ArticleOncology letters2019
Bioinformatics analysis identifies potential chemoresistance-associated genes across multiple types of cancer.
Article in Oncology letters, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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.
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.
Who cites it
3 citing papers in PubMed.
- Bibliometric-driven research on chemoresistance in breast cancer: knowledge mapping, hotspot evolution, and emerging insights (1994-2024).Discover oncology · 2025Article
- Identification of Key Gene Targets for Sensitizing Colorectal Cancer to Chemoradiation: an Integrative Network Analysis on Multiple Transcriptomics Data.Journal of gastrointestinal cancer · 2022Article
- Post-Hypoxic Cells Promote Metastatic Recurrence after Chemotherapy Treatment in TNBC.Cancers · 2021Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Despite the fact that studies have revealed mechanisms underlying tumor chemoresistance, the functions of numerous potential chemoresistance-associated genes have yet to be elucidated. A bioinformatics analysis was conducted to screen differentially expressed genes (DEGs) across four types of chemoresistant tumors and functional enrichment analysis was used to examine the biological significance of these genes. Furthermore, a gene network was constructed using weighted gene co-expression network analysis to identify hub genes. A total of 6,015, 2,074, 2,141 and 954 differentially expressed genes were identified in estrogen receptor-negative breast cancer, ovarian cancer, rectal cancer and gastric cancer, respectively; however, only five of these DEGs were dysregulated in all four types of cancer. Functional enrichment analysis of the DEGs suggested that genomic stability and immune response are crucial determinants of tumor chemoresistance. In addition, 14, 8, 6 and 1 co-expressed gene modules were identified in estrogen receptor-negative breast cancer, ovarian cancer, rectal cancer and gastric cancer, respectively, and protein-protein interaction networks were created. The analysis identified only calcium-calmodulin-dependent protein kinase kinase 2, erythropoietin receptor, mitochondrial poly(A) RNA polymerase, α-parvin, and zinc finger and BTB domain-containing protein 44 to be dysregulated in all four cancer types, indicating varying mechanisms of chemoresistance in different tumor types. Furthermore, our analysis suggests that type I collagen α1, fibroblast growth factor 14 and major histocompatibility complex, class II, DR β1 potentially serve key roles in the development of chemoresistance. In conclusion, the present study proposes a simple and effective strategy for identifying genes involved in chemoresistance and predicting their potential functional roles, which may guide subsequent experimental studies.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.