ArticleCell biochemistry and biophysics2025
Integrative In-Silico Analysis of Retroperitoneal Tumors in Colorectal Surgery: Advancements and Implications.
Article in Cell biochemistry and biophysics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Lipid Modifications and Inflammation in Cardiometabolic and Vascular Disease: Perspectives from a Special Edition Honoring a Pioneer Scientist.Cell biochemistry and biophysics · 2026Article
- An Update of AI and Radiomics in Precision Oncology: Insights from Liver Tumors as Case Models.Technology in cancer research & treatmentReview
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
Abstract
Retroperitoneal tumors pose significant challenges in colorectal surgery due to their complex anatomical location, aggressive behavior, and heterogeneous nature. Traditional diagnostic and treatment methods often fall short in effectively managing these tumors. This study leverages advanced in-silico methodologies to perform a comprehensive analysis of retroperitoneal tumors associated with colorectal conditions. By integrating computational modeling and cutting-edge bioinformatics tools, we aim to enhance the understanding of tumor biology, improve diagnostic precision, and optimize surgical outcomes. Our integrative approach combines transcriptomic, and proteomic data from publicly available databases such as The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). Transcriptomic analysis reveals differentially expressed genes (DEGs) that serve as potential biomarkers for early diagnosis and prognosis. Proteomic analysis highlights critical protein interaction networks and pathways involved in tumorigenesis and metastasis. Our integrative approach identifies key DEGs and constructs protein-protein interaction (PPI) networks to pinpoint critical regulatory genes, such as VWF, PF4, ITGA2B, CXCL8, and GP9, that may serve as potential biomarkers or therapeutic targets. Functional enrichment analysis reveals significant pathways involved in tumorigenesis, including cell proliferation, immune response, and DNA repair. Additionally, immune cell infiltration analysis using the CIBERSORT algorithm demonstrates an immunosuppressive tumor microenvironment characterized by increased regulatory T cells (Tregs) and M2 macrophages, which could contribute to tumor immune evasion.Future studies should focus on clinical validation of these findings and the expansion of computational models to include diverse patient populations. Through these efforts, we aim to revolutionize the management of retroperitoneal tumors in colorectal surgery, ultimately improving patient care and survival rates.
Indexed as
Identifiers
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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.