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ORIGINAL RESEARCH article

Front. Med.

Sec. Precision Medicine

Volume 12 - 2025 | doi: 10.3389/fmed.2025.1599791

Analysis of Gene Polymorphisms in Patients with Pulmonary Infections Based on Next-Generation Sequencing Technology and Their Prognostic Predictive Value

Provisionally accepted
Rui  ZengRui Zeng1Guang  WangGuang Wang1Feng  ZhouFeng Zhou1,2*
  • 1The Affiliated Hospital of Inner Mongolia Medical University, Hohhot, Inner Mongolia Autonomous Region, China
  • 2Department of Blood Transfusion, the Affiliated Hospital of Inner Mongolia Medical University, Inner Mongolia, China

The final, formatted version of the article will be published soon.

Pulmonary infections are a leading cause of morbidity and mortality worldwide, particularly among vulnerable populations such as children and the elderly. Gene polymorphisms play a critical role in disease susceptibility, progression, and prognosis, yet their specific contributions to pulmonary infections remain underexplored. This study utilized next-generation sequencing (NGS) technology to analyze gene polymorphisms in 200 patients with pulmonary infections and evaluate their prognostic value. Key findings include significant associations between polymorphisms in TLR4 (rs4986790), IL-10 (rs1800896), TNF-α (rs1800629), and MBL2 (rs1800450) with infection susceptibility, disease severity, and survival outcomes. Notably, carriers of the TLR4 rs4986790 mutation exhibited increased risk of severe infections and prolonged hospital stays, while IL-10 rs1800896 was linked to higher complication rates, particularly respiratory failure and sepsis. A polygenic risk score (PRS) revealed that high-risk patients had significantly elevated 30-day mortality and complication rates. These results highlight the potential of gene polymorphisms as prognostic biomarkers and personalized treatment targets. Future research should validate these findings in larger, diverse cohorts and explore functional mechanisms.

Keywords: pulmonary infections, gene polymorphisms, Next-generation sequencing, Prognosis prediction, personalized treatment

Received: 27 Mar 2025; Accepted: 06 Jun 2025.

Copyright: © 2025 Zeng, Wang and Zhou. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

* Correspondence: Feng Zhou, Department of Blood Transfusion, the Affiliated Hospital of Inner Mongolia Medical University, Inner Mongolia, China

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