Evidence map›Paper›PMID 42337154›Full record

ArticleDiscover oncology2026

Metabolic pathway signatures define prognostic subtypes of lung adenocarcinoma.

Doris Kafita, Zitha Mukwamba, Vanity Mapulanga, Adon Chawe, Musalula Sinkala

Abstract read
In one paragraph

Article in Discover oncology, 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

5 authors.

Doris KafitaDepartment of Biomedical Sciences, School of Health Sciences, The University of Zambia, Nationalist Road, P.O. Box 50110, Lusaka, Zambia.
Zitha MukwambaDepartment of Biomedical Sciences, School of Health Sciences, The University of Zambia, Nationalist Road, P.O. Box 50110, Lusaka, Zambia.
Vanity MapulangaDepartment of Biomedical Sciences, School of Health Sciences, The University of Zambia, Nationalist Road, P.O. Box 50110, Lusaka, Zambia.
Adon ChaweDepartment of Biomedical Sciences, School of Health Sciences, The University of Zambia, Nationalist Road, P.O. Box 50110, Lusaka, Zambia.
Musalula SinkalaDepartment of Biomedical Sciences, School of Health Sciences, The University of Zambia, Nationalist Road, P.O. Box 50110, Lusaka, Zambia. smsinks@icloud.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Metabolic reprogramming is a core hallmark of cancer, yet how it contributes to the clinical heterogeneity of lung adenocarcinoma (LUAD) remains poorly understood. Here, by applying unsupervised clustering to the metabolic transcriptomes of 510 LUAD tumours, we identify two robust and prognostically significant subtypes. We show that these subtypes have divergent clinical outcomes, with subtype-1 patients exhibiting significantly longer overall and disease-specific survival compared to the more aggressive subtype-2. This clinical distinction is underpinned by fundamentally different metabolic architectures: the aggressive subtype-2 is characterized by the upregulation of central carbon metabolism, including the citric acid cycle and respiratory electron transport, whereas the less aggressive subtype-1 shows a distinct enrichment for choline catabolism. Consistent with this, Cox regression analysis reveals that high expression of the TCA cycle enzyme OGDH is a top predictor of increased disease risk, while the glycolytic enzyme PGK1 is associated with a decreased risk. Our findings establish a direct link between transcriptional metabolic states and patient survival in LUAD, defining a framework for prognostic stratification and identifying subtype-specific metabolic vulnerabilities for therapeutic targeting.

Indexed as

BioinformaticsCholine metabolismCitric acid cycleLung adenocarcinomaMachine learningMetabolic reprogrammingPrognosisSurvival analysisTranscriptomeTumour subtyping

Identifiers

PMID42337154
PMCPMC13547566

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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 Socratic graph.