Here, we processed a total of 48 key DEGs with preferable diagnostic value, compared seven machine learning algorithms, and eventually applied the GBDT machine learning algorithm to build a diagnostic model with three genes, PBRM1, CA1 and TXLNG, that had a significant differential expression between PAH and control samples and were finally regarded as molecular biomarkers of Group I PAH. This evidence concerns the gene TXLNG and pulmonary arterial hypertension.