An artificial neural network diagnostic model for scleroderma and immune cell infiltration analysis based on mitochondria-associated genes

doi:10.12122/j.issn.1673-4254.2024.05.14

Abstract

Abstract:

Objective To establish a diagnostic model for scleroderma by combining machine learning and artificial neural network based on mitochondria-related genes. Methods The GSE95065 and GSE59785 datasets of scleroderma from GEO database were used for analyzing expressions of mitochondria-related genes, and the differential genes were identified by Random forest, LASSO regression and SVM algorithms. Based on these differential genes, an artificial neural network model was constructed, and its diagnostic accuracy was evaluated by 10-fold crossover verification and ROC curve analysis using the verification dataset GSE76807. The mRNA expressions of the key genes were verified by RT-qPCR in a mouse model of scleroderma. The CIBERSORT algorithm was used to estimate the bioinformatic association between scleroderma and the screened biomarkers. Results A total of 24 differential genes were obtained, including 11 up-regulated and 13 down-regulated genes. Seven most relevant mitochondria-related genes (POLB, GSR, KRAS, NT5DC2, NOX4, IGF1, and TGM2) were screened using 3 machine learning algorithms, and the artificial neural network diagnostic model was constructed. The model showed an area under the ROC curves of 0.984 for scleroderma diagnosis (0.740 for the verification dataset and 0.980 for cross-over validation). RT-qPCR detected significant up-regulation of POLB, GSR, KRAS, NOX4, IGF1 and TGM2 mRNAs and significant down-regulation of NT5DC2 in the mouse models of scleroderma. Immune cell infiltration analysis showed that the differential genes in scleroderma were associated with follicular helper T cells, immature B cells, resting dendritic cells, memory activated CD4⁺T cells, M0 macrophages, monocytes, resting memory CD4⁺T cells and mast cell activation. Conclusion The artificial neural network diagnostic model for scleroderma established in this study provides a new perspective for exploring the pathogenesis of scleroderma.

Key words: scleroderma, mitochondria, artificial neural network, immune cell infiltration, machine learning

Zhiwei ZUO, Qingliang MENG, Jiakang CUI, Kelei GUO, Hua BIAN. An artificial neural network diagnostic model for scleroderma and immune cell infiltration analysis based on mitochondria-associated genes[J]. Journal of Southern Medical University, 2024, 44(5): 920-929.

Figures/Tables 12

Fig.1 Flow chart of the study design.

Tab.1 GEO database chip data set

DATA	Sample size	Normal sample	SSc sample	Organization type	Data type
GSE95065	33	15	18	Homo sapiens	Expression profiling by array
GSE59785	82	2	80	Homo sapiens	Expression profiling by array
GSE76807	15	5	10	Homo sapiens	Expression profiling by array

Tab.2 Primer sequence of the target genes

Primer	Sequence 5'-3'
POLB	F: CTTCACTGGGAGTGACATCTTT R: CAGCGACTCCAGTGACC
GSR	F: GAGCTCCAAGTGGTGACTTC R: CAGGCCCTTAGAATTTGGGT
KRAS	F: GTGGATGAGTATGACCCTACG R GACCTGCTGTGTCGAGAATATC
NT5DC2	F: ACGTCGTCATCGTCCAG R: TCTCTAGGCGAGTGATACGG
NOX4	F: AGACTCTACACATCACATGTGG R: AAAGTTGAGGGCATTCACCA
IGF1	F: CCCACTGAAGCCTACAAA R: TTTCTTGTTTGTCGATAGGGA
TGM2	F: TGTCTGACAATGTGGAGGAG R: GCTGTAGCGAGAGGACATT
β-actin	F: TGCTGTCCCTGTATGCCTCTG R: TGATGTCACGCACGATTTCC

Fig.2 Analysis of the differential expressions of the differential mitochondria-related gene in scleroderma. A: DEGs heatmap (red for up-regulated and blue for down-regulated genes). B: DEGs volcano map (red for up-regulated and blue for down-regulated genes).

Fig.3 Metascape analysis of the DEGs in scleroderma. A: DEG enrichment pathway and process network. B: Histogram of DEGs enrichment pathways and processes.

Fig.4 GO and KEGG enrichment analysis of DEGs in scleroderma. A: GO bar chart. B: GO circle. C: KEGG bubble diagram. D: KEGG cluster graph.

Fig.5 Selection of the key genes using 3 machine learning algorithms. A: Correlation between the number of random forest trees and the model error. B: Result of Gini coefficient method in random forest classifier. C: Characteristic genes selected by LASSO regression algorithm. D: Feature genes screened by SVM algorithm. E: Venn diagram of the intersected genes of the 3 algorithms.

Fig.6 10-fold cross-validation result.

Fig.7 Construction and verification of artificial neural network model. A: ANN result visualization. B: ROC curves of mitochondria-associated genes in the training dataset. C: ROC curves for mitochondria-related genes in the verification dataset.

Fig.8 Relative mRNA expressions of the key genes in mouse models of scleroderma. *P<0.05, **P<0.01, ***P<0.001.

Fig.9 Correlation analysis of immune cell expression. Red indicates a positive correlation and blue indicates a negative correlation.

Fig.10 Correlation of the genetic biomarkers with the infiltrating immune cells. A: GSR. B: IGF1. C: KRAS. D: NOX4. E: NT5DC2. F: POLB. G: TGM2. P<0.05 indicates a significant correlation between immune cells and genes.

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