Evidence map›Paper›PMID 39420072›Full record

ArticleMedical oncology (Northwood, London, England)2024

Personalized oncology in pheochromocytomas and paragangliomas: integrating genetic analysis with machine learning.

Abida, Abdullah R Alzahrani, Hayaa M Alhuthali, Afnan Alkathiri, Ruba Omar M Almaghrabi, Jawaher Mohammad Alshehri, Syed Mohammed Basheeruddin Asdaq, Mohd Imran

Abstract read
PubMed Publisher
In one paragraph

Article in Medical oncology (Northwood, London, England), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Review
  2. Review
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

8 authors.

AbidaDepartment of Pharmaceutical Chemistry, College of Pharmacy, Northern Border University, 91911, Rafha, Saudi Arabia.
Abdullah R AlzahraniDepartment of Pharmacology and Toxicology, Faculty of Medicine, Umm Al-Qura University, Al-Abidiyah, P.O. Box 13578, 21955, Mecca, Saudi Arabia.
Hayaa M AlhuthaliDepartment of Clinical Laboratory Sciences, College of Applied Medical Sciences, Taif University, P.O. Box 11099, 21944, Taif, Saudi Arabia.
Afnan AlkathiriLaboratory Medicine Department, Faculty of Applied Medical Sciences, Al-Baha University, 65431, Albaha, Saudi Arabia.
Ruba Omar M AlmaghrabiLaboratory Medicine Department, Faculty of Applied Medical Sciences, Al-Baha University, 65431, Albaha, Saudi Arabia.
Jawaher Mohammad AlshehriOptometry Department, Faculty of Applied Medical Sciences, Al-Baha University, 65431, Al-Baha, Saudi Arabia.
Syed Mohammed Basheeruddin AsdaqDepartment of Pharmacy Practice, College of Pharmacy, AlMaarefa University, Dariyah, 13713, Riyadh, Saudi Arabia.
Mohd ImranDepartment of Pharmaceutical Chemistry, College of Pharmacy, Northern Border University, 91911, Rafha, Saudi Arabia. imran.pchem@gmail.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Pheochromocytomas and paragangliomas (PCCs/PGLs) are uncommon neuroendocrine tumors with a significant genetic tendency. Approximately 35-40% of these tumors are associated with genetic factors. The present study performed a thorough analysis using publicly accessible genetic and clinical data from the Cancer Genome Atlas (TCGA) to examine the involvement of six genes, namely GBP1, KIF13B, GPT, CSDE1, CEP164, and CLCA1, in the development of PCCs/PGLs. By employing multi-omics data, this study investigates the relationship between mutational patterns and the prognosis of tumors, focusing on the possibility of tailoring treatment methods to individual patients. The study utilizes Mutect2 to detect somatic mutations with high confidence in whole-exome sequencing data from PCCG samples. The study uncovers mild effects on protein function caused by particular mutations, including GBP1 (p.Cys12Tyr), KIF13B (p.Arg847Gly), and GPT (p.Gln50Arg). A random forest classifier uses mutational profiles to predict potential drug recommendations, proposing a focused therapy strategy. This study thoroughly analyzes the genetic mutations found in PCCs/PGLs, highlighting the significance of precision medicine in developing specific treatments for these uncommon types of cancer. This study aims to improve the understanding of the development of tumors and identify personalized treatment approaches by combining genetic data with machine learning analyses.

Indexed as

Adrenal Gland NeoplasmsMachine LearningParagangliomaPheochromocytomaPrecision MedicineExome SequencingGenetic TestingHumansMutationGenetic mutationsMachine learningParagangliomaPersonalized medicinePheochromocytomaPrecision oncology

Identifiers

PMID39420072

What Socratic holds

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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.