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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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lncRNA - Long Non-coding RNAs02:39

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siRNA - Small Interfering RNAs02:30

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Small interfering RNAs, or siRNAs, are short regulatory RNA molecules that can silence genes post-transcriptionally, as well as the transcriptional level in some cases. siRNAs are important for protecting cells against viral infections and silencing transposable genetic elements.
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The mammalian target of rapamycin or mTOR protein was discovered in 1994 due to its direct interaction with rapamycin. The protein gets its name from a yeast homolog called TOR. The mTOR protein complex in mammalian cells plays a major role in balancing anabolic processes such as the synthesis of proteins, lipids, and nucleotides and catabolic processes, such as autophagy in response to environmental cues, such as availability of nutrients and growth factors.
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piRNA - Piwi-interacting RNAs02:57

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PIWI-interacting RNAs, or piRNAs, are the most abundant short non-coding RNAs. More than 20,000 genes have been found in humans that code for piRNAs while only 2000 genes have been found for miRNAs. piRNAs can act at the transcriptional and post-transcriptional levels and have a vital role in silencing transposable elements present in germ cells. They are also involved in epigenetic silencing and activation. Previously, they were thought to function only in germ cells but new evidence suggests...
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Review and Preview01:10

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In statistics, several tools are used to interpret the data. Measures of central tendency represent the characteristics of the data, such as mean, median, and mode. Additionally, measures of variance like standard deviation and range are used to find the spread of data from the mean. Relative standing measures the distance between data locations. Commonly used measures of relative standings are percentile, z score, and quartiles.
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Related Experiment Video

Updated: Feb 14, 2026

Author Spotlight: Establishing a Murine Non-Small Cell Lung Cancer Model for Developing Nanoformulations of Anticancer Drugs
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Research progress on long non‑coding RNAs in lung cancer (Review).

Renjie Pan1, Chaohui Wang1, Yan Tang1

  • 1Department of Laboratory Medicine, Xinghua People's Hospital Affiliated to Yangzhou University, Taizhou, Jiangsu 225700, P.R. China.

Molecular Medicine Reports
|February 13, 2026
PubMed
Summary

Long non-coding RNAs (lncRNAs) are key regulators in lung cancer. This review details their roles, detection, and potential as biomarkers for improved lung cancer diagnosis and treatment.

Keywords:
biomarkerchemoresistanceexosomal long non‑coding RNAslong non‑coding RNAslung cancerprognosistreatment

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Area of Science:

  • Oncology
  • Molecular Biology
  • Genetics

Background:

  • Lung cancer presents significant challenges in early detection and advanced-stage treatment.
  • Identifying reliable biomarkers and understanding molecular mechanisms are crucial for improving patient outcomes.
  • Long non-coding RNAs (lncRNAs) are emerging as critical regulators in various cancer processes.

Purpose of the Study:

  • To provide a comprehensive overview of the mechanistic roles of lncRNAs in lung cancer progression.
  • To discuss the potential of lncRNAs as diagnostic and prognostic biomarkers.
  • To review advancements in lncRNA detection technologies and their clinical translation.

Main Methods:

  • Literature review synthesizing recent advances in lncRNA research related to lung cancer.
  • Analysis of mechanistic roles of lncRNAs in tumor invasion, metastasis, proliferation, apoptosis, and angiogenesis.
  • Discussion of lncRNA involvement in therapy response and chemoresistance.
  • Evaluation of emerging lncRNA detection technologies and their clinical applicability.

Main Results:

  • lncRNAs play significant roles in regulating key processes of lung cancer development and progression.
  • Accumulating evidence supports the promise of lncRNAs as diagnostic and prognostic biomarkers.
  • Various detection technologies are advancing, offering potential for clinical translation.
  • Challenges and limitations in utilizing lncRNAs as biomarkers are identified.

Conclusions:

  • lncRNAs represent a promising area for novel insights into lung cancer.
  • Further research is needed to fully harness the potential of lncRNAs for clinical applications.
  • Addressing current challenges is essential for the successful translation of lncRNA research into clinical practice.