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lncRNA - Long Non-coding RNAs

lncRNA - Long Non-coding RNAs

In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...

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Integrated Analysis Of Single-cell And Bulk Rna-sequencing Identifies A Metastasis-related Gene Signature For Predicting Prognosis In Lung Adenocarcinoma.

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Integrated Analysis Of Single-cell And Bulk Rna-sequencing Identifies A Metastasis-related Gene Signature For Predicting Prognosis In Lung Adenocarcinoma.

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Integrated analysis of single-cell and bulk RNA-sequencing identifies a metastasis-related gene signature for

Xu Cao^1,2, Jingjing Xi², Congyue Wang^2,3

¹Department of Oncology, The Affiliated Hospital of Xuzhou Medical University, Xuzhou, 221006, Jiangsu, China.

Clinical & Translational Oncology : Official Publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico

|November 8, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

Keywords:

Lung adenocarcinoma Machine learning Metastasis Prognostic signature Single-cell RNA-sequencing

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A new lung adenocarcinoma metastasis-related gene signature (LMRGS) accurately predicts patient prognosis. This signature identifies high-risk patients, enabling personalized treatment strategies for improved clinical outcomes.

Area of Science:

Oncology
Genomics
Bioinformatics

Background:

Metastasis is a critical prognostic factor in lung adenocarcinoma (LUAD).
The precise genetic and molecular drivers of LUAD metastasis remain incompletely understood.
Identifying mechanisms of metastasis is crucial for improving patient outcomes.

Purpose of the Study:

To elucidate the molecular mechanisms underlying LUAD metastasis.
To develop and validate a prognostic gene signature for LUAD metastasis.
To identify potential therapeutic targets for LUAD.

Main Methods:

Integrated single-cell transcriptomic analysis of primary and metastatic LUAD.
Construction of a LUAD metastasis-related gene signature (LMRGS) using machine learning algorithms.

Validation of the LMRGS across multiple independent cohorts.

Functional validation of CCT6A gene in LUAD cell metastasis.

Main Results:

Signaling pathway remodeling and metabolic reprogramming are key in LUAD cell metastasis.
A novel LMRGS was developed, demonstrating superior accuracy in predicting prognosis compared to existing signatures.
The LMRGS effectively stratified patients into high- and low-risk groups with distinct survival outcomes.
Knockdown of CCT6A inhibited LUAD cell apoptosis and migration.

Conclusions:

The LMRGS shows significant potential as a prognostic tool for lung adenocarcinoma.
This signature can aid in stratifying patients for personalized treatment approaches.
Further investigation into CCT6A may reveal therapeutic strategies for LUAD metastasis.