A prognostic nomogram based on LASSO Cox regression in patients with alpha-fetoprotein-negative hepatocellular carcinoma following non-surgical therapy

Zhou, Dongdong; Liu, Xiaoli; Wang, Xinhui; Yan, Fengna; Wang, Peng; Yan, Hu; Jiang, Yuyong; Yang, Zhiyun

doi:10.1186/s12885-021-07916-3

Cited by 15 publications

(14 citation statements)

References 55 publications

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“…To identify the core m6A regulators associated with OA progression and establish an effective OA prediction model, the GSE89408 dataset was subjected to LASSO-Cox regression analysis, a method for refined predictive modelling and key variable extraction [ 52 ]. When the lambda was 0.0230, six regulators (FTO, YTHDC1, METTL5, IGF2BP3, ZC3H13, and HNRNPC) were screened for modelling (Risk Score = 0.582*FTO + 0.093*HNRNPC + 0.240*IGF2BP3 + 0.095*METTL5−0.988*YTHDC1−0.145*ZC3H13, Fig.…”

Section: Resultsmentioning

confidence: 99%

Expression pattern analysis of m6A regulators reveals IGF2BP3 as a key modulator in osteoarthritis synovial macrophages

Pan

Zhang

et al. 2023

J Transl Med

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Background Disruption of N6 methyl adenosine (m6A) modulation hampers gene expression and cellular functions, leading to various illnesses. However, the role of m6A modification in osteoarthritis (OA) synovitis remains unclear. This study aimed to explore the expression patterns of m6A regulators in OA synovial cell clusters and identify key m6A regulators that mediate synovial macrophage phenotypes. Methods The expression patterns of m6A regulators in the OA synovium were illustrated by analyzing bulk RNA-seq data. Next, we built an OA LASSO-Cox regression prediction model to identify the core m6A regulators. Potential target genes of these m6A regulators were identified by analyzing data from the RM2target database. A molecular functional network based on core m6A regulators and their target genes was constructed using the STRING database. Single-cell RNA-seq data were collected to verify the effects of m6A regulators on synovial cell clusters. Conjoint analyses of bulk and single-cell RNA-seq data were performed to validate the correlation between m6A regulators, synovial clusters, and disease conditions. After IGF2BP3 was screened as a potential modulator in OA macrophages, the IGF2BP3 expression level was tested in OA synovium and macrophages, and its functions were further tested by overexpression and knockdown in vitro. Results OA synovium showed aberrant expression patterns of m6A regulators. Based on these regulators, we constructed a well-fitting OA prediction model comprising six factors (FTO, YTHDC1, METTL5, IGF2BP3, ZC3H13, and HNRNPC). The functional network indicated that these factors were closely associated with OA synovial phenotypic alterations. Among these regulators, the m6A reader IGF2BP3 was identified as a potential macrophage mediator. Finally, IGF2BP3 upregulation was verified in the OA synovium, which promoted macrophage M1 polarization and inflammation. Conclusions Our findings revealed the functions of m6A regulators in OA synovium and highlighted the association between IGF2BP3 and enhanced M1 polarization and inflammation in OA macrophages, providing novel molecular targets for OA diagnosis and treatment.

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Section: Resultsmentioning

confidence: 99%

Expression pattern analysis of m6A regulators reveals IGF2BP3 as a key modulator in osteoarthritis synovial macrophages

Pan

Zhang

et al. 2023

J Transl Med

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“…The Wilcoxon test was used to compare the gene expression levels between HCC patients and healthy individuals, as well as the composition of immune cells and ICD risk scores between low-and high-risk groups. LASSO-Cox regression was used for candidate gene selection (24). The Kaplan-Meier method and log-rank test were used to compare the survival rates between the low-and high-risk groups (25).…”

Section: Discussionmentioning

confidence: 99%

An immunogenic cell death-associated gene signature predicts prognosis in hepatocellular carcinoma

Liu

Chen

Peng

2023

Preprint

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Background: Liver cancer is the third leading cause of cancer-related mortality in the world, and hepatocellular carcinoma (HCC) accounts for approximately 90% of the incidence of all liver cancers. Methods: The TCGA-LIHC datasets were obtained from TCGA database, LIRI-JP datasets were obtained from ICGC database and immunogenic cell death (ICD) genes were obtained from previous literature. WGCNA analysis identifies ICD-associated genes. Functional analysis was used to investigate the biological characteristics of ICD-associated genes. Univariate cox analysis and least absolute shrinkage and selection operator (LASSO) cox regression analysis was used to select prognostic ICD-associated genes and constructed a prognostic risk score. Prognostic independence of ICD risk scores was determined by univariate and multivariate cox regression analyses. Then constructed a nomogram and the diagnostic value was assessed using the decision curve analysis. Immune infiltration analysis and drug sensitivity analysis were used to investigate immune cell enrichment and drug response in HCC patients who were classified as low- or high-risk based on their risk score. Results: Most of ICD genes were differentially expressed in normal and HCC patients, and some ICD genes were differentially expressed in different clinical groups. A total of 185 ICD-associated genes were identified by WGCNA. A nine-gene-based (CSN1S1, FGF9, PNLIPRP2, ISM2, FKBP6, BTNL9, CLEC3B, RAMP3, and SRL) prognostic risk score was developed. Based on the risk score, training cohort were divided into low- and high-risk groups, patients in the low-risk group had a better prognosis, and external independent data verified the reliability of the model. Univariate and multivariate Cox regression analysis showed independent prognostic factors for OS of ICD risk score. Diagnostic nomogram was constructed to predict prognosis. Immune infiltration analysis showed that some innate and adaptive immune cells were significantly different between low- and high-risk groups, Conclusion: We developed and validated a novel prognostic predictive classification system for HCC based on nine ICD-associated genes. This stratification had significant clinical outcomes for estimating prognosis, as well as the immunotherapy of HCC patients.

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“…The nomogram is a clinical tool that allows clinicians to determine the prognosis of patients by adding the score of each risk variable in the model to obtain the total score and the corresponding survival rate ( Romano et al, 2008 ). In recent years, the nomogram has been widely used as one of the practical tools in the assessment of cancer prognosis ( Ohori Tatsuo Gondo And Riu Hamada et al, 2009 ; Zhou et al, 2021a ; Wang et al, 2021b ; Wu et al, 2021 ). Calibration curves are often used to evaluate the accuracy of a nomogram.…”

Section: Discussionmentioning

confidence: 99%

The Construction of a Prognostic Model Based on a Peptidyl Prolyl Cis–Trans Isomerase Gene Signature in Hepatocellular Carcinoma

Shi

Zhong

et al. 2021

Front. Genet.

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Objective: The aim of the present study was to construct a prognostic model based on the peptidyl prolyl cis–trans isomerase gene signature and explore the prognostic value of this model in patients with hepatocellular carcinoma.Methods: The transcriptome and clinical data of hepatocellular carcinoma patients were downloaded from The Cancer Genome Atlas and the International Cancer Genome Consortium database as the training set and validation set, respectively. Peptidyl prolyl cis–trans isomerase gene sets were obtained from the Molecular Signatures Database. The differential expression of peptidyl prolyl cis–trans isomerase genes was analyzed by R software. A prognostic model based on the peptidyl prolyl cis–trans isomerase signature was established by Cox, Lasso, and stepwise regression methods. Kaplan–Meier survival analysis was used to evaluate the prognostic value of the model and validate it with an independent external data. Finally, nomogram and calibration curves were developed in combination with clinical staging and risk score.Results: Differential gene expression analysis of hepatocellular carcinoma and adjacent tissues showed that there were 16 upregulated genes. A prognostic model of hepatocellular carcinoma was constructed based on three gene signatures by Cox, Lasso, and stepwise regression analysis. The Kaplan–Meier curve showed that hepatocellular carcinoma patients in high-risk score group had a worse prognosis (p < 0.05). The receiver operating characteristic curve revealed that the area under curve values of predicting the survival rate at 1, 2, 3, 4, and 5 years were 0.725, 0.680, 0.644, 0.630, and 0.639, respectively. In addition, the evaluation results of the model by the validation set were basically consistent with those of the training set. A nomogram incorporating clinical stage and risk score was established, and the calibration curve matched well with the diagonal.Conclusion: A prognostic model based on 3 peptidyl prolyl cis–trans isomerase gene signatures is expected to provide reference for prognostic risk stratification in patients with hepatocellular carcinoma.

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A prognostic nomogram based on LASSO Cox regression in patients with alpha-fetoprotein-negative hepatocellular carcinoma following non-surgical therapy

Cited by 15 publications

References 55 publications

Expression pattern analysis of m6A regulators reveals IGF2BP3 as a key modulator in osteoarthritis synovial macrophages

Expression pattern analysis of m6A regulators reveals IGF2BP3 as a key modulator in osteoarthritis synovial macrophages

An immunogenic cell death-associated gene signature predicts prognosis in hepatocellular carcinoma

The Construction of a Prognostic Model Based on a Peptidyl Prolyl Cis–Trans Isomerase Gene Signature in Hepatocellular Carcinoma

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