Around the world, tuberculosis (TB) remains one of the most common causes of morbidity and mortality. The molecular mechanism ofMycobacterium tuberculosis(Mtb) infection is still unclear. Extracellular vesicles (EVs) play a key role in the onset and progression of many disease states and can serve as effective biomarkers or therapeutic targets for the identification and treatment of TB patients. We analysed the expression profile to better clarify the EVs characteristics of TB and explored potential diagnostic markers to distinguish TB from healthy control (HC). Twenty EVs‐related differentially expressed genes (DEGs) were identified, and 17 EVs‐related DEGs were up‐regulated and three DEGs were down‐regulated in TB samples, which were related to immune cells. Using machine learning, a nine EVs‐related gene signature was identified and two EVs‐related subclusters were defined. The single‐cell RNA sequence (scRNA‐seq) analysis further confirmed that these hub genes might play important roles in TB pathogenesis. The nine EVs‐related hub genes had excellent diagnostic values and accurately estimated TB progression. TB's high‐risk group had significantly enriched immune‐related pathways, and there were substantial variations in immunity across different groups. Furthermore, five potential drugs were predicted for TB using CMap database. Based on the EVs‐related gene signature, the TB risk model was established through a comprehensive analysis of different EV patterns, which can accurately predict TB. These genes could be used as novel biomarkers to distinguish TB from HC. These findings lay the foundation for further research and design of new therapeutic interventions aimed at treating this deadly infectious disease.

Remarkable clinical benefits in several advanced cancers are observed under the treatment of immune checkpoint inhibitor (ICI) agents. However, only a smaller proportion of patients respond to the treatments. Reelin (RELN) is frequently mutated in the cancer genome. In this study, the RELN mutation association with ICI treatment efficacy in melanoma and non-small cell lung cancer (NSCLC) was elucidated. Data from 631 melanoma and 109 NSCLC patients with both ICI treatment data and pre-treatment mutational profiles were collected. In addition, from the Cancer Genome Atlas (TCGA) project, we also obtained both tumors to explore the immunologic features behind RELN mutations. Melanoma patients with RELN mutations exhibited a favorable ICI survival benefit when compared with wild-type patients (HR: 0.66, 95% CI: 0.51–0.87, p = 0.003). A higher response rate was also noticed in RELN-mutated patients (38.9% vs. 28.3%, p = 0.017). The association of RELN mutations with a preferable immunotherapy outcome and response was further confirmed in NSCLC. Further exploration demonstrated that favorable immunocyte infiltration and immune response signaling pathways were found in patients with RELN mutations. In this study, RELN mutations were identified to connect with a better immune microenvironment and an improved ICI efficacy in melanoma and NSCLC, which provides a potential biomarker for immunological feature evaluation and immunotherapeutic outcome prediction at the molecular level.

Background: Tuberculosis (TB) is the deadliest communicable disease in the world with the exception of the ongoing COVID-19 pandemic. Programmed cell death (PCD) patterns play key roles in the development and progression of many disease states such that they may offer value as effective biomarkers or therapeutic targets that can aid in identifying and treating TB patients. Materials and methods: The Gene Expression Omnibus (GEO) was used to gather TB-related datasets after which immune cell profiles in these data were analyzed to examine the potential TB-related loss of immune homeostasis. Profiling of differentially expressed PCD-related genes was performed, after which candidate hub PCD-associated genes were selected via a machine learning approach. TB patients were then stratified into two subsets based on the expression of PCD-related genes via consensus clustering. The potential roles of these PCD-associated genes in other TB-related diseases were further examined. Results: In total, 14 PCD-related differentially expressed genes (DEGs) were identified and highly expressed in TB patient samples and significantly correlated with the abundance of many immune cell types. Machine learning algorithms enabled the selection of seven hub PCD-related genes that were used to establish PCD-associated patient subgroups, followed by the validation of these subgroups in independent datasets. These findings, together with GSVA results, indicated that immune-related pathways were significantly enriched in TB patients exhibiting high levels of PCD-related gene expression, whereas metabolic pathways were significantly enriched in the other patient group. Single cell RNA-seq (scRNA-seq) further highlighted significant differences in the immune status of these different TB patient samples. Furthermore, we used CMap to predict five potential drugs for TB-related diseases. Conclusion: These results highlight clear enrichment of PCD-related gene expression in TB patients and suggest that this PCD activity is closely associated with immune cell abundance. This thus indicates that PCD may play a role in TB progression through the induction or dysregulation of an immune response. These findings provide a foundation for further research aimed at clarifying the molecular drivers of TB, the selection of appropriate diagnostic biomarkers, and the design of novel therapeutic interventions aimed at treating this deadly infectious disease.

Background: Mycobacterium tuberculosis (Mtb) is the bacterial pathogen responsible for causing tuberculosis (TB), a severe public health concern that results in numerous deaths worldwide. Ubiquitination (Ub) is an essential physiological process that aids in maintaining homeostasis and contributes to the development of TB. Therefore, the main objective of our study was to investigate the potential role of Ub-related genes in TB. Methods: Our research entailed utilizing single sample gene set enrichment analysis (ssGSEA) in combination with several machine learning techniques to discern the Ub-related signature of TB and identify potential diagnostic markers that distinguish TB from healthy controls (HC). Results: In summary, we used the ssGSEA algorithm to determine the score of Ub families (E1, E2, E3, DUB, UBD, and ULD). Notably, the score of E1, E3, and UBD were lower in TB patients than in HC individuals, and we identified 96 Ub-related differentially expressed genes (UbDEGs). Employing machine learning algorithms, we identified 11 Ub-related hub genes and defined two distinct Ub-related subclusters. Notably, through GSVA and functional analysis, it was determined that these subclusters were implicated in numerous immune-related processes. We further investigated these Ub-related hub genes in four TB-related diseases and found that TRIM68 exhibited higher correlations with various immune cells in different conditions, indicating that it may play a crucial role in the immune process of these diseases. Conclusion: The observed enrichment of Ub-related gene expression in TB patients emphasizes the potential involvement of ubiquitination in the progression of TB. These significant findings establish a basis for future investigations to elucidate the molecular mechanisms associated with TB, select suitable diagnostic biomarkers, and design innovative therapeutic interventions for combating this fatal infectious disease.

Incorrect file published File Table S2 was erroneously published with the original version of this paper. The file has now been replaced. The original version of this article has been updated.In the published article, there was a mistake in the Materials and Methods section, subsection "Quantitative reverse transcription polymerase chain reaction", paragraph 2 (starting with "Samples were collected from each participant...").The original text incorrectly stated that "U6 served as an internal control." This was a clerical error during manuscript preparation. In fact, all qPCR experiments were performed using GAPDH as the internal control, and the quantitative data were properly normalized to GAPDH. The authors sincerely apologize for this mistake. We confirm that all experimental procedures, data analyses, and research conclusions relied on GAPDH normalization from the very beginning of the study. Consequently, this textual correction has no bearing whatsoever on any results or conclusions presented in the article.A correction has been made to the section Materials and methods, Quantitative reverse transcription polymerase chain reaction, Paragraph 2: "GAPDH served as an internal control" The original version of this article has been updated.