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This study aimed to investigate the molecular mechanism under the pathophysiology of subarachnoid hemorrhage (SAH) and identify the potential biomarkers for predicting the risk of SAH. Differentially expressed mRNAs (DEGs), microRNAs, and lncRNAs were screened. Protein–protein interaction (PPI), drug–gene, and competing endogenous RNA (ceRNA) networks were constructed to determine candidate RNAs. The optimized RNAs signature was established using least absolute shrinkage and selection operator and recursive feature elimination algorithms. A total of 124 SAH-related DEGs were identified, and were enriched in inflammatory response, TNF signaling pathway, and others. PPI network revealed 118 hub genes such as  TNF ,  MMP9 , and  TLR4 . Drug–gene network revealed that chrysin targeted more genes, such as  TNF  and  MMP9 .  JMJD1C - AS - hsa - miR - 204 - HDAC4 / SIRT1  and  LINC01144 - hsa - miR - 128 - ADRB2 / TGFBR3  regulatory axes were found from ceRNA network. From these networks, 125 candidate RNAs were obtained. Of which, an optimal 38 RNAs signatures (2 lncRNAs, 1 miRNA, and 35 genes) were identified to construct a Support Vector Machine classifier. The predictive value of 38 biomarkers had an AUC of 0.990. Similar predictive performance was found in external validation dataset (AUC of 0.845). Our findings provided the potential for 38 RNAs to serve as biomarkers for predicting the risk of SAH. However, their application values should be further validated in clinical.

Investigation of molecular regulation mechanism under the pathophysiology of subarachnoid hemorrhage