Article in Biomolecules, 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.
Mercedes Olvera-ValenciaPrograma Institucional de Biomedicina Molecular, Escuela Nacional de Medicina y Homeopatía del IPN, Guillermo Massieu Helguera #239 Fracc. La escalera, Ticoman 07320, Ciudad de Mexico, Mexico.
Fernando Luna-MaldonadoUnidad de Investigación Biomédica en Cáncer, Insituto de Investigaciones Biomédicas-Universidad Nacional Autónoma de México, Instituto Nacional de Cancerología, Mexico City 14080, Mexico.ORCID 0000-0003-1418-1511
Joselyn Juarez-ReyesLaboratorio de Genómica, Instituto Nacional de Cancerología, San Fernando 22. Col. Sección XVI, Tlalpan 14080, Ciudad de Mexico, Mexico.ORCID 0009-0003-5046-4233
Alejandro Lopez-SaavedraAdvanced Microscopy Applications Unit (ADMIRA)-Instituto Nacional de Cancerología, San Fernando 22. Col. Sección XVI, Tlalpan 14080, Ciudad de Mexico, Mexico.
Jossimar Coronel-HernandezLaboratorio de Genómica, Instituto Nacional de Cancerología, San Fernando 22. Col. Sección XVI, Tlalpan 14080, Ciudad de Mexico, Mexico.ORCID 0000-0001-6543-5310
Oliver Millan-CatalanLaboratorio de Genómica, Instituto Nacional de Cancerología, San Fernando 22. Col. Sección XVI, Tlalpan 14080, Ciudad de Mexico, Mexico.ORCID 0000-0003-1881-9324
Daniel Guzman-GomezLADISER de Inmunología y Biología Molecular, Facultad de Ciencias Químicas, Universidad Veracruzana (UV), Prolongación de Oriente 6 #1009, Colonia Rafael Alvarado, Orizaba 94340, Veracruz, Mexico.
Frida Rodríguez-IzquierdoLaboratorio de Genómica, Instituto Nacional de Cancerología, San Fernando 22. Col. Sección XVI, Tlalpan 14080, Ciudad de Mexico, Mexico.
Luis A HerreraUnidad de Investigación Biomédica en Cáncer, Insituto de Investigaciones Biomédicas-Universidad Nacional Autónoma de México, Instituto Nacional de Cancerología, Mexico City 14080, Mexico.ORCID 0000-0003-3998-9306
David Francisco Cantú-De LeónClinical Research, Instituto Nacional de Cancerologia, San Fernando 22. Col. Sección XVI, Tlalpan, Ciudad de Mexico 14080, Mexico.
Carlos Perez-PlasenciaLaboratorio de Genómica, Instituto Nacional de Cancerología, San Fernando 22. Col. Sección XVI, Tlalpan 14080, Ciudad de Mexico, Mexico.ORCID 0000-0002-8593-8211
Eloy-Andres Pérez-YepezLaboratorio de Genómica, Instituto Nacional de Cancerología, San Fernando 22. Col. Sección XVI, Tlalpan 14080, Ciudad de Mexico, Mexico.ORCID 0000-0003-2876-9463
Funding
Consejo Nacional de Humanidades, Ciencias y Tecnologías CF-2023-I-2071
6 · The paper itself
Abstract
Sarcomas are heterogeneous mesenchymal tumors, and their pharmacological treatment remains challenging due to the high toxicity and poor efficacy of current therapies. This study aimed to identify common overexpressed kinases in the four most frequent sarcoma subtypes to establish novel therapeutic targets. A bioinformatics approach using patient-derived gene expression data sets identified overexpressed kinases shared across these sarcoma types. Later,
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.
BUB1 an Overexpressed Kinase in Sarcoma: Finding New Target Therapy for Osteosarcoma, Liposarcoma, Synovial Sarcoma, and Leiomyosarcoma. · full record | OpenQuestion