Alterations in the immune landscape characterized by inflammatory activation and immune escape within 12 h after trauma.

BACKGROUND: Trauma is statistically a significant cause of mortality among patients across countries. Nevertheless, the precise correlation between genetic diagnostic markers and the intricate mechanism of trauma remains indistinct. METHODS: Our study exclusively centered on trauma patients and selected three trauma-related datasets from the Gene Expression Omnibus (GEO) database, all of which had blood samples collected within post-traumatic 12 h. Differential gene screening, the WGCNA and Cytoscape software were employed to analyze the two datasets, with a particular emphasis on the top 100 genes selected based on MCC algorithm scores. A logistic diagnostic model was constructed by analyzing the intersection genes in the third dataset, leading to the identification of diagnostic biomarkers with high efficiency. The global immune landscape of these patients was extensively investigated using a multidimensional approach. Meanwhile, the underlying pathological and physiological mechanisms associated with early trauma status are summarized by integrating existing literature. RESULTS: Out of these two GEO datasets, 21 overlapping genes were identified and incorporated into in the logistic diagnostic model constructed in the GSE36809 dataset. A panel of 9 genes was uncovered as a diagnostic biomarker, and their expression and correlation were subsequently verified. Additionally, by virtue of various algorithms, the findings revealed an upregulation of neutrophil expression and a downregulation of CD8+ T cell expression, indicating characteristic early trauma-induced inflammation activation and immune suppression. The correlation observed between the feature genes and immune cells serves to validate the exceptional diagnostic capability of these 9 genes in identifying trauma status and their promising potential for patients who could benefit from targeted immune interventions. Drawing from these findings, the discussion section offers insights into the underlying pathological and physiological mechanisms at play. CONCLUSION: Our research has discovered a novel diagnostic biomarker and unveiled its association with post-traumatic immune alterations. This breakthrough enables accurate and timely diagnosis of early trauma, facilitating the implementation of appropriate healthcare interventions.

Microbiome and lung cancer: carcinogenic mechanisms, early cancer diagnosis, and promising microbial therapies.

Microbiomes in the lung, gut, and oral cavity are correlated with lung cancer initiation and progression. While correlations have been preliminarily established in earlier studies, delving into microbe-mediated carcinogenic mechanisms will extend our understanding from correlation to causation. Building upon the causative relationships between microbiome and lung cancer, a novel concept of microbial biomarkers has emerged, mainly encompassing cancer-specific bacteria and circulating microbiome DNA. They might function as noninvasive liquid biopsy techniques for lung cancer early detection. Furthermore, potential microbial therapies have displayed initial efficacy in lung cancer treatment, providing multiple avenues for therapeutic intervention. Herein, we will discuss the molecular mechanisms and signaling pathways through which microbes influence lung cancer initiation and development. Additionally, we will summarize recent findings on microbial biomarkers as a member of tumor liquid biopsy techniques and provide an overview of the latest advances in various microbe-assisted/mediated therapeutic approaches for lung cancer.

Exploring the causal relationship between immune cells and idiopathic pulmonary fibrosis: a bi-directional Mendelian randomization study.

BACKGROUND: The potential pathogenic mechanism of idiopathic pulmonary fibrosis is widely recognized to involve immune dysregulation. However, the current pool of studies has yet to establish a unanimous agreement regarding the correlation between various types of immune cells and IPF. METHODS: By conducting a two-sample Mendelian randomization analysis using publicly available genetic data, the study examined the causal relationship between IPF and 731 immune cells. To ensure the reliability of the results, combined sensitivity analyses and inverse Mendelian analyses were conducted. Moreover, within subgroups, multivariate Mendelian randomization analyses were utilized to investigate the autonomous causal connection between immune cell characteristics and IPF. RESULTS: After adjusting for false discovery rate, it was discovered that 20 immunophenotypes exhibited a significant association with IPF. After subgrouping for multivariate Mendelian randomization analysis, there were six immunophenotypes that remained significantly associated with IPF. These included CD33 + HLA DR + CD14dim (OR = 0.96, 95% CI 0.93-0.99, P = 0.033), HLA DR + NK (OR = 0.92, 95% CI 0.85-0.98, P = 0.017), CD39 + CD8 + T cell %T cell (OR = 0.93, 95% CI 0.88-0.99, P = 0.024), CD3 on activated & secreting Treg (OR = 0.91, 95% CI 0.84-0.98, P = 0.026), PDL-1 on CD14- CD16 + monocyte (OR = 0.89, 95% CI 0.84-0.95, P = 8 × 10-4), and CD45 on CD33 + HLA DR + CD14- (OR = 1.08, 95% CI 1.01-1.15, P = 0.011). CONCLUSION: Our study reveals a noteworthy association between IPF and various immune cells, providing valuable insights for clinical research and aiding the advancement of immunologically-based therapeutic strategies.

High B7-H3 expression with low PD-L1 expression identifies armored-cold tumors in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is generally regarded as the most aggressive subtype among breast cancers, but exhibits higher chemotherapeutic and immunotherapeutic responses due to its unique immunogenicity. Thus, appropriate discrimination of subtypes is critical for guiding therapeutic options in clinical practice. In this research, using multiple in-house and public cohorts, we investigated the expression features and immuno-correlations of B7-H3 in breast cancer and checked the anti-tumor effect of the B7-H3 monoclonal antibody in a mouse model. We also developed a novel classifier combining B7-H3 and PD-L1 expression in TNBC. B7-H3 was revealed to be related to immuno-cold features and accumulated collagen in TNBC. In addition, targeting B7-H3 using the monoclonal antibody significantly suppressed mouse TNBC growth, reversed the armored-cold phenotype, and also boosted anti-PD-1 immunotherapy. In addition, patients with B7-H3 high and PD-L1 low expression showed the lowest anti-tumor immune infiltration, the highest collagen level, and the lowest therapeutic responses to multiple therapies, which mostly belong to armored-cold tumors. Overall, this research provides a novel subtyping strategy based on the combination of B7-H3/PD-L1 expression, which leads to a novel approach for the management of TNBC.

The causality between systemic inflammatory regulators and chronic respiratory diseases: A bidirectional Mendelian-randomization study.

INTRODUCTION: Accumulative evidence suggests the associations between systemic inflammatory regulators and chronic respiratory diseases (CRDs). However, the intrinsic causation remains implicit. Therefore, this study aimed to examine causative associations by mendelian randomization (MR) and to identify valuable active factors. METHODS: Based on data from the GWAS database, we performed MR analyses of 41 serum cytokines from 8,293 Finnish and European descent cohorts from GBMI and UKBB for five major CRDs. We mainly applied inverse variance weighted regression, supplemented by MR-Egger regression, weighted median, maximum likelihood, weighted mode, and simple mode algorithms. Moreover, sensitivity analyses were conducted using Cochrane's Q test, MR-Egger intercept, MR-PRESSO Global test and MR-Steiger filtering. Eventually, the consistency of MR results was assessed by leave-one-out. RESULTS: Our results suggest that 12 genetically predicted systemic inflammatory regulators probably participate in the progression of CRDs, including four risk factors (IL-1RA, IL-4, MIP-1A, PDGF-BB) and one protective factor (IL-6) in IPF, two protective factors (SCF, SDF-1A) in COPD, and two protective factors (SCF, SDF-1A) in asthma, two protective factors (GROA, IL-2RA) were also included in asthma, whereas only one factor (HGF) was protective against bronchiectasis. Additionally, two protective factors (FGF-BASIC, G-CSF) were identified in sarcoidosis. Sensitivity analyses showed no horizontal pleiotropy and significant heterogeneity. Finally, based on the findings of inverse MR analysis, no inverse causal association was uncovered, confirming the robustness of results. CONCLUSION: Our study unearths potential associations between systemic inflammatory modulators and common CRDs, providing new insights for inflammation-mediated CRD prevention and therapeutic approaches.

Erratum: SECTM1 is upregulated in immuno-hot tumors and predicts immunotherapeutic efficacy in multiple cancers.

[This corrects the article DOI: 10.1016/j.isci.2023.106027.].

Identification and validation of a novel NK cells-related signature to predict prognosis and immune microenvironment in LUAD.

BACKGROUND: The prevalence and fatality rates of lung cancer are experiencing a rapid escalation. Natural Killer (NK) cells have been established to have a crucial role in both tumor initiation and progression. Nevertheless, uncertainties persist regarding their precise implications in the prognosis of LUAD. METHODS: The data were obtained from reputable sources, such as the Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO) database, and our internally generated sequencing data. Utilizing the TCGA data as a background, we selected intersecting genes, validated by cluster analysis, to establish a Cox model and validated it using the GEO datasets. Furthermore, we conducted extensive analyses to investigate the significance of potential biomarkers in relation to immune cell infiltration, single-cell data, differential gene expression, and drug sensitivity. RESULTS: 67 immune-related genes associated with NK cells (NK-IRGs) were identified in the TCGA datasets, whose research potential was demonstrated by cluster analysis. A prognostic signature was identified utilizing the univariate and multivariate Cox model, resulting in the identification of five genes, which was validated using GEO datasets. Additionally, the nomogram's calibration curve demonstrated exceptional concordance between the projected and actual survival rates. Subsequent investigations uncovered that this prognostic signature demonstrated its independence as a risk factor. Notably, in the low-risk group, NK cells exhibited elevated levels of immune checkpoint molecules, indicating heightened sensitivity to immune therapy. These findings highlight the potential of utilizing this signature as a valuable tool in the selection of patients who could benefit from targeted immune interventions.

Establishment and validation of nomograms for predicting survival of lung invasive adenocarcinoma based on the level of pathological differentiation: a SEER cohort-based analysis.

Background: The pathological differentiation of invasive adenocarcinoma (IAC) has been linked closely with epidemiological characteristics and clinical prognosis. However, the current models cannot accurately predict IAC outcomes and the role of pathological differentiation is confused. This study aimed to establish differentiation-specific nomograms to explore the effect of IAC pathological differentiation on overall survival (OS) and cancer-specific survival (CSS). Methods: The data of eligible IAC patients between 1975 and 2019 were collected from the Surveillance, Epidemiology, and End Results (SEER) database, and randomly divided in a ratio of 7:3 into a training cohort and a validation cohort. The associations between pathological differentiation and other clinical characteristics were evaluated using chi-squared test. The OS and CSS analyses were performed using the Kaplan-Meier estimator, and the log-rank test was used for nonparametric group comparisons. Multivariate survival analysis was performed using a Cox proportional hazards regression model. The discrimination, calibration, and clinical performance of nomograms were assessed by area under receiver operating characteristic curve (AUC), calibration plots, and decision curve analysis (DCA). Results: A total of 4,418 IAC patients (1,001 high-differentiation, 1,866 moderate-differentiation, and 1,551 low-differentiation) were identified. Seven risk factors [age, sex, race, tumor-node-metastasis (TNM) stage, tumor size, marital status, and surgery] were screened to construct differentiation-specific nomograms. Subgroup analyses showed that disparate pathological differentiation played distinct roles in prognosis, especially in patients with older age, white race, and higher TNM stage. The AUC of nomograms for OS and CSS in the training cohort were 0.817 and 0.835, while in the validation cohort were 0.784 and 0.813. The calibration curves showed good conformity between the prediction of the nomograms and the actual observations. DCA results indicated that these nomogram models could be used as a supplement to the prediction of the TNM stage. Conclusions: Pathological differentiation should be considered as an independent risk factor for OS and CSS of IAC. Differentiation-specific nomogram models with good discrimination and calibration capacity were developed in the study to predict the OS and CSS in 1-, 3- and 5-year, which could be used predict prognosis and select appropriate treatment options.

The role of lipid metabolic reprogramming in tumor microenvironment.

Metabolic reprogramming is one of the most important hallmarks of malignant tumors. Specifically, lipid metabolic reprogramming has marked impacts on cancer progression and therapeutic response by remodeling the tumor microenvironment (TME). In the past few decades, immunotherapy has revolutionized the treatment landscape for advanced cancers. Lipid metabolic reprogramming plays pivotal role in regulating the immune microenvironment and response to cancer immunotherapy. Here, we systematically reviewed the characteristics, mechanism, and role of lipid metabolic reprogramming in tumor and immune cells in the TME, appraised the effects of various cell death modes (specifically ferroptosis) on lipid metabolism, and summarized the antitumor therapies targeting lipid metabolism. Overall, lipid metabolic reprogramming has profound effects on cancer immunotherapy by regulating the immune microenvironment; therefore, targeting lipid metabolic reprogramming may lead to the development of innovative clinical applications including sensitizing immunotherapy.

The multi-omics analysis identifies a novel cuproptosis-anoikis-related gene signature in prognosis and immune infiltration characterization of lung adenocarcinoma.

Background: Lung adenocarcinoma (LUAD) has emerged as one of the most aggressive lethal cancers. Anoikis serves as programmed apoptosis initiated by the detachment of cells from the extracel-lular matrix. Cuproptosis is distinct from traditional cell death modalities. The above two modes are both closely related to tumor progression, prognosis, and treatment. However, whether they have synergistic effects in LUAD deserves further investigation. Methods: The anoikis-related prognostic genes (ANRGs) co-expressed with cuproptosis-associated genes (CAGs) were screened using correlation analysis, analysis of variance, least absolute shrinkage, and selection operator (LASSO), and COX regression followed by functional analysis, and then LUAD risk score model was constructed. Using consensus clustering, the relationship between different subtypes and clinicopathological features, immune infiltration characteristics, and somatic mutations was analyzed. A nomogram was developed by incorporating clinical information, which provided a prediction of the survival of patients. Finally, a comprehensive analysis of ANRGs was performed and verified by the HPA database. Results: A total of 27 ANRGs associated with cuproptosis were obtained. On this basis, three distinct ANRGs subtypes were identified, and the differences between clinical prognosis and immune infiltration were observed. A risk score model has been constructed by incorporating seven ANRGs signatures (EIF2AK3, IKZF3, ITGAV, OGT, PLK1, TRAF2, XRCC5). A highly reliable nomogram was developed to help formulate treatment strategies based on risk score and the clinicopathological features of LUAD. The seven-gene signature was turned out to be strongly linked to immune cells and validated in single-cell data. Immunohistochemistry proved that all of them are highly expressed in LUAD tissues. Conclusion: This study reveals the potential relationship between cuproptosis-related ANRGs and clinicopathological features, tumor microenvironment (TME), and mutation characteristics, which can be applied for predicting the prognosis of LUAD and help develop individualized treatment strategies.

SECTM1 is upregulated in immuno-hot tumors and predicts immunotherapeutic efficacy in multiple cancers.

Immune checkpoint inhibitors (ICIs) have transformed the management of advanced cancers. However, many patients could not benefit from ICIs therapy, and thus several biomarkers for therapeutic prediction have been uncovered. In this research, more than ten public and in-house cohorts were used to explore the predictive value and immunological correlations of secreted and transmembrane 1 (SECTM1) in cancers. SECTM1 expression was enhanced in tumors from patients with well immunotherapeutic responses in multiple cancers. In addition, SECTM1 was immuno-correlated in pan-cancer and enhanced in immuno-hot tumors. In vitro assays revealed that SECTM1 was upregulated by the IFN-γ/STAT1 signaling. Moreover, analysis of in-house immunotherapy cohorts suggested both tumor-expressed and circulating SECTM1 are promising biomarkers to predict therapeutic responses. Overall, this study reveals that SECTM1 is a biomarker of benefit to ICIs in cancer patients. Further studies including large-scale patients are needed to establish its utilization as a biomarker of benefit to ICIs.

Recurrent lung adenocarcinoma benefits from microwave ablation following multidisciplinary treatments: A case with long-term survival.

Lung cancer has become the leading cause of cancer death all over the world. Nowadays, there is a consensus that the treatment of non-small cell lung cancer (NSCLC) prefers a combination of multidisciplinary comprehensive treatment and individualized treatment, which can significantly improve the prognosis of patients. Here, we report a female patient with recurrence-prone NSCLC. She had a decade-long disease course, during which the lesion recurred twice and finally cured with Multi-Disciplinary Treatment (MDT). An elderly female patient was admitted to the hospital after diagnosis of lung cancer, and treated with surgery and postoperative adjuvant chemotherapy. Five years later, suspicious lesions were found by computed tomography (CT) reexamination, and then confirmed tumor recurrence by puncture biopsy. Based on the genetic test results, gefitinib was used for subsequent targeted therapy, and the lesion gradually shrunk to disappear. However, the lesion appeared again two years later, after consultation the microwave ablation was adopted and the curative effect was excellent. At last, regular reexamination showed no abnormality, the patient has survived so far. The case proves the great benefit of multidisciplinary comprehensive treatment, especially microwave ablation for patient with recurrence-prone NSCLC. And the effect of systemic anti-tumor immune response induced by microwave ablation on lung cancer also needs to be further explored.