Evidence map›Paper›PMID 36101460›Full record

ArticleBiology2022

Identifying General Tumor and Specific Lung Cancer Biomarkers by Transcriptomic Analysis.

Beatriz Andrea Otálora-Otálora, Daniel Alejandro Osuna-Garzón, Michael Steven Carvajal-Parra, Alejandra Cañas, Martín Montecino, Liliana López-Kleine, Adriana Rojas

Open access · goldAbstract read
In one paragraph

Article in Biology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.

0numbers the graph read from it
0cells of the map it votes in
11citing papers in PubMed
0.9field-weighted citation impact, top 29% 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

11 citing papers in PubMed, 10 citations in OpenAlex.

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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

7 authors at 3 institutions in 2 countries.

Beatriz Andrea Otálora-OtáloraFacultad de Medicina, Universidad Nacional de Colombia, Bogotá 11001, Colombia.ORCID 0000-0002-5668-0266
Daniel Alejandro Osuna-GarzónDepartamento de Estadística, Universidad Nacional de Colombia, Bogotá 11001, Colombia.ORCID 0000-0003-2967-6576
Michael Steven Carvajal-ParraDepartamento de Estadística, Universidad Nacional de Colombia, Bogotá 11001, Colombia.
Alejandra CañasDepartamento de Medicina Interna, Facultad de Medicina, Pontificia Universidad Javeriana, Bogotá 110211, Colombia.ORCID 0000-0003-3975-2835
Martín MontecinoInstitute of Biomedical Sciences, Facultad de Medicina y Facultad de Ciencias de la Vida, Universidad Andres Bello, Santiago 8370134, Chile.ORCID 0000-0002-6025-0023
Liliana López-KleineDepartamento de Estadística, Universidad Nacional de Colombia, Bogotá 11001, Colombia.ORCID 0000-0001-9325-9529
Adriana RojasInstituto de Genética Humana, Facultad de Medicina, Pontificia Universidad Javeriana, Bogotá 110211, Colombia.ORCID 0000-0001-8528-4433
Universidad Nacional de Colombia · COPontificia Universidad Javeriana · COUniversidad Andrés Bello · CL

Funding

National University of Colombia 41492Pontificia Universidad Javeriana PUJ ID7687
6 · The paper itself

Abstract

The bioinformatic pipeline previously developed in our research laboratory is used to identify potential general and specific deregulated tumor genes and transcription factors related to the establishment and progression of tumoral diseases, now comparing lung cancer with other two types of cancer. Twenty microarray datasets were selected and analyzed separately to identify hub differentiated expressed genes and compared to identify all the deregulated genes and transcription factors in common between the three types of cancer and those unique to lung cancer. The winning DEGs analysis allowed to identify an important number of TFs deregulated in the majority of microarray datasets, which can become key biomarkers of general tumors and specific to lung cancer. A coexpression network was constructed for every dataset with all deregulated genes associated with lung cancer, according to DAVID's tool enrichment analysis, and transcription factors capable of regulating them, according to oPOSSUM´s tool. Several genes and transcription factors are coexpressed in the networks, suggesting that they could be related to the establishment or progression of the tumoral pathology in any tissue and specifically in the lung. The comparison of the coexpression networks of lung cancer and other types of cancer allowed the identification of common connectivity patterns with deregulated genes and transcription factors correlated to important tumoral processes and signaling pathways that have not been studied yet to experimentally validate their role in lung cancer. The Kaplan-Meier estimator determined the association of thirteen deregulated top winning transcription factors with the survival of lung cancer patients. The coregulatory analysis identified two top winning transcription factors networks related to the regulatory control of gene expression in lung and breast cancer. Our transcriptomic analysis suggests that cancer has an important coregulatory network of transcription factors related to the acquisition of the hallmarks of cancer. Moreover, lung cancer has a group of genes and transcription factors unique to pulmonary tissue that are coexpressed during tumorigenesis and must be studied experimentally to fully understand their role in the pathogenesis within its very complex transcriptomic scenario. Therefore, the downstream bioinformatic analysis developed was able to identify a coregulatory metafirm of cancer in general and specific to lung cancer taking into account the great heterogeneity of the tumoral process at cellular and population levels.

Indexed as

breast cancer (BC)coexpression networksdifferentially expressed genes (DEGs)early detection and prognosis biomarkersleukemia (LK)lung cancer (LC)

Identifiers

PMID36101460
PMCPMC9313083
OpenAlexW4286005846

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.