Evidence map›Paper›PMID 38226611›Full record

ArticleTechnology in cancer research & treatment

PRADclass: Hybrid Gleason Grade-Informed Computational Strategy Identifies Consensus Biomarker Features Predictive of Aggressive Prostate Adenocarcinoma.

Alex Stanley Balraj, Sangeetha Muthamilselvan, Rachanaa Raja, Ashok Palaniappan

Erratum issuedOpen access · goldAbstract read
PubMed Full text at PMC Publisher
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Article in Technology in cancer research & treatment. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing papers in PubMed
2.1field-weighted citation impact, top 14% of its field
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

4 citing papers in PubMed, 5 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors at 2 institutions in 1 country.

Alex Stanley BalrajDepartment of Bioinformatics, School of Chemical and Biotechnology, SASTRA Deemed to be University, Thanjavur, India.
Sangeetha MuthamilselvanDepartment of Bioinformatics, School of Chemical and Biotechnology, SASTRA Deemed to be University, Thanjavur, India.ORCID 0000-0002-2269-5216
Rachanaa RajaDepartment of Pharmaceutical Technology, UCE, Anna University (BIT campus), Trichy, India.ORCID 0009-0002-3211-9682
Ashok PalaniappanDepartment of Bioinformatics, School of Chemical and Biotechnology, SASTRA Deemed to be University, Thanjavur, India.ORCID 0000-0003-2841-9527
SASTRA University · INAnna University, Chennai · IN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundProstate adenocarcinoma (PRAD) is a common cancer diagnosis among men globally, yet large gaps in our knowledge persist with respect to the molecular bases of its progression and aggression. It is mostly indolent and slow-growing, but aggressive prostate cancers need to be recognized early for optimising treatment, with a view to reducing mortality.

methodsBased on TCGA transcriptomic data pertaining to PRAD and the associated clinical metadata, we determined the sample Gleason grade, and used it to execute: (i) Gleason-grade wise linear modeling, followed by five contrasts against controls and ten contrasts between grades; and (ii) Gleason-grade wise network modeling via weighted gene correlation network analysis (WGCNA). Candidate biomarkers were obtained from the above analysis and the consensus found. The consensus biomarkers were used as the feature space to train ML models for classifying a sample as benign, indolent or aggressive.

resultsThe statistical modeling yielded 77 Gleason grade-salient genes while the WGCNA algorithm yielded 1003 trait-specific key genes in grade-wise significant modules. Consensus analysis of the two approaches identified two genes in Grade-1 (SLC43A1 and PHGR1), 26 genes in Grade-4 (including LOC100128675, PPP1R3C, NECAB1, UBXN10, SERPINA5, CLU, RASL12, DGKG, FHL1, NCAM1, and CEND1), and seven genes in Grade-5 (CBX2, DPYS, FAM72B, SHCBP1, TMEM132A, TPX2, UBE2C). A RandomForest model trained and optimized on these 35 biomarkers for the ternary classification problem yielded a balanced accuracy ∼ 86% on external validation.

conclusionsThe consensus of multiple parallel computational strategies has unmasked candidate Gleason grade-specific biomarkers. PRADclass, a validated AI model featurizing these biomarkers achieved good performance, and could be trialed to predict the differentiation of prostate cancers. PRADclass is available for academic use at: https://apalania.shinyapps.io/pradclass (online) and https://github.com/apalania/pradclass (command-line interface).

Indexed as

AdenocarcinomaProstatic NeoplasmsBiomarkersConsensusHumansIntracellular Signaling Peptides and ProteinsLIM Domain ProteinsMaleMuscle ProteinsNeoplasm GradingProstateShc Signaling Adaptor ProteinsBiomarkersFHL1 protein, humanIntracellular Signaling Peptides and ProteinsLIM Domain ProteinsMuscle ProteinsSHCBP1 protein, humanShc Signaling Adaptor Proteinscancer aggressivenesscancer differentiationconsensus biomarkerGleason gradinggrade-salient genemachine learningprostate adenocarcinomarisk stratificationtrait-specific key geneWGCNA-based reconstruction

Identifiers

PMID38226611
PMCPMC10793196
OpenAlexW4390919900

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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 OpenQuestion graph.