Mixed data analysis detected Endocrine Fibroblast Growth Factors (FGF19, FGF21, and FGF23) as Prognostic and Diagnostic Markers of Colorectal Neoplasia and Carcinoma

Rejali, L.; Piroozkhah, M.; Jahanbin, M.; Jalali, P.; Khanabadi, B.; Abkenar, E. D.; Tavallaei, M.; Niasar, M. S.; Hashemi, M.; Sadeghi, A.; Salehi, Z.; Nazemalhosseini-Mojarad, E.

doi:10.1101/2023.06.27.546716

bioRxiv · 10.1101/2023.06.27.546716

Mixed data analysis detected Endocrine Fibroblast Growth Factors (FGF19, FGF21, and FGF23) as Prognostic and Diagnostic Markers of Colorectal Neoplasia and Carcinoma

Rejali, L.·Piroozkhah, M.·Jahanbin, M.·Jalali, P.·Khanabadi, B.·Abkenar, E. D.·Tavallaei, M.·Niasar, M. S.·Hashemi, M.·Sadeghi, A.·Salehi, Z.·Nazemalhosseini-Mojarad, E.

View original source ↗Export metadata ↓

Abstract

BackgroundEndocrine fibroblast growth factors (eFGFs) play important roles in various cellular signaling processes such as development and differentiation. These genes were also found to be significantly related to several cancer. However, little is known about the role of eFGFs in colon neoplasia and colon adenocarcinoma (COAD). MethodsWe performed systematically and comprehensively investigated the gene expression, DNA methylation, prognostic significance, genetic alteration, co-expressed genes, protein-protein interaction, small molecules pathway, and drug interactions of eFGFs based on the TIMER2.0, GEPIA2, UALCAN, OncoDB, cBioPortal, LinkedOmics, STRING, SMPDB, htfTarget, mirTarBase, circBank and DGIdb databases. Ultimately, the correlations of eFGFs expressions between polyp and COAD tissues compared to normal mucosa were validated using qRT-PCR as a cross-sectional part of our study. ResultsThe results indicated that eFGFs are highly expressed in COAD, and abnormal gene expressions may be related to promoter methylation. In this matter, methylation analysis revealed promotor hypermethylation of FGF19 and FGF21. Conversely, FGF23 was shown to have a tendency for promotor hypomethylation. Moreover, hypermethylation of FGF21 and FGF23 and downregulation of FGF23 were found to be detrimental to the survival of COAD patients. KEGG pathway analyses indicated that the co-expressed genes of eFGF family members were mainly related to the regulation of the actin cytoskeleton and, more notably, in Ras signaling, PI3k-Akt signaling, Rap1 signaling, and cancer pathways. Based on qRT-PCR results, FGF21 was significantly overexpressed in the colon polyps compared to normal mucosa. Additionally, RNA expression of FGF21 and FGF23 was markedly elevated in adenomatous polyps as opposed to hyperplastic polyps. ConclusionCollectively, these findings reveal the critical roles of eFGFs in COAD tumorigenesis and suggest eFGF family members as promising prognostic and diagnostic markers for CRC as well as discriminating markers for high-risk from low-risk polyps.

Explore related subjects

bioinformatics Preprint

Keep this discovery

Explore connections, maps & timelines

BibTeX RIS

Rejali, L., Piroozkhah, M., Jahanbin, M., Jalali, P., Khanabadi, B., Abkenar, E. D., Tavallaei, M., Niasar, M. S., Hashemi, M., Sadeghi, A., Salehi, Z., Nazemalhosseini-Mojarad, E.. 2023-06-29. Mixed data analysis detected Endocrine Fibroblast Growth Factors (FGF19, FGF21, and FGF23) as Prognostic and Diagnostic Markers of Colorectal Neoplasia and Carcinoma. https://doi.org/10.1101/2023.06.27.546716

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related preprints

bioRxiv2026

Uncovering Viral Signals Across Cell Populations in Colorectal Cancer

Viral sequences have been detected in colorectal cancer, but their cellular and anatomical distribution remains poorly resolved. We aimed to establish a cell-type-resolved map of viral reference-associated RNA signals across colorectal cancer, tumour-adjacent normal tissue, and healthy colon. We repurposed human-unmapped reads from single-cell RNA sequencing libraries generated in ten publicly available studies. We retained cell-barcode and unique molecular identifier information, removed duplicate molecules, and aligned reads to a curated human-associated viral reference using three aligners. Viral signals were analysed across cell types, tissue conditions, and colorectal segments. We used participant-grouped repeated cross-validation to evaluate tissue-condition classification and screened an independent paired colorectal cancer proteomics cohort for compatible peptide matches. The study included 547 libraries from 112 participants, including 49 with matched colorectal cancer and tumour-adjacent normal samples, and approximately 415,000 cells spanning ten major cell populations. Viral reference-associated RNA signals differed across epithelial, immune, stromal, and vascular compartments, tissue conditions, and anatomical regions of the colon. Of 318 independent healthy-reference viral-load comparisons, seven involving six virus-cell-type groups remained significant after Benjamini-Hochberg correction, including plasma-cell HHV-7 and HHV-6B and mast-cell HCV-2. Cell-type-resolved viral features distinguished colorectal cancer, tumour-adjacent normal tissue, and healthy colon in participant-grouped validation; random forest achieved a macro-AUC of 0.957 (SD 0.028), with HERV-K113 as the leading quantitative feature. An independent proteomic screen identified peptide matches compatible with 20 candidate viral groups. We established a cell-type-resolved map of viral reference-associated RNA signals across colorectal tissue conditions and anatomical regions. By identifying candidate viral signals in epithelial and immune compartments, this map provides a basis for investigating virus-host pathways involved in tumour biology, immune regulation, and antigen-presentation states, and could help prioritise future combination strategies pairing virus-directed or virus-mimetic approaches with standard treatment or immune checkpoint blockade.

bioinformatics↗

bioRxiv2026

Statistical significance testing for spatial domain detection in spatial transcriptomics

In spatial transcriptomics, domain-detection methods group platform-specific spatial measurement units--cells, bins or multicellular spots--into spatial domains for comparing molecular programs, cellular composition and interactions among tissue regions. However, the number of candidate domains detected in the same tissue can vary with user-specified parameters or model-selection criteria. Whether candidate domains should be merged can be framed as a test of a single-domain null hypothesis, under which each gene has constant mean expression across all spatial units in the combined region. However, testing candidate domains on the same gene-expression data used to detect them may induce post-selection bias and yield anti-conservative p-values, increasing the risk of oversegmentation. To address these challenges, we introduce SigDoTS (statistical Significance testing for Domain deTection in Spatial transcriptomics), a hypothesis-testing framework for spatial domain detection. SigDoTS uses hierarchical testing to determine which candidate domains to retain or merge, with adjustment for family-wise error control. It accounts for gene-gene covariance and spatial dependence and computes p-values by rerunning the upstream domain-detection pipeline on datasets simulated under the fitted single-domain null. In simulations, SigDoTS controlled the type I error rate at the root node under the single-domain null and improved recovery of the true spatial domains when multiple domains were present. In human dorsolateral prefrontal cortex, SigDoTS retained superficial and deep Layer 6 domains but merged Layer 3 candidate domains across different initial numbers of candidate domains. In ovarian and colorectal cancers, SigDoTS retained epithelial domains with different gene-expression programs and local immune environments, together with immune, stromal and vascular domains.

bioinformatics↗

bioRxiv2026

ASD Neuro Agentic (ANA): An Agentic Framework for Feature Discovery and Cross Site Generalization in Autism fMRI Classification

Classifiers of autism spectrum disorder (ASD) from resting state functional MRI rarely generalize across sites, and many reported accuracies are inflated by data leakage.We show that an agentic artificial intelligence framework, ASD Neuro Agentic (ANA), whose language model agents propose features from the literature and researcher input while fixed tools perform every calculation, can search auditably for site independent classifiers while controlling the leakage. In 1,028 participants from 17 ABIDE I centers, ANA tested 71 pipelines and selected a ComBat harmonized ensemble of tangent space connectivity models from six parcellations and 98 public connectivity derivatives. In leave one center out testing, the ensemble reached an area under the curve of 0.814 (95% CI, 0.773 to 0.841), outperforming ASD DiagNet and ASD SAENet. Predictive connectivity was mainly reduced in ASD. Agentic AI offers a systematic and efficient approach to exploring, evaluating, and identifying pipelines as model selection grows complex.

bioinformatics↗