新型细胞焦亡-免疫相关 lncRNA 特征在乳腺癌中展现出独特的免疫细胞浸润格局

Pearson's correlation coefficient was used to identify pyroptosis- and immune-related differentially expressed lncRNAs (DE-pyrolncRNAs and DE-ImmlncRNAs, respectively). The Cancer Genome Atlas dataset was allocated to training and testing subsets. Prognostic lncRNA signatures were derived based on the training subset using univariate Cox regression analysis and Least Absolute Shrinkage and Selection Operator methods. Stepwise Cox regression was used to refine these signatures and to select the optimal lncRNA signature. The median risk score of the training subset was applied as a threshold to divide patients into high-risk (HR) and low-risk (LR) groups. The Wilcoxon test was used to reveal differences in immune scores, cell types, functions, and checkpoint genes between these groups. Single-cell sequencing data from GSE176078 were used to validate the immune cell infiltration landscape of the identified lncRNA signatures.

We identified a six-lncRNA pyroptosis-immune signature comprising MAPT.AS1, CTA.384, D8.34, RP11.561, I11.3, HID1.AS1, AC097713.3, and USP2.AS1. Patients in the HR group demonstrated inferior prognoses in the training, testing, and full datasets (P=3.622e-07, P=3.736e-03, and P=1.151e-08, respectively). Immune scores were significantly enhanced in the LR group, whereas tumor purity was elevated in the HR group. Fifty-eight immune scores showed significant differences between the groups (P<0.05). Immune function (APC coinhibition, CCR, and checkpoints) more significantly impaired in the HR group. Expression levels of 38 immune checkpoint genes, including KIR2DS4, KIR3DL2, CD40LG, KIR3DL1, and PDCD1, were significantly higher in the LR group. Conversely, the TDO2, PVR, and CD276 levels were elevated in the HR group. Single-cell sequencing data from GSE176078 showed sparse T cell, B cell, myeloid, and plasmablast clusters in the HR group, whereas the LR group displayed significant clustering of B cells, myeloids, and plasmablasts.

The six-lncRNA pyroptosis-immune signature effectively predicted BC prognosis and highlighted distinct immune cell infiltration patterns. This holds promise for evaluating immunotherapy responses and guiding therapeutic target identification in BC.