Evidence map›Paper›PMID 38765019›Full record

ArticleAmerican journal of clinical and experimental immunology2024

Insight into NSCLC through novel analysis of gene interactions and characteristics.

Eric Pan, Yongsheng Bai

Abstract read
In one paragraph

Article in American journal of clinical and experimental immunology, 2024. 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

2 authors.

Eric PanDebakey High School Houston, TX 77030, USA.
Yongsheng BaiNext-Gen Intelligent Science Training Ann Arbor, MI 48105, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Around 80 to 85% of all lung cancers are non-small cell lung cancer (NSCLC). Previous research has aimed at exploring the genetic basis of NSCLC through individual approaches, but studies have yet to investigate the results of combining them. Here we show that analyzing NSCLC genetics through three approaches simultaneously creates unique insights into our understanding of the disease. Through a combination of previous research and bioinformatics tools, we determined 35 NSCLC candidate genes. We analyzed these genes in 3 different approaches. First, we found the gene fusions between these candidate genes. Second, we found the common superfamilies between genes. Finally, we identified mutational signatures that are possibly associated with NSCLC. Each approach has its individual, unique results. Fusion relationships identify specific gene fusion targets, common superfamilies identify possible avenues to determine novel target genes, and identifying NSCLC associated mutational signatures has diagnostic and prognostic benefits. Combining the approaches, we found that gene CD74 has significant fusion relationships, but it has no association with the other two approaches, suggesting that CD74 is associated with NSCLC mainly because of its fusion relationships. Targeting the gene fusions of CD74 may be an alternative NSCLC treatment. This genetic analysis has indeed created unique insight into NSCLC genes. Both the results from each of the approaches separately and combined allow pursuit of more effective treatment strategies for this cancer. The methodology presented can also apply to other cancers, creating insights that current analytical methods could not find.

Indexed as

CD74common superfamiliesgene fusionsmutational signaturesNon-small cell lung cancer (NSCLC)

Identifiers

PMID38765019
PMCPMC11101995

What OpenQuestion holds

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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.