LILRA1

When triple-negative breast cancer (TNBC) patients have residual disease after neoadjuvant chemotherapy (NACT), they have a high risk of metastatic relapse. With immune infiltrate in TNBC being prognostic and predictive of response to treatment, our aim was to develop an immunologic transcriptomic signature using post-NACT samples to predict relapse.

We identified 115 samples of residual tumors from post-NACT TNBC patients. We profiled the expression of 770 genes related to cancer microenvironment using the NanoString PanCancer IO360 panel to develop a prognostic transcriptomic signature, and we describe the immune microenvironments of the residual tumors.

Thirty-eight (33%) patients experienced metastatic relapse. Hierarchical clustering separated patients into five clusters with distinct prognosis based on pathways linked to immune activation, epithelial-to-mesenchymal transition and cell cycle. The immune microenvironment of the residual disease was significantly different between patients who experienced relapse compared to those who did not, the latter having significantly more effector antitumoral immune cells, with significant differences in lymphoid subpopulations. We selected eight genes linked to immunity (BLK, GZMM, CXCR6, LILRA1, SPIB, CCL4, CXCR4, SLAMF7) to develop a transcriptomic signature which could predict relapse in our cohort. This signature was validated in two external cohorts (KMplot and METABRIC).

Lack of immune activation after NACT is associated with a high risk of distant relapse. We propose a prognostic signature based on immune infiltrate that could lead to targeted therapeutic strategies to improve patient prognosis.

Although many pathological mechanisms and etiological hypotheses of acute unilateral vestibulopathy (AUVP) have been reported, but the actual etiology remains to be elucidated.

This study was based on comprehensive bioinformatics to identify the critical genes of AUVP and explore its pathological mechanism.

Gene expression profiles of AUVP and normal samples were collected from GSE146230 datasets of the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was constructed, and the WGCNA R-package extracted significant modules. The limma R-package was applied to identify differentially expressed genes (DEGs). The common genes of practical modules and DEGs were screened for GO and KEGG pathways analysis. The protein-protein interaction (PPI) layout and hub genes validation was created by Cytoscape software using the link from the STRING database. The functions of hub genes were predicted through the CTD (comparative genetics database).

A total of 332 common genes were screened from practical modules and DEGs. Functional enrichment analysis revealed that these genes were predominantly associated with inflammation and infection. After construction of PPI, expressions of hub genes, and drawing ROC curves, LILRB2, FPR1, AQP9, and LILRA1 are highly expressed in AUVP (p < 0.05) and have a certain diagnostic efficacy for AUVP (AUC > 0.7), so they were selected as hub genes. The functions of hub genes suggested that the occurrence of AUVP may be related to inflammation, necrosis, hepatomegaly, and other conditions in CTD.

LILRB2, FPR1, AQP9, and LILRA1 may play essential roles in developing AUVP, providing new ideas for diagnosing and treating AUVP.

Genetic variations associated with major depressive disorder (MDD) may affect the structural aspects of neural networks mediated by the molecular pathways involved in neuronal survival and synaptic plasticity. However, few studies have applied a novel approach such as whole-exome sequencing (WES) analysis to investigate the genetic contribution to the neurostructural changes in MDD.

In the first part of the study, we investigated rare variants of selected genes from previous WES studies using a WES analysis in 184 patients with MDD and 82 healthy controls. In the second part of the study, we explored the association between the common genetic variants from the WES analysis and cortical thickness in 91 patients with MDD and 75 healthy controls. The gray-matter thickness of each cortical region was measured using FreeSurfer.

We identified recurrent non-silent variants in 24 MDD-related genes including FASN, MYH13, UNC13D, LILRA1, CACNA1B, TRIO, HOMER3, and BCAR3, and observed eleven recurrently altered copy number alternations where a gain on 15q11.2 and losses on 7q34 and 15q11.1-q11.2 in MDD genomes. We also found that rs11592462 in CDH23, a calcium-dependent cell-adhesion molecule encoding gene, was significantly associated with thinning in the right anterior cingulate cortex.

The small sample size may lead our findings to be underpowered regarding rare variants.

The present study identified that non-synonymous rare variants were significantly associated with risk of MDD and found that genetic contributions to the development of MDD may be mediated by alterations in cortical thickness of emotion-processing neural circuits.