Immune signature of patients with cardiovascular disease predicts increased risk for a severe course of COVID-19.

Severe acute respiratory syndrome coronavirus type 2 (SARS-CoV-2) infection can lead to life-threatening clinical manifestations. Patients with cardiovascular disease (CVD) are at higher risk for severe courses of COVID-19. So far, however, there are hardly any strategies for predicting the course of SARS-CoV-2 infection in CVD patients at hospital admission. Thus, we investigated whether this prediction is achievable by prospectively analysing the blood immunophenotype of 94 nonvaccinated participants, including uninfected and acutely SARS-CoV-2-infected CVD patients and healthy donors, using a 36-colour spectral flow cytometry panel. Unsupervised data analysis revealed little differences between healthy donors and CVD patients, whereas the distribution of the cell populations changed dramatically in SARS-CoV-2-infected CVD patients. The latter had more mature NK cells, activated monocyte subsets, central memory CD4+ T cells, and plasmablasts but fewer dendritic cells, CD16+ monocytes, innate lymphoid cells, and CD8+ T-cell subsets. Moreover, we identified an immune signature characterised by CD161+ T cells, intermediate effector CD8+ T cells, and natural killer T (NKT) cells that is predictive for CVD patients with a severe course of COVID-19. Thus, intensified immunophenotype analyses can help identify patients at risk of severe COVID-19 at hospital admission, improving clinical outcomes through specific treatment.

Exploration of the molecular characteristics and potential clinical significance of shared immune-related genes between preterm preeclampsia and term preeclampsia.

BACKGROUND: Preeclampsia is a severe obstetric disorder that significantly affects the maternal and neonatal peri-partum safety and long-term quality of life. However, there is limited research exploring the common mechanisms and potential clinical significance between early-onset preeclampsia and full-term preeclampsia from an immunological perspective. METHODS: In this study, data analysis was conducted. Initially, immune-related co-expressed genes involving both subtypes of preeclampsia were identified through Weighted Gene Co-expression Network Analysis (WGCNA). Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were further employed to investigate the shared pathways regulated by immune-related genes. Binary logistic regression identified co-expressed genes with diagnostic value for preeclampsia, and a diagnostic model was constructed. Gene Set Enrichment Analysis (GSEA) predicted the potential biological functions of the selected genes. Lasso and Cox regression analyses identified genes closely associated with gestational duration, and a risk score model was established. A 4-gene feature, immune-related gene model for predicting the risk of preterm birth in preeclamptic pregnant women, was developed and validated through qPCR experiments. Immune cell infiltration analysis determined differences in immune cell infiltration between the two subtypes of preeclampsia. RESULTS: This study identified 4 immune-related co-expressed genes (CXCR6, PIK3CB, IL1RAP, and OSMR). Additionally, diagnostic and preterm birth risk prediction models for preeclampsia were constructed based on these genes. GSEA analysis suggested the involvement of these genes in the regulation of galactose metabolism, notch signaling pathway, and RIG-I like receptor signaling pathway. Immune pathway analysis indicated that the activation of T cell co-inhibition could be a potential intervention target for immunotherapy in early-onset preeclampsia. CONCLUSION: Our study provides promising insights into immunotherapy and mechanistic research for preeclampsia, discovering novel diagnostic and intervention biomarkers, and offering personalized diagnostic tools for preeclampsia.

Immune signatures predict response to house dust mite subcutaneous immunotherapy in patients with allergic rhinitis.

BACKGROUND: Identifying predictive biomarkers for allergen immunotherapy response is crucial for enhancing clinical efficacy. This study aims to identify such biomarkers in patients with allergic rhinitis (AR) undergoing subcutaneous immunotherapy (SCIT) for house dust mite allergy. METHODS: The Tongji (discovery) cohort comprised 72 AR patients who completed 1-year SCIT follow-up. Circulating T and B cell subsets were characterized using multiplexed flow cytometry before SCIT. Serum immunoglobulin levels and combined symptom and medication score (CSMS) were assessed before and after 12-month SCIT. Responders, exhibiting ≥30% CSMS improvement, were identified. The random forest algorithm and logistic regression analysis were used to select biomarkers and establish predictive models for SCIT efficacy in the Tongji cohort, which was validated in another Wisco cohort with 43 AR patients. RESULTS: Positive SCIT response correlated with higher baseline CSMS, allergen-specific IgE (sIgE)/total IgE (tIgE) ratio, and frequencies of Type 2 helper T cells, Type 2 follicular helper T (TFH2) cells, and CD23+ nonswitched memory B (BNSM) and switched memory B (BSM) cells, as well as lower follicular regulatory T (TFR) cell frequency and TFR/TFH2 cell ratio. The random forest algorithm identified sIgE/tIgE ratio, TFR/TFH2 cell ratio, and BNSM frequency as the key biomarkers discriminating responders from nonresponders in the Tongji cohort. Logistic regression analysis confirmed the predictive value of a combination model, including sIgE/tIgE ratio, TFR/TFH2 cell ratio, and CD23+ BSM frequency (AUC = 0.899 in Tongji; validated AUC = 0.893 in Wisco). CONCLUSIONS: A T- and B-cell signature combination efficiently identified SCIT responders before treatment, enabling personalized approaches for AR patients.

Dynamic immune signatures of patients with advanced non-small-cell lung cancer for infection prediction after immunotherapy.

Background: Pulmonary infections are a crucial health concern for patients with advanced non-small-cell lung cancer (NSCLC). Whether the clinical outcome of pulmonary infection is influenced by immunotherapy(IO) remains unclear. By evaluating immune signatures, this study investigated the post-immunotherapy risk of pulmonary infection in patients with lung cancer and identified circulating biomarkers that predict post-immunotherapy infection. Methods: Blood specimens were prospectively collected from patients with NSCLC before and after chemotherapy(C/T) and/or IO to explore dynamic changes in immune signatures. Real-world clinical data were extracted from medical records for outcome evaluation. Mass cytometry and ELISA were employed to analyze immune signatures and cytokine profiles to reveal potential correlations between immune profiles and the risk of infection. Results: The retrospective cohort included 283 patients with advanced NSCLC. IO was associated with a lower risk of pneumonia (odds ratio=0.46, p=0.012). Patients receiving IO and remained pneumonia-free exhibited the most favorable survival outcomes compared with those who received C/T or developed pneumonia (p<0.001). The prospective cohort enrolled 30 patients. The proportion of circulating NK cells significantly increased after treatment in IO alone (p<0.001) and C/T+IO group (p<0.01). An increase in cell densities of circulating PD-1+CD8+(cytotoxic) T cells (p<0.01) and PD-1+CD4+ T cells (p<0.01) were observed in C/T alone group after treatment. In IO alone group, a decrease in cell densities of TIM-3+ and PD-1+ cytotoxic T cells (p<0.05), and PD-1+CD4+ T cells (p<0.01) were observed after treatment. In C/T alone and C/T+IO groups, cell densities of circulating PD-1+ cytotoxic T cells significantly increased in patients with pneumonia after treatment(p<0.05). However, in IO alone group, cell density of PD-1+ cytotoxic T cells significantly decreased in patients without pneumonia after treatment (p<0.05). TNF-α significantly increased after treatment with IO alone (p<0.05) but decreased after C/T alone (p<0.01). Conclusions: Our results indicate that the incorporation of immunotherapy into treatment regimens may potentially offer protective effects against pulmonary infection. Protective effects are associated with reduction of exhausted T-cells and augmentation of TNF-α and NK cells. Exhausted T cells, NK cells, and TNF-α may play crucial roles in immune responses against infections. These observations highlight the potential utility of certain circulating biomarkers, particularly exhausted T cells, for predicting post-treatment infections.

Identification of an immune-related eRNA prognostic signature for clear cell renal cell carcinoma.

BACKGROUND: Immune-related enhancer RNAs (eRNAs) have garnered significant attention in cancer metabolism research, yet their specific roles in ccRCC have remained elusive. METHODS: We retrieved eRNA expression profiles from TCGA database and identified immune-related eRNAs (IREs) by assessing their co-expression with immune genes. Utilizing consensus clustering, we organized these IREs into two distinct clusters. The construction of an IREs signature was accomplished through the LASSO and multivariate Cox analysis. Furthermore, we performed Cell Counting Kit-8 and clonogenic assays to assess changes in the proliferative capacity of Caki-1 and 769-P cells. RESULTS: The existence of two clusters of immune-related eRNAs in ccRCC, each with distinctive prognostic and immunological attributes. Cluster B exhibited immunosuppressive properties and displayed a positive correlation with immunosuppressive cells. Functional enrichment analysis unveiled their involvement in several tumor-promoting pathways, metabolic pathways and immune pathways. The IREs signature demonstrated its potential to accurately predict patient immune and prognostic characteristics. AC003092.1, an eRNA strongly associated with patient survival, emerged as a potential oncogene significantly linked to adverse prognosis and the presence of immunosuppressive cells and checkpoints in ccRCC patients. Notably, AC003092.1 displayed marked upregulation in ccRCC tissues and cell lines, and its knockdown substantially inhibited the proliferation of Caki-1 and 769-P cells. CONCLUSION: We established a robust predictive model that played a vital role in determining the prognosis, clinicopathological characteristics and immune cell infiltration patterns of ccRCC patients. IRE, particularly AC003092.1, which was strongly associated with survival, hold promise as novel immunotherapeutic targets for ccRCC.

Mass cytometry analysis reveals altered immune profiles in patients with coronary artery disease.

Objective: The importance of inflammation in atherosclerosis is well accepted, but the role of the adaptive immune system is not yet fully understood. To further explore this, we assessed the circulating immune cell profile of patients with coronary artery disease (CAD) to identify discriminatory features by mass cytometry. Methods: Mass cytometry was performed on patient samples from the BioHEART-CT study, gated to detect 82 distinct cell subsets. CT coronary angiograms were analysed to categorise patients as having CAD (CAD+) or having normal coronary arteries (CAD-). Results: The discovery cohort included 117 patients (mean age 61 ± 12 years, 49% female); 79 patients (68%) were CAD+. Mass cytometry identified changes in 15 T-cell subsets, with higher numbers of proliferating, highly differentiated and cytotoxic cells and decreases in naïve T cells. Five T-regulatory subsets were related to an age and gender-independent increase in the odds of CAD incidence when expressing CCR2 (OR 1.12), CCR4 (OR 1.08), CD38 and CD45RO (OR 1.13), HLA-DR (OR 1.06) and Ki67 (OR 1.22). Markers of proliferation and differentiation were also increased within B cells, while plasmacytoid dendritic cells were decreased. This combination of changes was assessed using SVM models in discovery and validation cohorts (area under the curve = 0.74 for both), confirming the robust nature of the immune signature detected. Conclusion: We identified differences within immune subpopulations of CAD+ patients which are indicative of a systemic immune response to coronary atherosclerosis. This immune signature needs further study via incorporation into risk scoring tools for the precision diagnosis of CAD.

Identification and validation of an immune-relevant risk signature predicting survival outcome and immune infiltration in uveal melanoma.

PURPOSE: The current study aimed to reveal a novel immune-related signature to evaluate immune infiltration status and the survival outcome for patients with uveal melanoma (UM). METHODS: Based on 80 UM samples from the Cancer Genome Atlas, the transcriptome gene expression and clinical characteristics were analyzed to identify immune-related genes that contributed most to prognosis based on LASSO Cox regression. By combining the gene expression level with the corresponding regression coefficient, a risk score was calculated and all patients were divided into high- and low-risk groups. Survival, tumor-infiltrating immune cell abundance, dysregulated signaling pathways, immunophenoscore and tumor mutation burden were compared between two groups. Validation of the risk signature was performed in GSE22138 and GSE44295 cohort. For evaluating the immunotherapy efficacy, 348 advanced urothelial cancer patients treated with immune checkpoint inhibitor (ICI) were used for external validation. RESULTS: Nine immune-related prognostic genes were identified under the LASSO Cox regression in the TCGA cohort; they are ACKR2, AREG, CCL5, CLEC11A, IGKV1-33, IL36B, NROB1, TRAV8-4 and TRBV28. Better prognosis, elevated immune cell infiltration, decreased immune-suppressive cell infiltration, immune response-related pathways and higher immunophenoscore were found in low-risk patients, with better ICI treatment response rate. CONCLUSION: The identified immune risk signature was demonstrated to be associated with the favorable immune infiltration, prognosis and immunotherapeutic efficacy, which may provide clues for survival evaluation and immune treatment.

Heterogeneity and potential therapeutic insights for triple-negative breast cancer based on metabolic-associated molecular subtypes and genomic mutations.

Objective: Due to a lack of effective therapy, triple-negative breast cancer (TNBC) is extremely poor prognosis. Metabolic reprogramming is an important hallmark in tumorigenesis, cancer diagnosis, prognosis, and treatment. Categorizing metabolic patterns in TNBC is critical to combat heterogeneity and targeted therapeutics. Methods: 115 TNBC patients from TCGA were combined into a virtual cohort and verified by other verification sets, discovering differentially expressed genes (DEGs). To identify reliable metabolic features, we applied the same procedures to five independent datasets to verify the identified TNBC subtypes, which differed in terms of prognosis, metabolic characteristics, immune infiltration, clinical features, somatic mutation, and drug sensitivity. Results: In general, TNBC could be classified into two metabolically distinct subtypes. C1 had high immune checkpoint genes expression and immune and stromal scores, demonstrating sensitivity to the treatment of PD-1 inhibitors. On the other hand, C2 displayed a high variation in metabolism pathways involved in carbohydrate, lipid, and amino acid metabolism. More importantly, C2 was a lack of immune signatures, with late pathological stage, low immune infiltration and poor prognosis. Interestingly, C2 had a high mutation frequency in PIK3CA, KMT2D, and KMT2C and displayed significant activation of the PI3K and angiogenesis pathways. As a final output, we created a 100-gene classifier to reliably differentiate the TNBC subtypes and AKR1B10 was a potential biomarker for C2 subtypes. Conclusion: In conclusion, we identified two subtypes with distinct metabolic phenotypes, provided novel insights into TNBC heterogeneity, and provided a theoretical foundation for therapeutic strategies.

Immune gene signatures as prognostic criteria for cancer patients.

Recently, the possibility of using immune gene signatures (IGSs) has been considered as a novel prognostic tool for numerous cancer types. State-of-the-art methods of genomic, transcriptomic, and protein analysis have allowed the identification of a number of immune signatures correlated to disease outcome. The major adaptive and innate immune components are the T lymphocytes and macrophages, respectively. Herein, we collected essential data on IGSs consisting of subsets of T cells and tumor-associated macrophages and indicating cancer patient outcomes. We discuss factors that can introduce errors in the recognition of immune cell types and explain why the significance of immune signatures can be interpreted with uncertainty. The unidirectional functions of cell types should be entirely addressed in the signatures constructed by the combination of innate and adaptive immune cells. The state of the antitumor immune response is the key basis for IGSs and should be considered in gene signature construction. We also analyzed immune signatures for the prediction of immunotherapy response. Finally, we attempted to explain the present-day limitations in the use of immune signatures as robust criteria for prognosis.

TRB CDR3-cancer testis antigen chemical complementarity scoring for identifying productive immune responses in renal cell carcinoma.

BACKGROUND: Immunogenomics approaches to the characterization of renal cell carcinoma (RCC) have helped to better our understanding of the features of RCC immune dysfunction. However, much is still unknown with regard to specific immune interactions and their impact in the tumor microenvironment. OBJECTIVE: This study applied chemical complementarity scoring for the TRB complementarity determining region-3 (CDR3) amino acid sequences and cancer testis antigens (CTAs) to determine whether such complementarity correlated with survival and the expression of immune marker genes. METHODS: TRB recombination reads from RCC tumor samples from RNAseq files obtained from two separate databases, Moffitt Cancer Center and The Cancer Genome Atlas (TCGA), were evaluated. Chemical complementarity scores (CSs) were calculated for TRB CDR3-CTA pairs and survival assessments based on those CSs were performed. RESULTS: Moffitt Cancer Center and TCGA cases representing the upper 50th percentile of chemical CSs for TRB CDR3 amino acid sequences and the CTA POTEA were found to be associated with a better overall survival (OS) Also, greater tumor RNA expression of multiple immune signature genes, including granzyme A, granzyme B, and interferon-gamma were correlated with the higher chemical CSs. CONCLUSIONS: These results indicate that TRB CDR3-CTA chemical complementarity scoring may be useful in distinguishing RCC cases with a productive, anti-tumor immune response from cases where basic immune parameter assessments are inconsistent with a productive immune response.

The Mutational, Prognostic, and Therapeutic Landscape of Neuroendocrine Neoplasms.

BACKGROUND: Neuroendocrine neoplasms (NENs) represent clinically and genetically heterogeneous malignancies, thus a comprehensive understanding of underlying molecular characteristics, prognostic signatures, and potential therapeutic targets is urgently needed. METHODS: Next-generation sequencing (NGS) and immunohistochemistry were applied to acquire genomic and immune profiles of NENs from 47 patients. RESULTS: Difference was distinguished based on differentiation grade and primary localization. Poorly differentiated neuroendocrine carcinomas (NECs) and well-differentiated neuroendocrine tumors (NETs) harbored distinct molecular features; we observed that tumor mutational burden (TMB) and tumor neoantigen burden (TNB) were significantly higher in NECs versus NETs. Notably, we identified a 7-gene panel (MLH3, NACA, NOTCH1, NPAP1, RANBP17, TSC2, and ZFHX4) as a novel prognostic signature in NENs; patients who carried mutations in any of the 7 genes exhibited significantly poorer survival. Furthermore, loss of heterozygosity (LOH) and germline homogeneity in human leukocyte antigen (HLA) are common in NENs, accounting for 39% and 36%, respectively. Notably, HLA LOH was an important prognostic biomarker for a subgroup of NEN patients. Finally, we analyzed clinically actionable targets in NENs, revealing that TMB high (TMB-H) or gene mutations in TP53, KRAS, and HRAS were the most frequently observed therapeutic indicators, which granted eligibility to immune checkpoint blockade (ICB) and targeted therapy. CONCLUSION: Our study revealed heterogeneity of NENs, and identified novel prognostic signatures and potential therapeutic targets, which directing improvements of clinical management for NEN patients in the foreseeable future.

Cross-platform comparison of immune signatures in immunotherapy-treated patients with advanced melanoma using a rank-based scoring approach.

BACKGROUND: Gene expression profiling is increasingly being utilised as a diagnostic, prognostic and predictive tool for managing cancer patients. Single-sample scoring approach has been developed to alleviate instability of signature scores due to variations from sample composition. However, it is a challenge to achieve comparable signature scores across different expressional platforms. METHODS: The pre-treatment biopsies from a total of 158 patients, who have received single-agent anti-PD-1 (n = 84) or anti-PD-1 + anti-CTLA-4 therapy (n = 74), were performed using NanoString PanCancer IO360 Panel. Multiple immune-related signature scores were measured from a single-sample rank-based scoring approach, singscore. We assessed the reproducibility and the performance in reporting immune profile of singscore based on NanoString assay in advance melanoma. To conduct cross-platform analyses, singscores between the immune profiles of NanoString assay and the previous orthogonal whole transcriptome sequencing (WTS) data were compared through linear regression and cross-platform prediction. RESULTS: singscore-derived signature scores reported significantly high scores in responders in multiple PD-1, MHC-1-, CD8 T-cell-, antigen presentation-, cytokine- and chemokine-related signatures. We found that singscore provided stable and reproducible signature scores among the repeats in different batches and cross-sample normalisations. The cross-platform comparisons confirmed that singscores derived via NanoString and WTS were comparable. When singscore of WTS generated by the overlapping genes to the NanoString gene set, the signatures generated highly correlated cross-platform scores (Spearman correlation interquartile range (IQR) [0.88, 0.92] and r2 IQR [0.77, 0.81]) and better prediction on cross-platform response (AUC = 86.3%). The model suggested that Tumour Inflammation Signature (TIS) and Personalised Immunotherapy Platform (PIP) PD-1 are informative signatures for predicting immunotherapy-response outcomes in advanced melanoma patients treated with anti-PD-1-based therapies. CONCLUSIONS: Overall, the outcome of this study confirms that singscore based on NanoString data is a feasible approach to produce reliable signature scores for determining patients' immune profiles and the potential clinical utility in biomarker implementation, as well as to conduct cross-platform comparisons, such as WTS.

Plasma Proteomics Unveil Novel Immune Signatures and Biomarkers upon SARS-CoV-2 Infection.

Several elements have an impact on COVID-19, including comorbidities, age and sex. To determine the protein profile changes in peripheral blood caused by a SARS-CoV-2 infection, a proximity extension assay was used to quantify 1387 proteins in plasma samples among 28 Finnish patients with COVID-19 with and without comorbidities and their controls. Key immune signatures, including CD4 and CD28, were changed in patients with comorbidities. Importantly, several unreported elevated proteins in patients with COVID-19, such as RBP2 and BST2, which show anti-microbial activity, along with proteins involved in extracellular matrix remodeling, including MATN2 and COL6A3, were identified. RNF41 was downregulated in patients compared to healthy controls. Our study demonstrates that SARS-CoV-2 infection causes distinct plasma protein changes in the presence of comorbidities despite the interpatient heterogeneity, and several novel potential biomarkers associated with a SARS-CoV-2 infection alone and in the presence of comorbidities were identified. Protein changes linked to the generation of SARS-CoV-2-specific antibodies, long-term effects and potential association with post-COVID-19 condition were revealed. Further study to characterize the identified plasma protein changes from larger cohorts with more diverse ethnicities of patients with COVID-19 combined with functional studies will facilitate the identification of novel diagnostic, prognostic biomarkers and potential therapeutic targets for patients with COVID-19.

Unique profile on the progress free survival and overall survival in patients with advanced non-small cell lung cancer in the Qujing area, Southwest China.

Background: China's southwestern region, Qujing, harbors a high incidence of non-small cell lung cancer (NSCLC) and related mortality. This study was designed to reveal the impact of an immune-related prognostic signature (IRPS) on advanced NSCLC in the Qujing. Methods: Tissue specimens from an independent cohort of 37 patients with advanced NSCLC were retrospectively evaluated to determine the relationship between the IRPS estimated by next-generation sequencing (NGS) and clinical outcome. To compare the IRPS in tissue and the clinical outcomes between Qujing and non-Qujing populations, we analyzed datasets of 23 patients with advanced NSCLC from The Cancer Genome Atlas (TCGA) database. In addition, an independent cohort (n=111) of blood specimens was retrospectively analyzed to determine the relationship between the IRPS and clinical outcome. Finally, we evaluated the utility of the blood IRPS in classifying 24 patients with advanced NSCLC who might benefit from immunotherapy. Results: In cohort 1, the Qujing population with tTMB-H (≥ 10 mutations/Mb) or KRAS mutations had shorter progression-free survival (PFS) (hazard ratio [HR] 0.37, 0.14 to 0.97, P = 0.04; HR 0.23, 0.08 to 0.66, P < 0.01) and overall survival (OS) (HR 0.05, 0.01 to 0.35, P < 0.01; HR 0.22, 0.07 to 0.66, P < 0.01). In cohort 2 of the Qujing population, bTMB-H (≥ 6 mutations per Mb) and KRAS mutations were related to PFS (HR 0.59, 0.36 to 0.99, P = 0.04; HR 0.50, 0.26 to 0.98, P = 0.04) and OS (HR 0.58, 0.35 to 0.96, P = 0.03; HR 0.48, 0.25 to 0.93, P = 0.03). Notably, the Qujing population with bTMB-H had superior PFS (HR 0.32, 0.09 to 1.09, P = 0.01), OS (HR 0.33, 0.10 to 1.13, P < 0.01) and objective response rates (ORRs) (83.3% vs. 14.3% vs. 20.0%, P <0.01) to immunotherapy than other populations. Conclusions: These findings show that tTMB, bTMB and KRAS mutations appear to be independent validated IRPSs that predict the clinical outcomes of Qujing populations with advanced NSCLC and that bTMB may be used as a reliable IRPS to predict the clinical benefit from anti-PD-1 therapies among populations from Qujing with advanced NSCLC.

Azathioprine therapy induces selective NK cell depletion and IFN-γ deficiency predisposing to herpesvirus reactivation.

BACKGROUND: Azathioprine is a widely prescribed drug for patients with chronic inflammatory diseases such as myasthenia gravis or organ transplant recipients. Azathioprine exerts immunosuppressive effects by inhibiting intracellular purine synthesis and reducing the numbers of circulating B and T lymphocytes. Case reports indicate increased risk for serious infections that can occur despite regular measurements of lymphocyte counts during azathioprine therapy. OBJECTIVE: We sought to comprehensively investigate therapy-associated patient risks and the underlying immune dysfunction of azathioprine use. METHODS: Peripheral blood leukocytes were analyzed using single-cell mass and spectral flow cytometry to detect specific effects of azathioprine use on the systemic immune signature. Therapy-associated clinical features were analyzed in 2 independent cohorts of myasthenia gravis patients. RESULTS: Azathioprine therapy selectively induced pronounced CD56dimCD16+ natural killer cell depletion and concomitant IFN-γ deficiency. Cytokine profiling revealed a specific contraction of classical TH1 cells during azathioprine treatment. We further observed an increased occurrence of reactivation of endogenous latent herpesviruses in the azathioprine-treated group versus in patients with myasthenia gravis who were not receiving immunomodulatory treatment; this increased occurrence was validated in an independent cohort. CONCLUSION: Our study highlights the risk of development of adverse events during azathioprine therapy and suggests that natural killer cell monitoring could be valuable in clinical practice.

Developing a Novel Immune-Related Seven-Gene Signature and Immune Infiltration Pattern in Patients with COVID-19 and Cardiovascular Disease.

BACKGROUND: patients with pre-existence of cardiovascular disease (CVD) are vulnerable to coronavirus disease 2019 (COVID-19), and COVID-19 will cause long-term burden of CVD. However, the common pathogenic mechanisms are not fully elucidated. More detailed knowledge of linking biological molecules and the role of immune signature would allow more valuable and specific clinical management. METHODS: the gene expression profiles of CVD and COVID-19 were retrieved from the GEO database. Common differentially expressed genes (DEGs) were screened with the Limma R package and the WGCNA algorithm, and then functional enrichment analysis, protein-protein interaction network, hub genes, and small therapeutic molecules analyses were performed. The hub immune-related genes (HIRGs) were intersected, and their associations with immune cells, expressional correlation, evaluated performance, and potential signal pathways were further investigated. RESULTS: In total, 57 common DEGs were identified as a shared transcriptional signature between CVD and COVID-19, and 12 hub genes were screened using five topological algorithms. There are common altered immune responses in the response of these two diseases, and seven HIRGs, including C5AR1, MMP9, CYBB, FPR2, CSF1R, TLR2, and TLR4, were identified, with positive correlation to altered macrophages and neutrophils. Nine small molecular agents (SMAs) were detected as promising therapeutic drugs. These seven HIRGs mainly participated in the inflammatory immune response through activation of Il2 stat5 signaling and Tnfa signaling via nfκb pathways, and ROC curves confirmed their good discriminatory capacity in the two diseases. CONCLUSIONS: this study established the co-expression network and identified a new immune-related seven-gene signature as therapeutic targets, which may provide new insights into pathogenic mechanisms and novel clinical management strategies.

m7G-related lncRNAs are potential biomarkers for predicting prognosis and immune responses in patients with oral squamous cell carcinoma.

Among head and neck cancers, oral squamous cell carcinoma (OSCC) is the most common malignant tumor. N-7-methylguanosine (m7G) and lncRNAs are both related to the development and progression of tumors. Therefore, this study aims to explore and establish the prognostic signal of OSCC based on m7G-related lncRNAs. In this study, RNA sequencing transcriptome data of OSCC patients were downloaded from The Cancer Genome Atlas (TCGA) database. Therefore, m7G-related lncRNAs were identified as differentially expressed in OSCC. Then, univariate Cox regression analysis and LASSO regression analysis were conducted to evaluate the prognostic significance of differentially expressed lncRNAs. Consequently, the abovementioned lncRNAs were assigned five OSCC patient risk scores, with high-risk and low-risk patients assigned to each group. Different signaling pathways were significantly enriched between the two groups as determined by set enrichment analysis (GSEA). Multivariate Cox regression analysis confirmed the factors used to construct the nomogram model. Then, the prognosis of the nomogram model was evaluated. Consequently, high-risk individuals had higher immune infiltration levels. According to the results of a study that evaluated the sensitivity of different risk subgroups to antitumour drugs, the high-risk group had a high sensitivity to doxorubicin. By performing real-time polymerase chain reaction (RT‒PCR), we verified the expression of these five m7G lncRNAs. Therefore, the model based on five m7G-related lncRNAs was able to predict the overall survival rates of OSCC patients and guide their treatment. It can also spur new ideas about how to prevent and treat OSCC.

Baseline immune signature score of Tregs × HLA-DR+CD4+ T cells × PD1+CD8+ T cells predicts outcome to immunotherapy in cancer patients.

Background: The use of immunotherapy (IT) is rapidly increasing across different tumor entities. PD-L1 expression is primarily used for therapy evaluation. The disadvantages of PD-L1 status are spatial and temporal heterogeneity as well as tumor type-dependent variation of predictive value. To optimize patient selection for IT, new prediction markers for therapy success are needed. Based on the systemic efficacy of IT, we dissected the immune signature of peripheral blood as an easily accessible predictive biomarker for therapeutic success. Methods: We conducted a retrospective clinical study of 62 cancer patients treated with IT. We assessed peripheral immune cell counts before the start of IT via flow cytometry. The predictive value for therapy response of developed immune signature scores was tested by ROC curve analyses and scores were correlated with time to progression (TTP). Results: High score values of "Tregs ÷ (CD4+/CD8+ ratio)" (Score A) and high score values of "Tregs × HLA-DR+CD4+ T cells × PD1+CD8+ T cells" (Score B) significantly correlated with response at first staging (p = 0.001; p < 0.001). At the optimal cutoff point, Score A correctly predicted 79.1% and Score B correctly predicted 89.3% of the staging results (sensitivity: 86.2%, 90.0%; specificity: 64.3%, 87.5%). A high Score A and Score B statistically correlated with prolonged median TTP (6.13 vs. 2.17 months, p = 0.025; 6.43 vs. 1.83 months, p = 0.016). Cox regression analyses for TTP showed a risk reduction of 55.7% (HR = 0.44, p = 0.029) for Score A and an adjusted risk reduction of 73.2% (HR = 0.27, p = 0.016) for Score B. Conclusion: The two identified immune signature scores showed high predictive value for therapy response as well as for prolonged TTP in a pan-cancer patient population. Our scores are easy to determine by using peripheral blood and flow cytometry, apply to different cancer entities, and allow an outcome prediction before the start of IT.

A novel immune signature predicts immunotherapy responsiveness and reveals the landscape of the tumor immune microenvironment in head and neck squamous cell carcinoma.

Background: Immune-checkpoint blockade (ICB) has been routinely implemented to treat head and neck squamous cell carcinoma (HNSCC) patients. However, only a few patients benefit from immune checkpoint inhibitor (ICI) therapies. Methods: In this study, we used a combined cohort (including the GSE41613, GSE65858, TCGA, and CELL cohorts) to identify hub genes significantly associated with ICB and activated CD8+ T-cell gene signatures. We performed single-sample gene set enrichment analysis (ssGSEA) to quantify the expression of hub genes; we then constructed a novel immune signature named "the IMS" that can predict immunotherapy responsiveness, prognosis, immune infiltration, and clinical characteristics. Data from the GSE102349 external cohort and the pembrolizumab cohort obtained from a clinical trial were used to validate the efficiency of the IMS. In addition, we revealed potential mechanisms of the antitumor response by analyzing the HNSCC single-cell database. Finally, we used the LASSO algorithm to build an IMS-related risk model. Results: The high IMS group was associated with significant immune activation, better prognosis, and increased immunotherapy responsiveness; thus, the IMS potentially represents a candidate biomarker for ICB. Moreover, a tumor microenvironment with a higher IMS underwent remarkable metabolic reprogramming characterized by enrichment in the glycolysis/gluconeogenesis, oxidative phosphorylation, and citrate cycle (TCA cycle) pathways. We also revealed key information on cellular crosstalk between the IMS and other immune lineages, which may mechanistically explain immune escape. In addition, we constructed and validated a risk prediction model (CD2, TBC1D10C, and CD3E) that could stratify HNSCC patients based on survival and response to ICB treatment. Conclusion: IMS is a signature closely correlated with the tumor immune microenvironment. The findings of this study contribute to the understanding of the immune landscape in HNSCC patients. IMS may aid in the clinical management of HNSCC patients through the identification of effective immunotherapies for specific patients.

Amino acid metabolism-based molecular classification of colon adenocarcinomavia in silico analysis.

Amino acid metabolism is closely related to the occurrence and development of colon adenocarcinoma (COAD). Studies on the relationship between COAD and the expression of amino acid metabolism are still rare. Based on in silico analysis, we used 358 amino acid metabolism-related genes (AAMRGs) to determine the amino acid metabolism characteristics and then classified COAD into two distinct subtypes, namely AA1 and AA2. Then we analyzed the clinical characteristics, somatic mutation landscape, transcriptome profile, metabolism signatures, immune infiltration, and therapy sensitivity of these two subtypes. The AA1 subtype had inferior overall survival and was characterized by lower amino acid metabolic activity, higher tumor mutation burden, and higher immune cell infiltration, while AA2 displayed higher metabolic activity and relatively better survival. Furthermore, the AA1 subtype was likely to benefit from irinotecan in chemotherapy and immune checkpoint blockade therapy including programmed cell death protein-1 (PD-1) and cytotoxic T-lymphocyte-associated protein-4 (CTLA-4) immune checkpoint inhibitor but was resistant to targeted therapy cetuximab. The AA2 subtype showed higher sensitivity to 5-fluorouracil and oxaliplatin. To provide perspectives on cell-specific metabolism for further investigation, we explored metabolic activity in different cell types including lymphocytes, mast cells, myeloid cells stromal cells, and epithelial cells via colorectal cancer single-cell data. Additionally, to assist in clinical decision-making and prognosis prediction, a 60-AAMRG-based classifier was generated and validated in an independent cohort.