Prognostic Value of mRNAsi/Corrected mRNAsi Calculated by the One-Class Logistic Regression Machine-Learning Algorithm in Glioblastoma Within Multiple Datasets

Zhang, Mingwei; Chen, Hong; Liang, Bo; Wang, Xuezhen; Gu, Ning; Xue, Fangqin; Yue, Qiuyuan; Zhang, Qiuyu; Hong, Jinsheng

doi:10.3389/fmolb.2021.777921

Cited by 7 publications

(3 citation statements)

References 58 publications

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“…Anti-programmed cell death protein 1 (PD-1), anti-programmed death-ligand 1 (PD-L1), and anti-cytotoxic T-lymphocyte antigen 4 (CTLA-4) immunotherapies are commonly used in the treatment of various cancers [ 28 , 29 ]. Here, we analyzed the sensitivity of FMO2 expression to immunotherapy.…”

Section: Resultsmentioning

confidence: 99%

Discovery and identification of the prognostic significance and potential mechanism of FMO2 in breast cancer

Wu,

Chu,

Shangguan

et al. 2023

Aging

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Background: Flavin containing dimethylaniline monoxygenase 2 (FMO2), is downexpressed in diverse tumors and displays vital roles in tumorigenesis. However, the prognostic value and potential mechanism of FMO2 in breast cancer remain unclear. Methods: The expression of FMO2 was analyzed and the relationship between FMO2 expression level and clinical indicators in breast cancer was analyzed. Then the prognostic value of FMO2 in breast cancer was assessed. The FMO2-correlated genes were obtained, and the highest-ranked gene was chosen. The expression, therapeutic responder analysis, and gene set enrichment analysis of the highest-ranked gene were conducted. Results: FMO2 was downregulated in breast cancer and was closely related to clinical indicators. Patients with decreased FMO2 expression showed poor overall survival, post-progression survival, relapse-free survival, and distant metastasis-free survival. FMO2 correlates with N/ER/PR subgroups in breast cancer and patients with high FMO2 levels were sensitive to anti-programmed cell death protein 1, anti-programmed death-ligand 1, and anti-cytotoxic T-lymphocyte antigen 4 immunotherapies. Mechanically, FMO2 was positively and highly correlated with secreted Frizzled-related protein 1 (SFRP1), which was downregulated in breast cancer due to hypermethylation. Moreover, SFRP1 was correlated to pathological complete response and relapse-free survival status at 5 years regardless of any chemotherapy, hormone therapy, and anti-HER2 therapy. Gene set enrichment analysis revealed enrichment of component and coagulation cascades, focal adhesion, protein export, and spliceosome. Conclusions: FMO2 was lower expressed in breast cancer than normal tissues and contributes to subtype classification and prognosis prediction with co-expressed SFRP1.

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Section: Resultsmentioning

confidence: 99%

Discovery and identification of the prognostic significance and potential mechanism of FMO2 in breast cancer

Wu,

Chu,

Shangguan

et al. 2023

Aging

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“…CSCs are more aggressive than normal cancer cells, thereby promoting tumor invasion and metastasis [5]. In recent years, a new cancer stemness index (mRNAsi) generated by deep learning method has gained significant attention in diverse cancers [6], including hepatocellular carcinoma [7,8], renal cell carcinoma [8], lung cancer [9], and glioma [10]. Furthermore, metabolic reprogramming has emerged as a novel fundamental characteristic of cancer cells in recent years [11].…”

Section: Introductionmentioning

confidence: 99%

A novel prognostic signature based on cancer stemness and metabolism-related genes for cervical squamous cell carcinoma and endocervical adenocarcinoma

Wang,

Han,

Jin

et al. 2024

Aging

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Background: CESC is the second most commonly diagnosed gynecological malignancy. Given the pivotal involvement of metabolism-related genes (MRGs) in the etiology of multiple tumors, our investigation aims to devise a prognostic risk signature rooted in cancer stemness and metabolism. Methods: The stemness index based on mRNA expression (mRNAsi) of samples from the TCGA dataset was computed using the One-class logistic regression (OCLR) algorithm. Furthermore, potential metabolism-related genes related to mRNAsi were identified through weighted gene co-expression network analysis (WGCNA). We construct a stemness-related metabolic gene signature through shrinkage estimation and univariate analysis, thereby calculating the corresponding risk scores. Moreover, we selected corresponding DEGs between groups with high-and low-risk score and conducted routine bioinformatic analyses. Furthermore, we validated the expression of four hub genes at the protein level through immunohistochemistry (IHC) in samples obtained from our patient cohort. Results: According to the findings, it was found that six genes-AKR1B10, GNA15, ALDH1B1, PLOD2, LPCAT1, and GPX8-were differentially expressed in both TCGA-CSEC and GEO datasets among 23 differentially expressed metabolism-related genes (DEMRGs). mRNAsi exhibited a notable association with the extent of key oncogene mutation. The results showed that the AUC values for forecasting survival at 1, 3, and 5 years are 0.715, 0.689, and 0.748, individually. We observed a notable association between the risk score and different immune cell populations, along with enrichment in crucial signaling pathways in CESC. Four genes differentially expressed between different risk score groups were validated by IHC to be highly expressed in the CESC samples at the protein level. Conclusion: The current investigation indicated that a 3-gene signature based on stemness-related metabolic and 4 hub genes with differential expression between high and low-risk score subgroups may serve as valuable prognostic markers and potential therapeutic targets in CESC.

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“…mRNAsi is a cancer stem cell index describing the similarity degree between tumor and stem cells, which can be considered a quantification of cancer stem cells [13]. The values of mRNAsi range from 0 to 1, and it has a close connection with the tumor dedifferentiation level and biological processes of CSCs [14,15]. mRNAsi has been verified as an indicator of survival, classification, and disease progression in cancer patients [15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Construction and Validation of a Prognostic Model Based on mRNAsi-Related Genes in Breast Cancer

Zhao

Lin

2022

Computational and Mathematical Methods in Medicine

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Background. Breast cancer is a big threat to the women across the world with substantial morbidity and mortality. The pressing matter of our study is to establish a prognostic gene model for breast cancer based on mRNAsi for predicting patient’s prognostic survival. Methods. From The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, we downloaded the expression profiles of genes in breast cancer. On the basis of one-class logistic regression (OCLR) machine learning algorithm, mRNAsi of samples was calculated. Kaplan-Meier (K-M) and Kruskal-Wallis (K-W) tests were utilized for the assessment of the connection between mRNAsi and clinicopathological variables of the samples. As for the analysis on the correlation between mRNAsi and immune infiltration, ESTIMATE combined with Spearman test was employed. The weighted gene coexpression network analysis (WGCNA) network was established by utilizing the differentially expressed genes in breast cancer, and the target module with the most significant correlation with mRNAsi was screened. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were conducted to figure out the biological functions of the target module. As for the construction of the prognostic model, univariate, least absolute shrinkage and selection operator (LASSO) and multivariate Cox regression analyses were performed on genes in the module. The single sample gene set enrichment analysis (ssGSEA) and tumor mutational burden were employed for the analysis on immune infiltration and gene mutations in the high- and low-risk groups. As for the analysis on whether this model had the prognostic value, the nomogram and calibration curves of risk scores and clinical characteristics were drawn. Results. Nine mRNAsi-related genes (CFB, MAL2, PSME2, MRPL13, HMGB3, DCTPP1, SHCBP1, SLC35A2, and EVA1B) comprised the prognostic model. According to the results of ssGSEA and gene mutation analysis, differences were shown in immune cell infiltration and gene mutation frequency between the high- and low-risk groups. Conclusion. Nine mRNAsi-related genes screened in our research can be considered as the biomarkers to predict breast cancer patients’ prognoses, and this model has a potential relationship with individual somatic gene mutations and immune regulation. This study can offer new insight into the development of diagnostic and clinical treatment strategies for breast cancer.

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Prognostic Value of mRNAsi/Corrected mRNAsi Calculated by the One-Class Logistic Regression Machine-Learning Algorithm in Glioblastoma Within Multiple Datasets

Cited by 7 publications

References 58 publications

Discovery and identification of the prognostic significance and potential mechanism of FMO2 in breast cancer

Discovery and identification of the prognostic significance and potential mechanism of FMO2 in breast cancer

A novel prognostic signature based on cancer stemness and metabolism-related genes for cervical squamous cell carcinoma and endocervical adenocarcinoma

Construction and Validation of a Prognostic Model Based on mRNAsi-Related Genes in Breast Cancer

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