Article in International journal of molecular sciences, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
0numbers the graph read from it
0cells of the map it votes in
1citing 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.
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
12 authors.
Diego Armando Alvarado-CamachoLaboratorio de microRNAs y Cáncer, Unidad Académica de Ciencias Biológicas, Universidad Autónoma de Zacatecas "Francisco García Salinas", Av. Preparatoria S/N, Zacatecas 98066, Mexico.
Ricardo Castillo-VelázquezUnidad de Investigación Biomédica de Zacatecas, Instituto Mexicano del Seguro Social, Zacatecas 98000, Mexico.ORCID 0009-0003-2396-6115
Angelica Judith Granados-LópezLaboratorio de microRNAs y Cáncer, Unidad Académica de Ciencias Biológicas, Universidad Autónoma de Zacatecas "Francisco García Salinas", Av. Preparatoria S/N, Zacatecas 98066, Mexico.
Hiram Hernández-LópezLaboratorio de Síntesis Química, Unidad Académica de Ciencias Químicas, Campus Siglo XXI, Universidad Autónoma de Zacatecas "Francisco García Salinas", Kilometro 6, Ejido la Escondida, Zacatecas 98160, Mexico.ORCID 0000-0002-4286-163X
Yamilé López-HernándezLaboratorio de Proteómica y Metabolómica, Unidad Académica de Ciencias Biológicas, Universidad Autónoma de Zacatecas "Francisco García Salinas", Av. Preparatoria S/N, Zacatecas 98066, Mexico.ORCID 0000-0002-1068-603X
Rosalinda Gutiérrez-HernándezLaboratorio de Etnofarmacología y Nutrición, Unidad Académica de Nutrición, Campus Siglo XXI, Universidad Autónoma de Zacatecas "Francisco García Salinas", Kilometro 6, Ejido la Escondida, Zacatecas 98160, Mexico.ORCID 0000-0001-6803-925X
José Antonio Varela-SilvaLaboratorio de microRNAs y Cáncer, Unidad Académica de Ciencias Biológicas, Universidad Autónoma de Zacatecas "Francisco García Salinas", Av. Preparatoria S/N, Zacatecas 98066, Mexico.
Claudia Araceli Reyes-EstradaLaboratorio de Inmunohistoquímica y Estrés Oxidativo, Unidad Académica de Medicina Humana, Campus Siglo XXI, Universidad Autónoma de Zacatecas "Francisco García Salinas", Kilometro 6, Ejido la Escondida, Zacatecas 98160, Mexico.
Cesar Rogelio Solorio-AlvaradoDivisión de Ciencias Naturales y Exactas, Departamento de Química, Campus Guanajuato, Universidad de Guanajuato, Noria Alta S/N, Guanajuato 36050, Mexico.ORCID 0000-0001-6082-988X
Sergio Hugo Sánchez-RodríguezLaboratorio de Biología Celular y Neurobiología, Unidad Académica de Ciencias Biológicas, Universidad Autónoma de Zacatecas "Francisco García Salinas", Av. Preparatoria S/N, Zacatecas 98066, Mexico.
David Alejandro García-LópezLaboratorio de Biología Celular y Neurobiología, Unidad Académica de Ciencias Biológicas, Universidad Autónoma de Zacatecas "Francisco García Salinas", Av. Preparatoria S/N, Zacatecas 98066, Mexico.ORCID 0000-0003-2064-1959
Jesús Adrián LópezLaboratorio de microRNAs y Cáncer, Unidad Académica de Ciencias Biológicas, Universidad Autónoma de Zacatecas "Francisco García Salinas", Av. Preparatoria S/N, Zacatecas 98066, Mexico.ORCID 0000-0001-8012-7041
Funding
CONAHCYT FIDCC 2022/321616
6 · The paper itself
Abstract
Cervical cancer remains a significant cause of cancer-related mortality among women, particularly in low- and middle-income countries. High-throughput technologies, such as microarrays, have facilitated the comprehensive analysis of gene expression profiles in cervical cancer, enabling the identification of key differentially expressed genes (DEGs) involved in its pathogenesis. The publicly available microarray datasets, including GSE39001, GSE9750, GSE7803, GSE6791, GSE63514, and GSE52903 in combination with bioinformatics database predictions, were used to identify differential expression genes, potential biomarkers, and therapeutic targets for cervical cancer; additionally, we undertook bioinformatic analysis to determine gene ontology and possible miRNA targets related to our DEGs. Our analysis revealed several DEGs significantly associated with cervical cancer progression, such as cell death, regulation of DNA replication, protein binding processes, and transcription factors. The most relevant transcription factors (TFs) identified were SP1, ELF3, E2F1, TP53, RELA, HDAC, and FOXM1. Importantly, the DEGs with more important changes were 11 coding genes that were upregulated (
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.
Differentially Expressed Genes Associated with the Development of Cervical Cancer. · full record | OpenQuestion