Feature selection for RNA cleavage efficiency at specific sites using the LASSO regression model in Arabidopsis thaliana

Ueno, Daishin; Kawabe, Harunori; Yamasaki, Satoshi; Demura, Taku; Kato, Ko

doi:10.1186/s12859-021-04291-5

Cited by 15 publications

(9 citation statements)

References 30 publications

(40 reference statements)

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“…In this study, 34 prognosis-related differentially expressed GTs were first obtained. Then, LASSO regression was applied to construct a prognostic signature, as used in previous studies( Ueno, et al, 2021 ). The final screening result identified 6 genes (POMGNT1, DPM1, B4GALT3, B4GALT2, B4GALNT1, and B3GAT3); consistent with the screening results, we confirmed their differential expression in cells and tissues.…”

Section: Discussionmentioning

confidence: 99%

Identification of a Novel Glycosyltransferase Prognostic Signature in Hepatocellular Carcinoma Based on LASSO Algorithm

Zhou

Wang

et al. 2022

Front. Genet.

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Although many prognostic models have been developed to help determine personalized prognoses and treatments, the predictive efficiency of these prognostic models in hepatocellular carcinoma (HCC), which is a highly heterogeneous malignancy, is less than ideal. Recently, aberrant glycosylation has been demonstrated to universally participate in tumour initiation and progression, suggesting that dysregulation of glycosyltransferases can serve as novel cancer biomarkers. In this study, a total of 568 RNA-sequencing datasets of HCC from the TCGA database and ICGC database were analysed and integrated via bioinformatic methods. LASSO regression analysis was applied to construct a prognostic signature. Kaplan–Meier survival, ROC curve, nomogram, and univariate and multivariate Cox regression analyses were performed to assess the predictive efficiency of the prognostic signature. GSEA and the “CIBERSORT” R package were utilized to further discover the potential biological mechanism of the prognostic signature. Meanwhile, the differential expression of the prognostic signature was verified by western blot, qRT–PCR and immunohistochemical staining derived from the HPA. Ultimately, we constructed a prognostic signature in HCC based on a combination of six glycosyltransferases, whose prognostic value was evaluated and validated successfully in the testing cohort and the validation cohort. The prognostic signature was identified as an independent unfavourable prognostic factor for OS, and a nomogram including the risk score was established and showed the good performance in predicting OS. Further analysis of the underlying mechanism revealed that the prognostic signature may be potentially associated with metabolic disorders and tumour-infiltrating immune cells.

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Section: Discussionmentioning

confidence: 99%

Identification of a Novel Glycosyltransferase Prognostic Signature in Hepatocellular Carcinoma Based on LASSO Algorithm

Zhou

Wang

et al. 2022

Front. Genet.

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“…Then, we screened 1,140 GT-related lncRNAs based on the co-expression of lncRNAs and GTs. Subsequently, seven prognostic GT-related lncRNAs (LINC02381, AC002310.1, ZEB1AS1, AC020558.2, AC105219.1, MIR210HG, and AC009237.14) were identified through LASSO and Cox regression analysis ( 30 ). The risk signature was determined based on the seven GT-related lncRNAs, which stratified COAD patients into two high- and low-risk groups.…”

Section: Discussionmentioning

confidence: 99%

“…To identify GT-related lncRNAs with prognostic value, we conducted the univariate and multivariate COX analysis. We first screened GT-related lncRNAs with potential survival impact on COAD patients based on the standard of p < 0.05 and subsequently incorporated those lncRNAs into LASSO Cox regression analysis and multivariate COX regression analysis ( 30 ). The following formula was used to calculate the risk score of each patient: risk score = (coefficient lncRNA 1 × expression of lncRNA 1 ) + (coefficient lncRNA 2 × expression of lncRNA 2 ) +…+ (coefficient lncRNA n × expression of lncRNA n ).…”

Section: Methodsmentioning

confidence: 99%

Glycosyltransferase-related long non-coding RNA signature predicts the prognosis of colon adenocarcinoma

Zhang

et al. 2022

Front. Oncol.

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PurposeColon adenocarcinoma (COAD) is the most common type of colorectal cancer (CRC) and is associated with poor prognosis. Emerging evidence has demonstrated that glycosylation by long noncoding RNAs (lncRNAs) was associated with COAD progression. To date, however, the prognostic values of glycosyltransferase (GT)-related lncRNAs in COAD are still largely unknown.MethodsWe obtained the expression matrix of mRNAs and lncRNAs in COAD from The Cancer Genome Atlas (TCGA) database. Then, the univariate Cox regression analysis was conducted to identify 33 prognostic GT-related lncRNAs. Subsequently, LASSO and multivariate Cox regression analysis were performed, and 7 of 33 GT-related lncRNAs were selected to conduct a risk model. Gene set enrichment analysis (GSEA) was used to analyze gene signaling pathway enrichment of the risk model. ImmuCellAI, an online tool for estimating the abundance of immune cells, and correlation analysis were used to explore the tumor-infiltrating immune cells in COAD. Finally, the expression levels of seven lncRNAs were detected in colorectal cancer cell lines by reverse transcription-quantitative polymerase chain reaction (RT-qPCR).ResultsA total of 1,140 GT-related lncRNAs were identified, and 7 COAD-specific GT-related lncRNAs (LINC02381, MIR210HG, AC009237.14, AC105219.1, ZEB1-AS1, AC002310.1, and AC020558.2) were selected to conduct a risk model. Patients were divided into high- and low-risk groups based on the median of risk score. The prognosis of the high-risk group was worse than that of the low-risk group, indicating the good reliability and specificity of our risk model. Additionally, a nomogram based on the risk score and clinical traits was built to help clinical decisions. GSEA showed that the risk model was significantly enriched in metabolism-related pathways. Immune infiltration analysis revealed that five types of immune cells were significantly different between groups, and two types of immune cells were negatively correlated with the risk score. Besides, we found that the expression levels of these seven lncRNAs in tumor cells were significantly higher than those in normal cells, which verified the feasibility of the risk model.ConclusionThe efficient risk model based on seven GT-related lncRNAs has prognostic potential for COAD, which may be novel biomarkers and therapeutic targets for COAD patients.

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“…In the creation of innovative clinical prediction models, the least absolute shrinkage selection operator (LASSO) regression model is typically utilized [ 25 ]. Based on the gene signature generated by LASSO, we calculated the risk score for each patient by applying the following formula:…”

Section: Methodsmentioning

confidence: 99%

Integrated single-cell and bulk RNA sequencing analysis identifies a cancer-associated fibroblast-related gene signature for predicting survival and therapy in gastric cancer

et al. 2023

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As the dominant component of the tumor microenvironment, cancer-associated fibroblasts (CAFs), play a vital role in tumor progression. An increasing number of studies have confirmed that CAFs are involved in almost every aspect of tumors including tumorigenesis, metabolism, invasion, metastasis and drug resistance, and CAFs provide an attractive therapeutic target. This study aimed to explore the feature genes of CAFs for potential therapeutic targets and reliable prediction of prognosis in patients with gastric cancer (GC). Bioinformatic analysis was utilized to identify the feature genes of CAFs in GC by performing an integrated analysis of single-cell and transcriptome RNA sequencing using R software. Based on these feature genes, a CAF-related gene signature was constructed for prognostic prediction by LASSO. Simultaneously, survival analysis and nomogram were performed to validate the prognostic predictive value of this gene signature, and qRT–PCR and immunohistochemical staining verified the expression of the feature genes of CAFs. In addition, small molecular drugs for gene therapy of CAF-related gene signatures in GC patients were identified using the connectivity map (CMAP) database. A combination of nine CAF-related genes was constructed to characterize the prognosis of GC, and the prognostic potential and differential expression of the gene signature were initially validated. Additionally, three small molecular drugs were deduced to have anticancer properties on GC progression. By integrating single-cell and bulk RNA sequencing analyses, a novel gene signature of CAFs was constructed. The results provide a positive impact on future research and clinical studies involving CAFs for GC.

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Feature selection for RNA cleavage efficiency at specific sites using the LASSO regression model in Arabidopsis thaliana

Cited by 15 publications

References 30 publications

Identification of a Novel Glycosyltransferase Prognostic Signature in Hepatocellular Carcinoma Based on LASSO Algorithm

Identification of a Novel Glycosyltransferase Prognostic Signature in Hepatocellular Carcinoma Based on LASSO Algorithm

Glycosyltransferase-related long non-coding RNA signature predicts the prognosis of colon adenocarcinoma

Integrated single-cell and bulk RNA sequencing analysis identifies a cancer-associated fibroblast-related gene signature for predicting survival and therapy in gastric cancer

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