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Related Concept Videos

lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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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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MicroRNA Based Liquid Biopsy: The Experience of the Plasma miRNA Signature Classifier MSC for Lung Cancer Screening
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Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis.

Beatriz Andrea Otálora-Otálora1, Daniel Alejandro Osuna-Garzón2, Michael Steven Carvajal-Parra2

  • 1Facultad de Medicina, Universidad Nacional de Colombia, Bogotá 11001, Colombia.

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|September 14, 2022
PubMed
Summary
This summary is machine-generated.

This study identifies key genes and transcription factors involved in lung cancer and other tumors. It reveals common and unique molecular patterns, offering potential biomarkers for cancer diagnosis and treatment.

Keywords:
breast cancer (BC)coexpression networksdifferentially expressed genes (DEGs)early detection and prognosis biomarkersleukemia (LK)lung cancer (LC)

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Area of Science:

  • Bioinformatics
  • Cancer Genomics
  • Transcriptomics

Background:

  • Tumoral diseases involve complex genetic and regulatory alterations.
  • Identifying common and specific deregulated genes and transcription factors (TFs) is crucial for understanding cancer progression.
  • Lung cancer shares some molecular mechanisms with other cancers but also possesses unique features.

Purpose of the Study:

  • To identify general and specific deregulated tumor genes and TFs in lung cancer compared to two other cancer types.
  • To uncover common and unique molecular patterns and regulatory networks across different cancers.
  • To identify potential biomarkers for general tumors and specifically for lung cancer.

Main Methods:

  • Analysis of twenty microarray datasets using a bioinformatic pipeline.
  • Identification of differentially expressed genes (DEGs) and hub genes.
  • Construction of coexpression networks and enrichment analysis (DAVID, oPOSSUM).
  • Kaplan-Meier survival analysis for identified TFs.

Main Results:

  • Identified numerous deregulated TFs common to multiple cancers and specific to lung cancer.
  • Revealed common connectivity patterns in coexpression networks between lung and other cancers.
  • Discovered thirteen TFs significantly associated with lung cancer patient survival.
  • Identified two TF networks regulating gene expression in lung and breast cancer.

Conclusions:

  • A coregulatory network of TFs is implicated in the hallmarks of cancer.
  • Lung cancer exhibits unique genes and TFs coexpressed during tumorigenesis.
  • The study identified a regulatory 'metafirm' for cancer, both general and lung-specific, highlighting the heterogeneity of cancer.