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Purpose <p>Cutaneous leishmaniasis is a common neglected parasitic disease in developing countries that primarily causes skin lesions but may also be associated with systemic manifestations. In this study, bioinformatics approaches were used to analyze gene expression data from blood samples of patients with cutaneous leishmaniasis in order to identify key molecular markers and mechanisms involved in the development of systemic manifestation.</p> Methods <p>Gene expression datasets were retrieved from GEO database and examined to determine differentially expressed genes. Protein–protein interaction (PPI) network were constructed using the STRING and HIPPIE database, Cytoscape software, and the MCODE plugin. Gene expression regulatory network (TF–miRNA–gene) were generated using the miRTarBase, TRANSFAC, and TransmiR databases. Genes with high degree and betweenness centrality in both PPI and regulatory networks were considered key genes associated with systemic manifestations. Functional enrichment and molecular pathway analyses of critical genes were performed using the DAVID database.</p> Results <p>A total of 221 genes were identified as significantly differentially expressed, including 192 up-regulated and 29 down-regulated genes. Network analyses identified critical genes, including <i>ICAM1</i>,<i> CD274</i>,<i> BIRC3</i>,<i> EZH2</i>,<i> CTLA4</i>,<i> IFIH1</i>,<i> IER3</i>,<i> SAMD8</i>,<i> GLUL</i>, and <i>NAA50</i>. These genes were mainly involved in critical functional pathways such as apoptosis, autophagy, mitophagy, TNF signaling, immune regulation, cell adhesion, natural killer (NK) cell cytotoxicity, transcription factor activation, and Signaling by NTRKs.</p> Conclusion <p>This study enhances insights into the molecular mechanisms underlying the systemic effects of cutaneous leishmaniasis and highlights potential biomarkers and therapeutic targets that may aid in the development of targeted treatments.</p>

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Deciphering the Molecular Basis of Systemic Manifestations in Cutaneous Leishmaniasis Using Integrated Bioinformatics Analysis

  • 期刊论文

  • 发表日期：

    2026年6月1日

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  • Zeinab Dehghan,
  • Zeinab Zarei-Behjani,
  • Masoud Rezaei,
  • Hamidreza Arabi,
  • Monir Taherimoghadam,
  • Gholamreza Hatam

摘要

Purpose

Cutaneous leishmaniasis is a common neglected parasitic disease in developing countries that primarily causes skin lesions but may also be associated with systemic manifestations. In this study, bioinformatics approaches were used to analyze gene expression data from blood samples of patients with cutaneous leishmaniasis in order to identify key molecular markers and mechanisms involved in the development of systemic manifestation.

Methods

Gene expression datasets were retrieved from GEO database and examined to determine differentially expressed genes. Protein–protein interaction (PPI) network were constructed using the STRING and HIPPIE database, Cytoscape software, and the MCODE plugin. Gene expression regulatory network (TF–miRNA–gene) were generated using the miRTarBase, TRANSFAC, and TransmiR databases. Genes with high degree and betweenness centrality in both PPI and regulatory networks were considered key genes associated with systemic manifestations. Functional enrichment and molecular pathway analyses of critical genes were performed using the DAVID database.

Results

A total of 221 genes were identified as significantly differentially expressed, including 192 up-regulated and 29 down-regulated genes. Network analyses identified critical genes, including ICAM1, CD274, BIRC3, EZH2, CTLA4, IFIH1, IER3, SAMD8, GLUL, and NAA50. These genes were mainly involved in critical functional pathways such as apoptosis, autophagy, mitophagy, TNF signaling, immune regulation, cell adhesion, natural killer (NK) cell cytotoxicity, transcription factor activation, and Signaling by NTRKs.

Conclusion

This study enhances insights into the molecular mechanisms underlying the systemic effects of cutaneous leishmaniasis and highlights potential biomarkers and therapeutic targets that may aid in the development of targeted treatments.