Luo Tingting, Shen Shijie, Sun Yufei, El-Ashram Saeed, Zhang Xia, Liu Keyu, Cao Chengzhang, Alajmi Reem Atalla, Deng Siqi, Wu Jiangdong, Zhang Wanjiang, Zhang Hongying
Key Laboratory of Xinjiang Endicand Ethnic Diseases Cooperated By Education Ministry with Xinjiang Province, Shihezi University, Shihezi, China.
Zoology Department, Faculty of Science, Kafrelsheikh University, Kafr El-Sheikh, Egypt.
DNA Cell Biol. 2025 Feb;44(2):82-98. doi: 10.1089/dna.2024.0166. Epub 2024 Dec 2.
According to the World Health Organization, infections affect approximately 25% of the world's population. There is mounting evidence linking autophagy and immunological dysregulation to tuberculosis (TB). As a result, this research set out to discover TB-related autophagy-related biomarkers and prospective treatment targets. We used five autophagy databases to get genes linked to autophagy and Gene Expression Omnibus databases to get genes connected to TB. Then, functional modules associated with autophagy were obtained by analyzing them using weighted gene co-expression network analysis. Both Gene Ontology and Kyoto Encyclopedia of Genes and Genomes were used to examine the autophagy-related genes (ATGs) of important modules. Limma was used to identify differentially expressed ATGs (DE-ATGs), and the external datasets were used to further confirm their identification. We used DE-ATGs and a protein-protein interaction network to search the hub genes. CIBERSORT was used to estimate the kinds and amounts of immune cells. After that, we built a drug-gene interaction network and a network that included messenger RNA, small RNA, and DNA. At last, the differential expression of hub ATGs was confirmed by RT-qPCR, immunohistochemistry, and western blotting. The diagnostic usefulness of hub ATGs was evaluated using receiver operating characteristic curve analysis. Including 508 ATGs, four of the nine modules strongly linked with TB were deemed essential. Interleukin 1B (), , and signal transducer and activator of transcription 1 () were identified by intersection out of 22 DE-ATGs discovered by differential expression analysis. Research into immune cell infiltration found that patients with TB had an increased proportion of plasma cells, CD8 T cells, and M0 macrophages. A competitive endogenous RNA network utilized 10 long non-coding RNAs and 2 miRNAs. Then, the -targeted drug Cankinumad was assessed using this network. During bioinformatics analysis, three hub genes were validated in mouse and macrophage infection models. We found that , , and are important biomarkers for TB. As a result, these crucial hub genes may hold promise as TB treatment targets.
根据世界卫生组织的数据，感染影响着全球约25%的人口。越来越多的证据表明自噬和免疫失调与结核病（TB）有关。因此，本研究旨在发现与结核病相关的自噬相关生物标志物和潜在治疗靶点。我们使用了五个自噬数据库来获取与自噬相关的基因，并使用基因表达综合数据库来获取与结核病相关的基因。然后，通过加权基因共表达网络分析对它们进行分析，获得与自噬相关的功能模块。基因本体论和京都基因与基因组百科全书都用于检查重要模块的自噬相关基因（ATG）。Limma用于识别差异表达的ATG（DE-ATG），并使用外部数据集进一步确认它们的识别。我们使用DE-ATG和蛋白质-蛋白质相互作用网络来搜索枢纽基因。CIBERSORT用于估计免疫细胞的种类和数量。之后，我们构建了药物-基因相互作用网络以及一个包含信使RNA、小RNA和DNA的网络。最后，通过RT-qPCR、免疫组织化学和蛋白质印迹法确认了枢纽ATG的差异表达。使用受试者工作特征曲线分析评估了枢纽ATG的诊断效用。包括508个ATG，与结核病密切相关的九个模块中有四个被认为是必不可少的。通过差异表达分析发现的22个DE-ATG中，通过交集鉴定出白细胞介素1B（）、 和信号转导子和转录激活子1（）。对免疫细胞浸润的研究发现，结核病患者的浆细胞、CD8 T细胞和M0巨噬细胞比例增加。一个竞争性内源性RNA网络利用了10个长链非编码RNA和2个微小RNA。然后，使用该网络评估了靶向 的药物坎地努单抗。在生物信息学分析过程中，在小鼠和巨噬细胞感染模型中验证了三个枢纽基因。我们发现 、 和 是结核病的重要生物标志物。因此，这些关键的枢纽基因有望成为结核病的治疗靶点。
