Identification and Validation of Prognostic Signature of Papillary Thyroid Carcinoma Based on Six Immune‐Related lncRNAs

This research seeks to identify and verify the prognostic signature of immune-related RNA of papillary thyroid carcinoma (PTC). The Cancer Genome Atlas (TCGA) database provided the PTC samples and clinical data. Using the limma program, differentially expressed mRNAs and lncRNAs (DElncRNAs) were found. A risk score (RS) model was created by using univariate and multivariate Cox regression analyses to find lncRNAs associated with overall survival prognosis. The association between the lncRNA prognostic signature and the invasion of several immune cell subtypes was investigated using the Tumor Immune Estimation Resource (TIMER). To verify the expression of the prognostic signature, RT-PCR was utilized. From TCGA, 491 PTC tumor samples were acquired, comprising 18497 mRNAs and 2525 lncRNAs. There were 138 DElncRNAs and 494 differentially expressed mRNAs. Six DElncRNAs with independent prognoses were then selected from a total of 138 DElncRNAs. According to the TIMER database, the 6-DElncRNA PTC prognostic signature was correlated with the invasion of several immune cell subtypes. The expression patterns of four DElncRNAs matched the database results, according to RT-PCR analysis. Six lncRNAs were found to constitute a predictive characteristic for PTC in this study, and these signatures were connected to the infiltration of different immune cell subtypes. These findings provide a potential basis for the development of diagnostic biomarkers and immunotherapeutic stratification in PTC.

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Publication Details

Journal
Biotechnology and Applied Biochemistry
Published
2025-10-17
DOI
https://doi.org/10.1002/bab.70063
Citations
1
Primary Topic
Cancer-related molecular mechanisms research
Type
article
Field-Weighted Citation Impact
0.94
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article

Identification and Validation of Prognostic Signature of Papillary Thyroid Carcinoma Based on Six Immune‐Related lncRNAs

Xiang Jiao, Huamei Zhou, Xuelei Zhu, Lijing Chen et al.
1 citations
Biotechnology and Applied Biochemistry
Cancer-related molecular mechanisms research
0.94
article

Identification and Validation of Prognostic Signature of Papillary Thyroid Carcinoma Based on Six Immune‐Related lncRNAs

Xiang Jiao, Huamei Zhou, Xuelei Zhu, Lijing Chen, Yi Zhang, Yan Luo, Yi Su
article en
1 citations

Abstract

This research seeks to identify and verify the prognostic signature of immune-related RNA of papillary thyroid carcinoma (PTC). The Cancer Genome Atlas (TCGA) database provided the PTC samples and clinical data. Using the limma program, differentially expressed mRNAs and lncRNAs (DElncRNAs) were found. A risk score (RS) model was created by using univariate and multivariate Cox regression analyses to find lncRNAs associated with overall survival prognosis. The association between the lncRNA prognostic signature and the invasion of several immune cell subtypes was investigated using the Tumor Immune Estimation Resource (TIMER). To verify the expression of the prognostic signature, RT-PCR was utilized. From TCGA, 491 PTC tumor samples were acquired, comprising 18497 mRNAs and 2525 lncRNAs. There were 138 DElncRNAs and 494 differentially expressed mRNAs. Six DElncRNAs with independent prognoses were then selected from a total of 138 DElncRNAs. According to the TIMER database, the 6-DElncRNA PTC prognostic signature was correlated with the invasion of several immune cell subtypes. The expression patterns of four DElncRNAs matched the database results, according to RT-PCR analysis. Six lncRNAs were found to constitute a predictive characteristic for PTC in this study, and these signatures were connected to the infiltration of different immune cell subtypes. These findings provide a potential basis for the development of diagnostic biomarkers and immunotherapeutic stratification in PTC.

Biotechnology and Applied Biochemistry
People's Hospital of Bishan District (CN), First People's Hospital of Chongqing (CN)
Openalex Percentile: Top 19%
Cancer-related molecular mechanisms research
0.94
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