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MiR-30a-5p mediates epileptogenesis in epilepsy models by targeting SOX4 to regulate the Wnt/β-catenin pathway.

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  1. [1] § RESULTS › Bioinformatics prediction indicates that miR‐30a‐5p participates in the pathogenesis of epilepsy by regulating the Wnt/β‐catenin signaling pathway through targeting SOX4 ↔ EPI4-11-983-s003.r, lines 207–255 · score 0.57 · Cellular components, Molecular functions, Biological processes, pathway, KEGG

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The authors' code

R · 255 lines · 10 KB · no license · 1 match

  1. # 安装必要的包（如果未安装）
  2. if (!require("BiocManager", quietly = TRUE))
  3. install.packages("BiocManager")
  5. required_packages <- c("clusterProfiler", "org.Hs.eg.db", "enrichplot",
  6. "ggplot2", "DOSE", "stringr", "dplyr", "tidyr")
  7. for (pkg in required_packages) {
  8. if (!require(pkg, character.only = TRUE)) {
  9. BiocManager::install(pkg)
  10. }
  11. library(pkg, character.only = TRUE)
  12. }
  14. # 读取基因列表（从你的数据）
  15. # 假设你的基因列表在一个名为"genes.txt"的文件中，每行一个基因
  16. # 或者你可以直接使用向量
  18. # 这里我创建了基因列表向量（从你提供的数据）
  19. gene_list <- c(
  20. "TWF1", "KLHL20", "PPARGC1B", "MKRN3", "B3GNT5", "RORA", "FZD3", "BRWD3", "CELSR3",
  21. "XPO1", "PDE7A", "NT5E", "EED", "RFX6", "RFX7", "KLHL28", "TNRC6A", "SCN9A", "CTNNB1",
  22. "MTDH", "NEDD4", "CHD1", "LMBR1", "PLAGL2", "FRZB", "SETD5", "SOX9", "EML1", "REEP3",
  23. "RUNX1", "FOXG1", "STK39", "VIM", "KLF10", "NFIB", "SPEN", "SH2B3", "NECAP1", "STIM2",
  24. "ITGA6", "RUNX2", "OTUD6B", "SAMD8", "SOCS1", "GOLGA1", "AZIN1", "CNKSR2", "CFL2",
  25. "PFN2", "COL13A1", "SOCS3", "ANKRD17", "EEA1", "CLOCK", "TNRC6B", "SEC23A", "TMOD2",
  26. "RAB8A", "WNT3A", "PSMD7", "BNIP3L", "SH3RF1", "RAPGEF4", "RAP1B", "CAMK2D", "LRRC8C",
  27. "KMT2C", "PAWR", "PGM1", "LPP", "TAOK1", "SCN1A", "PRLR", "ERLIN1", "SACS", "OXR1",
  28. "OSBPL8", "VPS26B", "PICALM", "ZNF711", "PPARGC1A", "MCF2L", "RASA1", "CAND1", "GATM",
  29. "PER2", "SIX4", "LRRC8D", "ARHGEF6", "MYO5A", "CALU", "PRKAA2", "DNMT3A", "EDNRA",
  30. "TENM3", "CBX2", "STX16", "ACTR1A", "NRIP1", "RAB23", "PDE4D", "ABL1", "LOX", "EPG5",
  31. "VAT1", "NDEL1", "ZDHHC17", "RHOB", "DLGAP1", "MAP3K5", "OSTM1", "SLC25A36", "PPP3CA",
  32. "FLVCR1", "CHD7", "EDEM3", "TMEM87A", "SNX10", "ELAVL2", "HOXA1", "NAA25", "PPP1R12A",
  33. "GJA1", "BCL9", "IDH1", "KMT2A", "PIP4K2B", "HIPK2", "MAML1", "ACVR1", "BCOR", "DDIT4",
  34. "ARID1A", "SLC38A2", "GLI2", "GALNT2", "DOCK7", "ATXN1", "ATP2A2", "SNAI1", "CSNK1G1",
  35. "NF1", "DSTYK", "CADPS", "ELOVL5", "GOLGA4", "IRF4", "NR4A2", "JOSD1", "NLGN1", "EFNA3",
  36. "CHMP2B", "PGM3", "IFNAR2", "RNF220", "SLC9A8", "PGP", "ST8SIA4", "IRS1", "ITGB3",
  37. "LRP6", "ZBTB18", "REEP1", "EPB41", "LIFR", "DNAJC13", "PDCD10", "INPP4A", "AP4E1",
  38. "MIA3", "EDC3", "DPYSL2", "UBE3C", "GCLC", "PI4K2B", "SLC4A7", "NUS1", "CAMK4", "PDGFRB",
  39. "RAPGEF2", "RTN4R", "TBC1D2B", "STAG2", "MAPK8", "SP4", "MAP3K7", "TRIO", "FAM13A",
  40. "ATP2B1", "ATL2", "GRB10", "PRPF40A", "CRKL", "IGF2R", "STT3B", "SLC5A3", "ZEB2",
  41. "PIGA", "TMCC1", "EPC2", "SLC6A6", "FIGN", "KCTD5", "TRAF3", "SBF1", "CADM2", "RASGEF1B",
  42. "NR3C1", "NEDD4L", "NAPG", "B4GALT6", "KRAS", "PAX9", "TNPO3", "RPS6KA2", "USP45",
  43. "LEPR", "SIK3", "AGO1", "ASCC3", "SLC7A6", "HNRNPC", "ATP2B2", "GCNT2", "QKI", "ITSN1",
  44. "MTTP", "CLCF1", "UGT8", "GALNT3", "AP3S1", "FXR1", "PCDH10", "HNRNPA2B1", "USP2",
  45. "MBNL1", "SALL4", "CPEB4", "LRCH2", "KCTD20", "BTBD10", "SLC6A9", "PHF6", "PLXNA2",
  46. "FAM217B", "TET1", "WDR26", "PPFIA2", "AFF3", "HNRNPA3", "IRS2", "MBNL2", "SETD3",
  47. "SLC7A11", "PPIP5K2", "SOS1", "WDFY3", "SEC23IP", "KCTD3", "SLC35A5", "TRPS1", "FYCO1",
  48. "TNKS", "NFIA", "ARF4", "SNAPIN", "LCOR", "LARP4", "UBE2D3", "HERC2", "JARID2", "ALPK3",
  49. "ZNF746", "GLUD1", "ZFYVE26", "NID1", "PBRM1", "PLXNA1", "SLC38A1", "SATB1", "DGKZ",
  50. "USP22", "UNKL", "PGM2L1", "CEP170B", "SON", "ZNF148", "MAN1B1", "ZBTB7A", "SIRT1",
  51. "MTF2", "YES1", "NCOA3", "SOX4", "RAB10", "SLC29A3", "H6PD", "NOVA1", "RNMT", "ACTN1",
  52. "NSD1", "CCNY", "PITX1", "JAG2", "USP15", "BAZ1A", "AGO2", "ADAM10", "MINPP1", "MIB1",
  53. "TIMP2", "TXNDC5", "SNX27", "FUCA1", "CHKA", "FOXP4", "SUCLG2", "CHD9", "GNAQ", "TBL1X",
  54. "APC", "RAB11A", "TIA1", "EPB41L4B", "DOC2A", "FRMD4A", "MMP9", "Axin 2", "CTH", "MECP2",
  55. "SMARCD2", "Cyclin D1", "TUBGCP3", "Axin 1"
  56. )
  58. # 注意：基因符号需要标准化（去除空格等）
  59. gene_list <- gsub(" ", "", gene_list) # 去除空格
  60. gene_list <- unique(gene_list) # 去重
  62. cat("基因列表数量:", length(gene_list), "\n")
  64. # 将基因符号转换为ENTREZID
  65. gene_entrez <- bitr(gene_list,
  66. fromType = "SYMBOL",
  67. toType = "ENTREZID",
  68. OrgDb = org.Hs.eg.db)
  70. cat("成功转换的基因数量:", nrow(gene_entrez), "\n")
  72. if (nrow(gene_entrez) == 0) {
  73. stop("没有基因被成功转换，请检查基因符号是否正确")
  74. }
  76. # 1. GO富集分析
  77. go_bp <- enrichGO(gene = gene_entrez$ENTREZID,
  78. OrgDb = org.Hs.eg.db,
  79. keyType = "ENTREZID",
  80. ont = "BP", # BP:生物过程，MF:分子功能，CC:细胞组分
  81. pvalueCutoff = 0.05,
  82. qvalueCutoff = 0.2,
  83. readable = TRUE)
  85. go_mf <- enrichGO(gene = gene_entrez$ENTREZID,
  86. OrgDb = org.Hs.eg.db,
  87. keyType = "ENTREZID",
  88. ont = "MF",
  89. pvalueCutoff = 0.05,
  90. qvalueCutoff = 0.2,
  91. readable = TRUE)
  93. go_cc <- enrichGO(gene = gene_entrez$ENTREZID,
  94. OrgDb = org.Hs.eg.db,
  95. keyType = "ENTREZID",
  96. ont = "CC",
  97. pvalueCutoff = 0.05,
  98. qvalueCutoff = 0.2,
  99. readable = TRUE)
  101. # 2. KEGG富集分析
  102. kegg <- enrichKEGG(gene = gene_entrez$ENTREZID,
  103. organism = 'hsa', # 人类
  104. pvalueCutoff = 0.05,
  105. qvalueCutoff = 0.2)
  107. # 将KEGG结果转换为可读的基因符号
  108. if (!is.null(kegg)) {
  109. kegg <- setReadable(kegg, OrgDb = org.Hs.eg.db, keyType = "ENTREZID")
  110. }
  112. # 保存结果到文件
  113. output_dir <- "enrichment_results"
  114. if (!dir.exists(output_dir)) {
  115. dir.create(output_dir)
  116. }
  118. # 保存GO结果
  119. if (nrow(go_bp) > 0) {
  120. write.csv(go_bp, file.path(output_dir, "GO_BP_enrichment.csv"), row.names = FALSE)
  121. cat("GO BP结果已保存到", file.path(output_dir, "GO_BP_enrichment.csv"), "\n")
  122. }
  124. if (nrow(go_mf) > 0) {
  125. write.csv(go_mf, file.path(output_dir, "GO_MF_enrichment.csv"), row.names = FALSE)
  126. cat("GO MF结果已保存到", file.path(output_dir, "GO_MF_enrichment.csv"), "\n")
  127. }
  129. if (nrow(go_cc) > 0) {
  130. write.csv(go_cc, file.path(output_dir, "GO_CC_enrichment.csv"), row.names = FALSE)
  131. cat("GO CC结果已保存到", file.path(output_dir, "GO_CC_enrichment.csv"), "\n")
  132. }
  134. # 保存KEGG结果
  135. if (!is.null(kegg) && nrow(kegg) > 0) {
  136. write.csv(kegg, file.path(output_dir, "KEGG_enrichment.csv"), row.names = FALSE)
  137. cat("KEGG结果已保存到", file.path(output_dir, "KEGG_enrichment.csv"), "\n")
  138. }
  140. # 3. 可视化
  141. # 创建可视化目录
  142. vis_dir <- file.path(output_dir, "visualization")
  143. if (!dir.exists(vis_dir)) {
  144. dir.create(vis_dir)
  145. }
  147. # GO富集条形图（BP为例）
  148. if (nrow(go_bp) > 0) {
  149. pdf(file.path(vis_dir, "GO_BP_barplot.pdf"), width = 12, height = 8)
  150. print(barplot(go_bp, showCategory = 20, title = "GO Biological Process Enrichment"))
  151. dev.off()
  153. # 点图
  154. pdf(file.path(vis_dir, "GO_BP_dotplot.pdf"), width = 12, height = 8)
  155. print(dotplot(go_bp, showCategory = 20, title = "GO Biological Process Enrichment"))
  156. dev.off()
  158. # 网络图（显示基因与GO term的关系）
  159. if (nrow(go_bp) >= 5) {
  160. pdf(file.path(vis_dir, "GO_BP_cnetplot.pdf"), width = 14, height = 10)
  161. print(cnetplot(go_bp, categorySize = "pvalue", foldChange = NULL,
  162. showCategory = 10, node_label = "all"))
  163. dev.off()
  164. }
  165. }
  167. # KEGG可视化
  168. if (!is.null(kegg) && nrow(kegg) > 0) {
  169. pdf(file.path(vis_dir, "KEGG_barplot.pdf"), width = 12, height = 8)
  170. print(barplot(kegg, showCategory = 20, title = "KEGG Pathway Enrichment"))
  171. dev.off()
  173. pdf(file.path(vis_dir, "KEGG_dotplot.pdf"), width = 12, height = 8)
  174. print(dotplot(kegg, showCategory = 20, title = "KEGG Pathway Enrichment"))
  175. dev.off()
  177. # KEGG通路图（需要安装pathview）
  178. tryCatch({
  179. if (!require("pathview", quietly = TRUE)) {
  180. BiocManager::install("pathview")
  181. }
  182. library(pathview)
  184. # 为每个显著富集的KEGG通路生成通路图
  185. sig_kegg_ids <- kegg@result$ID[1:min(5, nrow(kegg))]
  186. for (pid in sig_kegg_ids) {
  187. tryCatch({
  188. pathview(gene.data = gene_entrez$ENTREZID,
  189. pathway.id = pid,
  190. species = "hsa",
  191. limit = list(gene = 5, cpd = 1))
  192. }, error = function(e) {
  193. cat("无法生成通路图", pid, ":", e$message, "\n")
  194. })
  195. }
  196. # 移动生成的PNG文件到可视化目录
  197. png_files <- list.files(pattern = "hsa.*\\.png$")
  198. if (length(png_files) > 0) {
  199. file.copy(png_files, vis_dir)
  200. file.remove(png_files)
  201. }
  202. }, error = function(e) {
  203. cat("路径可视化失败:", e$message, "\n")
  204. })
  205. }
  207. # 4. 结果汇总
  208. cat("\n========== 富集分析结果汇总 ==========\n")
  209. cat("输入基因总数:", length(gene_list), "\n")
  210. cat("成功转换的基因数:", nrow(gene_entrez), "\n")
  212. cat("\nGO富集结果:\n")
  213. cat(" Biological Process:", ifelse(nrow(go_bp) > 0, paste(nrow(go_bp), "个显著条目"), "无显著条目"), "\n")
  214. cat(" Molecular Function:", ifelse(nrow(go_mf) > 0, paste(nrow(go_mf), "个显著条目"), "无显著条目"), "\n")
  215. cat(" Cellular Component:", ifelse(nrow(go_cc) > 0, paste(nrow(go_cc), "个显著条目"), "无显著条目"), "\n")
  217. cat("\nKEGG富集结果:\n")
  218. cat(" 显著通路:", ifelse(!is.null(kegg) && nrow(kegg) > 0, paste(nrow(kegg), "个"), "无"), "\n")
  220. cat("\n结果文件保存位置:\n")
  221. cat(" 数据文件:", output_dir, "\n")
  222. cat(" 可视化文件:", vis_dir, "\n")
  224. # 5. 显示部分结果
  225. if (nrow(go_bp) > 0) {
  226. cat("\nTop 10 GO Biological Process:\n")
  227. print(head(go_bp@result[, c("Description", "pvalue", "qvalue", "Count")], 10))
  228. }
  230. if (!is.null(kegg) && nrow(kegg) > 0) {
  231. cat("\nTop 10 KEGG Pathways:\n")
  232. print(head(kegg@result[, c("Description", "pvalue", "qvalue", "Count")], 10))
  233. }
  235. # 6. 可选：生成HTML报告
  236. tryCatch({
  237. if (!require("rmarkdown", quietly = TRUE)) {
  238. install.packages("rmarkdown")
  239. }
  241. # 创建简化的报告
  242. report_file <- file.path(output_dir, "enrichment_report.Rmd")
  244. report_content <- '---
  245. title: "基因富集分析报告"
  246. output: html_document
  247. date: "`r Sys.Date()`"
  248. ---
  250. ```{r setup, include=FALSE}
  251. knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)
  252. library(clusterProfiler)
  253. library(org.Hs.eg.db)
  254. library(enrichplot)
  255. library(ggplot2)

EPI4-11-983-s003.r, no license · at the source

Overview

Authors: Zhenlin Yang1, Xu Zhang1, Jingjing Guo1, Yuanxin Wei1, Jinzi Li1
ORCID iDs: Jinzi Li
  1. Department of Pediatrics, Yanbian University Hospital, Yanji, China
Institutions: Yanbian University Hospital (China)
Journal: Epilepsia open, volume 11, issue 3, pages 983-1002
Dates: received 14 January 2026; accepted 1 April 2026; published online 24 April 2026; in print June 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1002/epi4.70270 · PMID 42029101 · PMCID PMC13238734 · OpenAlex W7155559619
Open access: gold, a free copy (OpenAlex)
Status: code verified
Categories: histology / microscopy (modality), rat (organism), epilepsy (population), cellular / molecular (subfield)
Methods: Statistics
Keywords: epilepsy, miR‐30a‐5p, SOX4, synaptic plasticity, Wnt/β‐catenin pathway
MeSH: Epilepsy*, Hippocampus*, MicroRNAs*, SOXC Transcription Factors*, Wnt Signaling Pathway*, Animals, beta Catenin, Disease Models, Animal, Male, Neuronal Plasticity, Neurons, Rats, Rats, Sprague-Dawley (* major topic)
Topic: MicroRNA in disease regulation (Cancer Research, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: Jilin Health Science and Technology Capacity Enhancement Program (2023JC022); Scientific Research Program of Jilin Provincial Department of Education (JJKH20240693KJ)
Citations: not cited yet (Europe PMC); 46 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

Its files are read in the Code ↔ Paper reader above, with 1 match between paragraphs and lines of code.

supp:PMC13238734/EPI4-11-983-s003.r

License: none: the authors keep all their rights
State: the link answers, verified on 30 September 2026
Evidence: files inventoried
Languages: R (1)
Size: 1 file, 1 script
Software Heritage: not checked
Found in: the supplementary material
Not found: README, license file, CITATION.cff, environment file, tests, continuous integration, documentation
Tools: clusterProfiler (1 file), ggplot2 (1 file), tidyverse (1 file)
Availability: 1 check, the latest on 30 September 2026: the link answers (HTTP 200)
  • 30 September 2026: the link answers (HTTP 200)
1 file
At the source: pmc-oa-opendata.s3.amazonaws.com/PMC13238734.1/EPI4-11-983-s003.r

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 1 script, each with its path and the digest of its content;
  • 1 match between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

No dataset and no data link were found in the paper.

Data availability statement

The paper has a data availability statement. Its license (CC BY-NC-ND) does not allow reproducing it here; in short, from what the harvester recognized in it:

  • it says that the data are available on request

Read it in the paper: doi.org/10.1002/epi4.70270.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 1, 30 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 5 authors, 5 keywords, 13 MeSH terms, 2 funders, 45 references.

Cite

This paper

Yang, Z., Zhang, X., Guo, J., Wei, Y., & Li, J. (2026). MiR-30a-5p mediates epileptogenesis in epilepsy models by targeting SOX4 to regulate the Wnt/β-catenin pathway. Epilepsia open, 11(3), 983-1002. https://doi.org/10.1002/epi4.70270

BibTeX

@article{yang2026mir,
author = {Yang, Zhenlin and Zhang, Xu and Guo, Jingjing and Wei, Yuanxin and Li, Jinzi},
title = {{MiR-30a-5p mediates epileptogenesis in epilepsy models by targeting SOX4 to regulate the Wnt/β-catenin pathway}},
journal = {Epilepsia open},
year = {2026},
month = apr,
volume = {11},
number = {3},
pages = {983--1002},
publisher = {Wiley},
issn = {2470-9239},
doi = {10.1002/epi4.70270},
url = {https://doi.org/10.1002/epi4.70270},
pmid = {42029101},
pmcid = {PMC13238734}
}

RIS

TY - JOUR
AU - Yang, Zhenlin
AU - Zhang, Xu
AU - Guo, Jingjing
AU - Wei, Yuanxin
AU - Li, Jinzi
TI - MiR-30a-5p mediates epileptogenesis in epilepsy models by targeting SOX4 to regulate the Wnt/β-catenin pathway
T2 - Epilepsia open
J2 - Epilepsia Open
PY - 2026
DA - 2026/04/24
VL - 11
IS - 3
SP - 983
EP - 1002
SN - 2470-9239
PB - Wiley
DO - 10.1002/epi4.70270
UR - https://doi.org/10.1002/epi4.70270
LA - en
ER -

CSL-JSON

{
"id": "10.1002/epi4.70270",
"type": "article-journal",
"title": "MiR-30a-5p mediates epileptogenesis in epilepsy models by targeting SOX4 to regulate the Wnt/β-catenin pathway",
"container-title": "Epilepsia open",
"author": [
{
"family": "Yang",
"given": "Zhenlin"
},
{
"family": "Zhang",
"given": "Xu"
},
{
"family": "Guo",
"given": "Jingjing"
},
{
"family": "Wei",
"given": "Yuanxin"
},
{
"family": "Li",
"given": "Jinzi"
}
],
"container-title-short": "Epilepsia Open",
"volume": "11",
"issue": "3",
"page": "983-1002",
"DOI": "10.1002/epi4.70270",
"PMID": "42029101",
"PMCID": "PMC13238734",
"ISSN": "2470-9239",
"publisher": "Wiley",
"URL": "https://doi.org/10.1002/epi4.70270",
"language": "en",
"issued": {
"date-parts": [
[
2026,
4,
24
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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[9] doi:10.1073/pnas.2609132123 [code]
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[10] doi:10.1186/s12967-026-08266-z [code]
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