”

Section: Discussionmentioning

confidence: 99%

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Identification and validation of a 17-gene signature to improve the survival prediction of gliomas

Tong

Xia²,

Xu³

et al. 2022

Front. Immunol.

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Gliomas are one of the most frequent types of nervous system tumours and have significant morbidity and mortality rates. As a result, it is critical to fully comprehend the molecular mechanism of glioma to predict prognosis and target gene therapy. The goal of this research was to discover the hub genes of glioma and investigate their prognostic and diagnostic usefulness. In this study, we collected mRNA expression profiles and clinical information from glioma patients in the TCGA, GTEx, GSE68848, and GSE4920 databases. WGCNA and differential expression analysis identified 170 DEGs in the collected datasets. GO and KEGG pathway analyses revealed that DEGs were mainly enriched in gliogenesis and extracellular matrix. LASSO was performed to construct prognostic signatures in the TCGA cohort, and 17 genes were used to build risk models and were validated in the CGGA database. The ROC curve confirmed the accuracy of the prognostic signature. Univariate and multivariate Cox regression analyses showed that all independent risk factors for glioma except gender. Next, we performed ssGSEA to demonstrate a high correlation between risk score and immunity. Subsequently, 7 hub genes were identified by the PPI network and found to have great drug targeting potential. Finally, RPL39, as one of the hub genes, was found to be closely related to the prognosis of glioma patients. Knockdown of RPL39 in vitro significantly inhibited the proliferation and migration of glioma cells, whereas overexpression of RPL39 had the opposite effect. And we found that knockdown of RPL39 inhibited the polarization and infiltration of M2 phenotype macrophages. In conclusion, our new prognosis-related model provides more potential therapeutic strategies for glioma patients.

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Section: Discussionmentioning

confidence: 99%

“…Zhou L et al. developed an 8-gene signature to predict overall survival in colon cancer patients ( 23 ). However, similar research in gliomas was significantly less common than in other tumours, implying that more comprehensive and in-depth studies of gliomas are urgently needed.…”

Section: Discussionmentioning

confidence: 99%

Identification and validation of a 17-gene signature to improve the survival prediction of gliomas

Tong

Xia²,

Xu³

et al. 2022

Front. Immunol.

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“…Univariate Cox regression analysis was performed to screen the prognosis-related nAChRs among the smoking population in the HNSCC cohort. To avoid overfitting, least absolute shrinkage and selection operator (LASSO) analysis was performed, and a novel prognostic signature was constructed using the following formula: risk score = nAChR (A) expression * coef (A) + nAChR (B) expression * coef (B) + nAChR (i) expression * coef (i) [ 15 ]. Based on the expression of nAChRs, the risk of each of the patients was scored and they were divided into low- and high-risk groups according to the median value.…”

Section: Methodsmentioning

confidence: 99%

Smoking-mediated nicotinic acetylcholine receptors (nAChRs) for predicting outcomes for head and neck squamous cell carcinomas

Shen

Huang

et al. 2022

BMC Cancer

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Background As a human tumor disease, head and neck squamous cell carcinoma (HNSCC) is associated with a high mortality rate worldwide. Nicotinic acetylcholine receptors (nAChRs) are transmembrane receptor proteins and exert their biological effects following activation by nicotine. We aimed to construct a prognostic signature based on the expression of nAChRs among smokers with HNSCC. Methods The transcriptome profile of nAChRs was obtained from The Cancer Genome Atlas (TCGA). Following the integration of survival information, univariate Cox regression and least absolute shrinkage and selection operator (LASSO) analyses were performed to screen the prognosis-related nAChRs and construct a prognostic signature. Kaplan–Meier (KM), receiver operating characteristic (ROC), principal component analysis (PCA), and independent prognostic analysis were utilized to verify the predictive power of the nAChR-associated prognostic signature. The expression of α5 nAChR in clinical samples was verified by quantitative reverse transcriptase PCR. Results Subunits α2, α5, α9, and β4 were related to the prognosis. The prognostic signature comprised the expression of subunits α5, α9, and β4. The nAChR-associated signature showed high sensitivity and specificity for prognostic prediction and was an independent factor for overall survival. Based on the clinical variables and expression of nAChRs, a nomogram was constructed for predicting the outcomes of HNSCC patients who were smokers in the clinical settings. In clinical specimens, α5 nAChR showed high expression in HNSCC tissues, especially among smokers. Conclusions The nAChR-associated signature constructed in this study may provide a better system for the classification of HNSCC patients and facilitate personalized treatment according to their smoking habits.

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“…Univariate Cox regression analysis was used to evaluate the association between genes and NPC prognosis. DEGs that were significantly associated with NPC (P<0.05) were considered as candidate genes ( 26 ). Subsequently, the glmnet package in R was used to filter and remove genes that might result in overfitting the model by the Lasso algorithm.…”

Section: Methodsmentioning

confidence: 99%

Construction of diagnostic and prognostic models based on gene signatures of nasopharyngeal carcinoma by machine learning methods

Wang¹,

He²,

Duan³

et al. 2023

Transl Cancer Res

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Background: Diagnostic models based on gene signatures of nasopharyngeal carcinoma (NPC) were constructed by random forest (RF) and artificial neural network (ANN) algorithms. Least absolute shrinkage and selection operator (Lasso)-Cox regression was used to select and build prognostic models based on gene signatures. This study contributes to the early diagnosis and treatment, prognosis, and molecular mechanisms associated with NPC.Methods: Two gene expression datasets were downloaded from the Gene Expression Omnibus (GEO) database, and differentially expressed genes (DEGs) associated with NPC were identified by gene expression differential analysis. Subsequently, significant DEGs were identified by a RF algorithm. ANN were used to construct a diagnostic model for NPC. The performance of the diagnostic model was evaluated by area under the curve (AUC) values using a validation set. Lasso-Cox regression examined gene signatures associated with prognosis. Overall survival (OS) and disease-free survival (DFS) prediction models were constructed and validated from The Cancer Genome Atlas (TCGA) database and the International Cancer Genome Consortium (ICGC) database.Results: A total of 582 DEGs associated with NPC were identified, and 14 significant genes were identified by the RF algorithm. A diagnostic model for NPC was successfully constructed using ANN, and the validity of the model was confirmed on the training set AUC =0.947 [95% confidence interval (CI): 0.911-0.969] and the validation set AUC =0.864 (95% CI: 0.828-0.901). The 24-gene signatures associated with prognosis were identified by Lasso-Cox regression, and prediction models for OS and DFS of NPC were constructed on the training set. Finally, the ability of the model was validated on the validation set.Conclusions: Several potential gene signatures associated with NPC were identified, and a highperformance predictive model for early diagnosis of NPC and a prognostic prediction model with robust performance were successfully developed. The results of this study provide valuable references for early diagnosis, screening, treatment and molecular mechanism research of NPC in the future.

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Development and Validation of an 8-Gene Signature to Improve Survival Prediction of Colorectal Cancer

Cited by 9 publications

References 45 publications

Identification and validation of a 17-gene signature to improve the survival prediction of gliomas

Identification and validation of a 17-gene signature to improve the survival prediction of gliomas

Smoking-mediated nicotinic acetylcholine receptors (nAChRs) for predicting outcomes for head and neck squamous cell carcinomas

Construction of diagnostic and prognostic models based on gene signatures of nasopharyngeal carcinoma by machine learning methods

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