Perineural invasion is an independent risk factor for poor prognosis of cervical cancer patients , and the occurrence of perineural invasion can be effectively predicted by the constructed multivariate mode.

Ran Tang; Gege Jiang; Xiangwen Meng; Zheng Cai; Li Jin; Nan Xiang; Min Zhang; Xiaoyi Jia

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Perineural invasion is an independent risk factor for poor prognosis of cervical cancer patients , and the occurrence of perineural invasion can be effectively predicted by the constructed multivariate mode.

VernacularTitle:机器学习联合生物信息学探究系统性红斑狼疮诊断相关生物标志物
Author: Ran Tang ¹ ; Gege Jiang ¹ ; Xiangwen Meng ¹ ; Zheng Cai ¹ ; Li Jin ² ; Nan Xiang ² ; Min Zhang ² ; Xiaoyi Jia ¹
Author Information

1. School of Pharmacy, Anhui University of Chinese Medicine , Hefei 230012 ; Anhui Province Key Laboratory of Bioactive Natural Products , Hefei 230012
2. Dept of Rheumatology and Immunology, The First Afiliated Hospital of USTC(Anhui Provincial Hospital) ,Hefei 230001
Publication Type:Journal Article
Keywords: systemic lupus erythematosus; machine learning; bioinformatics; HERC5; interferon pathway; biomarker
From: Acta Universitatis Medicinalis Anhui 2025;60(12):2368-2377
CountryChina
Language:Chinese
Abstract: Objective:To predict and screen potential biomarkers of systemic lupus eythematosus(SLE) based on machine learning algorithms and structural biology, and to reveal their mechanisms of action and to provide new targets for disease diagnosis and treatment.
Methods:Four machine learning algorithms, random forest(RF), eXtreme gradient boosting(XGBoost), support vector machine(SVM), least absolute shrinkage and selection operator(LASSO), were used to analyze the gene expression data of SLE patients in GEO(datasets: GSE121239 and GSE11907) to analyze the gene expression data of SLE patients and screen key markers. Peripheral blood single nucleated cells(PBMCs) from SLE patients were collected and RT-qPCR was used to detect differential gene expression levels. Subsequently, GSEA enrichment analysis was used to identify biomarker-related pathways. CIBERSORT immune infiltration analysis and protein interactions network were applied to calculate the sample immune cell infiltration abundance. Single-cell data were analyzed for gene expression specificity in immune cells. Interaction relationships in combination with AlphaFold3(AF3) were predicted.
Results:Multiple algorithms were screened together to identify the unique marker gene HERC5 , and expression analysis of multiple datasets showed that HERC5 was highly expressed in SLE compared to the normal group (P < 0. 05) , and RT⁃qPCR verified the same trend (P = 0. 006 2) . Functional enrichment analysis identified the major pathway promoted by HERC5 in SLE as the interferon receptor signalling pathway (P < 0. 05) . Immune infiltration analysis showed that HERC5 was closely associated with immune cells (Neutrophils : r = 0. 39 , P < 0. 05 ; Memory B cells : r = 0. 33 , P < 0. 05 ; Activated dendritic cell : r = 0. 52 , P < 0. 05) . Most HERC5 ⁃related interacting proteins were associated with SLE ,and potential transcription factors of HERC5 and its related genes were also significantly associated with immune responses.
Conclusion :The HERC5 gene is an important biomarker for SLE , which upregulates the interferon pathway to promote SLE progression and provides a new target for SLE diagnosis and treatment.
Full text:2026030410022664242机器学习联合生物信息学探究系统性红斑狼疮诊断相关生物标志物_唐然.pdf