Evidence map›Paper›PMID 42608544›Full record

ReviewJournal of human genetics2026

Clinical significance of miRNAs and exosomal miRNAs in non-small cell lung cancer: diagnostic, prognostic, and therapeutic perspectives.

Imteyaz Ahmad Khan, Surabhi Gupta, Jagdish Prasad Meena

Abstract readReview
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In one paragraph

Review in Journal of human genetics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Imteyaz Ahmad KhanDivision of Pediatric Oncology, Department of Pediatrics, All India Institute of Medical Sciences, New Delhi, India.
Surabhi GuptaDepartment of Reproductive Biology, All India Institute of Medical Sciences, New Delhi, India.
Jagdish Prasad MeenaDivision of Pediatric Oncology, Department of Pediatrics, All India Institute of Medical Sciences, New Delhi, India. drjpmeena@yahoo.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Lung cancer is the most frequently diagnosed malignancy and the leading cause of cancer-related mortality worldwide, with approximately 85% of cases classified as non-small cell lung cancer (NSCLC). Despite significant advances in diagnostic techniques and therapeutic strategies, including immunotherapy and targeted therapies, the prognosis of lung cancer remains poor, with an overall 5-year survival rate of approximately 22%. MicroRNAs (miRNAs) are small, single-stranded, non-coding RNA molecules that regulate gene expression at the post-transcriptional level and play critical roles in key biological processes, such as proliferation, apoptosis, migration, and invasion. miRNAs can function as oncogenes (oncomiRs) or tumor suppressors (TS-miRs), and their dysregulation is frequently observed in various malignancies, including NSCLC. Owing to their remarkable stability in serum and plasma, miRNAs have emerged as promising non-invasive biomarkers for the diagnosis and prognosis of NSCLC. In particular, exosomal miRNAs are encapsulated within a lipid bilayer that protects them from enzymatic degradation, thereby further enhancing their utility as liquid biopsy components and as potential biomarkers and therapeutic targets. Moreover, a single miRNA can regulate multiple target genes, highlighting their promise as therapeutic targets. This review summarizes the diagnostic, prognostic, predictive, and therapeutic potential of tissue-derived, circulating, and exosomal miRNAs in NSCLC and discusses the major challenges limiting the clinical translation of miRNA-based biomarkers and therapeutics.

Identifiers

PMID42608544

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.