Comprehensive characterization of costimulatory molecule gene for diagnosis, prognosis and recognition of immune microenvironment features in sepsis.

The present study, which involved 10 GEO datasets and 3 ArrayExpress datasets, comprehensively characterized the potential effects of CMGs in sepsis. Based on machine learning algorithms (Lasso, SVM and ANN), the CMG classifier was constructed by integrating 6 hub CMGs (CD28, CD40, LTB, TMIGD2, TNFRSF13C and TNFSF4). The CMG classifier exhibit excellent diagnostic values across multiple datasets and time points, and was able to distinguish sepsis from other critical diseases. The CMG classifier performed better in predicting mortality than other clinical characteristics or endotypes. More importantly, from clinical specimens, the CMG classifier showed more superior diagnostic values than PCT and CRP. Alternatively, the CMG classifier/hub CMGs is significantly correlated with immune cells infiltration (B cells, T cells, Tregs, and MDSC), pivotal immune and molecular pathways (inflammation-promoting, complement and coagulation cascades), and several cytokines. Collectively, CMG classifier was a robust tool for diagnosis, prognosis and recognition of immune microenvironment features in sepsis.

Construction of Autophagy-Related Gene Classifier for Early Diagnosis, Prognosis and Predicting Immune Microenvironment Features in Sepsis by Machine Learning Algorithms.

Background: The immune system plays a fundamental role in the pathophysiology of sepsis, and autophagy and autophagy-related molecules are crucial in innate and adaptive immune responses; however, the potential roles of autophagy-related genes (ARGs) in sepsis are not comprehensively understood. Methods: A systematic search was conducted in ArrayExpress and Gene Expression Omnibus (GEO) cohorts from July 2005 to May 2022. Machine learning approaches, including modified Lasso penalized regression, support vector machine, and artificial neural network, were applied to identify hub ARGs, thereby developing a prediction model termed ARG classifier. Diagnostic and prognostic performance of the model was comprehensively analyzed using multi-transcriptome data. Subsequently, we systematically correlated the ARG classifier/hub ARGs with immunological characteristics of multiple aspects, including immune cell infiltration, immune and molecular pathways, cytokine levels, and immune-related genes. Further, we collected clinical specimens to preliminarily investigate ARG expression levels and to assess the diagnostic performance of ARG classifier. Results: A total of ten GEO and three ArrayExpress datasets were included in this study. Based on machine learning algorithms, eight key ARGs (ATG4C, BAX, BIRC5, ERBB2, FKBP1B, HIF1A, NCKAP1, and NFKB1) were integrated to establish ARG classifier. The model exhibited excellent diagnostic values (AUC > 0.85) in multiple datasets and multiple points in time and superiorly distinguished sepsis from other critical illnesses. ARG classifier showed significant correlations with clinical characteristics or endotypes and performed better in predicting mortality (AUC = 0.70) than other clinical characteristics. Additionally, the identified hub ARGs were significantly associated with immune cell infiltration (B, T, NK, dendritic, T regulatory, and myeloid-derived suppressor cells), immune and molecular pathways (inflammation-promoting pathways, HLA, cytolytic activity, apoptosis, type-II IFN response, complement and coagulation cascades), levels of several cytokines (PDGFRB, IL-10, IFNG, and TNF), which indicated that ARG classifier/hub ARGs adequately reflected the immune microenvironment during sepsis. Finally, using clinical specimens, the expression levels of key ARGs in patients with sepsis were found to differ significantly from those of control patients, and ARG classifier exhibited superior diagnostic performance, compared to procalcitonin and C-reactive protein. Conclusion: Collectively, a diagnostic and prognostic model (ARG classifier) based on eight ARGs was developed which may assist clinicians in diagnosis of sepsis and recognizing patient at high risk to guide personalized treatment. Additionally, the ARG classifier effectively reflected the immune microenvironment diversity of sepsis and may facilitate personalized counseling for specific therapy.

Construction of an HLA Classifier for Early Diagnosis, Prognosis, and Recognition of Immunosuppression in Sepsis by Multiple Transcriptome Datasets.

Background: Sepsis is a clinical syndrome, due to a dysregulated inflammatory response to infection. Accumulating evidence shows that human leukocyte antigen (HLA) genes play a key role in the immune responses to sepsis. Nevertheless, the effects of HLA genes in sepsis have still not been comprehensively understood. Methods: A systematical search was performed in the Gene Expression Omnibus (GEO) and ArrayExpress databases from inception to 10 September 2021. Random forest (RF) and modified Lasso penalized regression were conducted to identify hub genes in multi-transcriptome data, thus we constructed a prediction model, namely the HLA classifier. ArrayExpress databases, as external validation, were utilized to evaluate its diagnostic, prognostic, and predictive performance. Immune cell infiltration score was calculated via CIBERSORTx tools and single-sample gene set enrichment analysis (ssGSEA). Gene set variation analysis (GSVA) and ssGSEA were conducted to determine the pathways that are significantly enriched in different subgroups. Next, we systematically correlated the HLA classifier with immunological characteristics from multiple perspectives, such as immune-related cell infiltration, pivotal molecular pathways, and cytokine expression. Finally, quantitative real-time polymerase chain reaction (qRT-PCR) was conducted to validate the expression level of HLA genes in clinical samples. Results: A total of nine datasets comprising 1,251 patients were included. Based on RF and modified Lasso penalized regression in multi-transcriptome datasets, five HLA genes (B2M, HLA-DQA1, HLA-DPA1, TAP1, and TAP2) were identified as hub genes, which were used to construct an HLA classifier. In the discovery cohort, the HLA classifier exhibited superior diagnostic value (AUC = 0.997) and performed better in predicting mortality (AUC = 0.716) than clinical characteristics or endotypes. Encouragingly, similar results were observed in the ArrayExpress databases. In the E-MTAB-7581 dataset, the use of hydrocortisone in the HLA high-risk subgroup (OR: 2.84, 95% CI 1.07-7.57, p = 0.037) was associated with increased risk of mortality, but not in the HLA low-risk subgroup. Additionally, immune infiltration analysis by CIBERSORTx and ssGSEA revealed that B cells, activated dendritic cells, NK cells, T helper cells, and infiltrating lymphocytes (ILs) were significantly richer in HLA low-risk phenotypes, while Tregs and myeloid-derived suppressor cells (MDSCs) were more abundant in HLA high-risk phenotypes. The HLA classifier was significantly negatively correlated with B cells, activated dendritic cells, NK cells, T helper cells, and ILs, yet was significantly positively correlated with Tregs and MDSCs. Subsequently, molecular pathways analysis uncovered that cytokine-cytokine receptor (CCR) interaction, human leukocyte antigen (HLA), and antigen-presenting cell (APC) co-stimulation were significantly enriched in HLA low-risk endotypes, which was significantly negatively correlated with the HLA classifier in multi-transcriptome data. Finally, the expression levels of several cytokines (IL-10, IFNG, TNF) were significantly different between the HLA subgroups, and the ratio of IL-10/TNF was significantly positively correlated with HLA score in multi-transcriptome data. Results of qRT-PCR validated the higher expression level of B2M as well as lower expression level of HLA-DQA1, HLA-DPA1, TAP1, and TAP2 in sepsis samples compared to control sample. Conclusion: Based on five HLA genes, a diagnostic and prognostic model, namely the HLA classifier, was established, which is closely correlated with responses to hydrocortisone and immunosuppression status and might facilitate personalized counseling for specific therapy.

Construction and validation of immune-related LncRNAs classifier to predict prognosis and immunotherapy response in laryngeal squamous cell carcinoma.

BACKGROUND: Rapid advances in transcriptomic profiles have resulted in recognizing IRLs (immune-related long noncoding RNAs), as modulators of the expression of genes related to immune cells that mediate immune inhibition as well as immune stimulatory, indicating LncRNAs play fundamental roles in immune modulation. Hence, we establish an IRL classifier to precisely predict prognosis and immunotherapeutic efficiency in laryngeal squamous cell carcinoma (LSCC). METHODS: LSCC RNA-seq (RNA sequencing) datasets, somatic mutation data, and corresponding clinicopathologic information were acquired from TCGA (the Cancer Genome Atlas) and Gene Expression Omnibus (GEO) databases. Spearman correlation analysis identified LncRNAs associated with immune-related genes (IRG). Based on Lasso penalized regression and random forest (RF), we constructed an IRL classifier associated with prognosis. GEO database was utilized to validate the IRL classifier. The predictive precision and clinical application of the IRL classifier were assessed and compared to clinicopathologic features. The immune cell infiltration of LSCC was calculated via CIBERSORTx tools and ssGSEA (single-sample gene set enrichment analysis). Then, we systematically correlated the IRL classifier with immunological characteristics from multiple perspectives, such as immune-related cells infiltrating, tumor microenvironment (TME) scoring, microsatellite instability (MSI), tumor mutation burden (TMB), and chemokines. Finally, the TIDE (tumor immune dysfunction and exclusion) algorithm was used to predict response to immunotherapy. RESULTS: Based on machine learning approach, three prognosis-related IRLs (BARX1-DT, KLHL7-DT, and LINC02154) were selected to build an IRL classifier. The IRL classifier could availably classify patients into the low-risk and high-risk groups based on the different endpoints, including recurrence-free survival (RFS) and overall survival (OS). In terms of predictive ability and clinical utility, the IRL classifier was superior to other clinical characteristics. Encouragingly, similar results were observed in the GEO databases. Immune infiltration analysis displayed immune cells that are significantly richer in low-risk group, CD8 T cells and activated NK cells via CIBERSORTx algorithm as well as activated CD8 T cell via ssGSEA. Additionally, compared with the high-risk group, immune score, CD8 T effector was higher in the low-risk group, yet stromal score, score of p53 signaling pathway and TGFher in the Tx algorithm, was lower in the low-risk group. Corresponding results were confirmed in GEO dataset. Finally, TIDE analysis uncovered that the IRL classifier may be effectually predict the clinical response of immunotherapy in LSCC. CONCLUSION: Based on BARX1-DT, KLHL7-DT, and LINC02154, the IRL classifier was established, which can be used to predict the prognosis, immune infiltration status, and immunotherapy response in LSCC patients and might facilitate personalized counseling for immunotherapy.

Cancer germline antigen gene MAGEB2 promotes cell invasion and correlates with immune microenvironment and immunotherapeutic efficiency in laryngeal cancer.

By multiple transcriptome datasets (TCGA, GSE59102, GSE25727, GSE27020 and GSE65858) and multi-omics (RNA-seq, SNP, CNV, DNA methylation) in-depth analysis, we found that cancer germline antigen (CGA) family/genes MAGEB2 is involved in the imitation, progression and prognosis in LC as well as correlated positively with lymphatic metastasis and negatively with DNA methylation. Then, in vitro experiment verified that MAGEB2 expression renders significant alteration in LC tissues and cells via immunohistochemical (IHC), qRT-PCR and western blot (WB), and up-regulation of MAGEB2 expression could facilitate the proliferation, migration and invasion of LC cells and vice versa. Subsequently, MAGEB2 knockdown suppressed tumor growth and lung metastasis in vivo animal experiment, while MAGEB2 overexpression promoted tumor growth and lung metastasis. Lastly, MAGEB2 is significantly associated with immune cell infiltration (CD8+ T cells particularly, IHC staining confirmed that as the protein expression of MAGEB2 increased, the protein level of CD8 (representing tumor-infiltrating CD8 + T cells) decreased in vitro), immunomodulators (knockdown or overexpression of MAGEB2 on LC cell lines can significantly affect the chemokine/cytokine secretion in vitro), and immunogenicity(TMB) in LC, which hints that MAGEB2 is tightly correlated with immune characteristics and might guide more effective immunotherapy strategies for LC patients.

Construction of immune-related LncRNAs classifier to predict prognosis and immunotherapy response in thymic epithelial tumors.

The primary objective of this study was to construct an immune-related long noncoding RNAs (IRLs) classifier to precisely predict the prognosis and immunotherapy response of patients with thymic epithelial tumors (TET). Based on univariable Cox regression analysis and Lasso regression, six prognosis-related IRLs (AC004466.3, AC138207.2, AC148477.2, AL450270.1, HOXB-AS1 and SNHG8) were selected to build an IRL classifier. Importantly, results of qRT-PCR validated that higher expression levels of AC138207.2, AC148477.2, AL450270.1 and SNHG8 as well as lower expression levels of AC004466.3, and HOXB-AS1 in TETs samples compared with normal controls. The IRL classifier could effectively classify patients into the low-risk and high-risk groups based on the different survival parameters. In terms of predictive ability and clinical utility, the IRL classifier was superior to Masaoka staging system. Additionally, IRL classifier is significantly associated with immune cells infiltration (dendritic cells, activated CD4 memory T cells and tumor-infiltrating lymphocyte (TIL), T cell subsets in particular), immune microenvironment (immune score and immune checkpoint inhibitors) and immunogenicity (TMB) in TETs, which hints that IRL classifier is tightly correlated with immune characteristics and might guide more effective immunotherapy strategies for TETs patients. Encouragingly, according to TIDE algorithm, there were more immunotherapy responders in the low-risk IRL subgroup and the IRL score was robustly negatively linked to the immunotherapeutic response. To sum up, the IRL classifier was established, which can be used to predict the prognosis, immune infiltration status, immunotherapy response in TETs patients, and may facilitate personalized counseling for immunotherapy.

Characterization of immune-related genes andimmune infiltration features for early diagnosis, prognosis and recognition of immunosuppression in sepsis.

Among the body systems, the immune system plays a fundamental role in the pathophysiology of sepsis. The effects of immunogenomic and immune cell infiltration in sepsis were still not been systematically understood. Based on modified Lasso penalized regression and RF, 8 DEIRGs (ADM, CX3CR1, DEFA4, HLA-DPA1, MAPK14, ORM1, RETN, and SLPI) were combined to construct an IRG classifier. In the discovery cohort, IRG classifier exhibited superior diagnostic efficacy and performed better in predicting mortality than clinical characteristics or MARS/SRS endotypes. Encouragingly, similar results were observed in the ArrayExpress databases. The use of hydrocortisone in IRG high-risk subgroup was associated with increased risk of mortality. In IRG low-risk phenotypes, NK cells, T helper cells, and infiltrating lymphocyte (IL) are significantly richer, while T cells regulatory (Tregs) and myeloid-derived suppressor cells (MDSC) are more abundant in IRG high-risk phenotypes. IRG score were significantly negatively correlated with Cytokine cytokine receptor interaction (CCR) and human leukocyte antigen (HLA). Between the IRG subgroups, the expression levels of several cytokines (IL-10, IFNG, TNF) were significantly different, and IRG score was significantly positively correlated with ratio of IL-10/TNF. Results of qRT-PCR validated that higher expression level of ADM, DEFA4, MAPK14, ORM1, RETN, and SLPI as well as lower expression level of CX3CR1 and HLA-DPA1 in sepsis samples compared to control sample. A diagnostic and prognostic model, namely IRG classifier, was established based on 8 IRGs that is closely correlated with responses to hydrocortisone and immunosuppression status and might facilitate personalized counseling for specific therapy.

Discrete model studies of two grafted polyelectrolyte polymer hydrogels pressed in contact.

The interaction between two grafted polymer gels was investigated. We studied a defect-free network of diamond-like topology containing 8 tetra-functional nodes linked by 16 non-crossing chains. In order to explain the very low friction coefficient observed for polyelectrolyte hydrogels, we computed the monomer density profile of these polymer gels, the interpenetration between two polymer gels (defined as the percentage of monomers belonging to one gel which have penetrated the second gel), the normal force per unit area, and the radial distribution function of the interacting monomers. Low monomer density in the interface region separating the two gels and low interpenetration of the gels similar to that found in our simulations are likely to be responsible for the small friction coefficients observed for polyelectrolyte polymer gels.

Comparison of the kinetic friction of planar neutral and polyelectrolyte polymer brushes using molecular dynamics simulations.

We have simulated the relative shear motion of both neutral and polyelectrolyte end-grafted polymer brushes using molecular dynamics. The flexible neutral polymer brush is treated as a bead-spring model, and the polyelectrolyte brush is treated the same way except that each bead is charged and there are counterions present to neutralize the charge. We investigated the friction coefficient, monomer density, and brush penetration for both polyelectrolyte and neutral brushes with both equal grafting density and equal normal force under good solvent conditions. We found that polyelectrolyte brushes had a smaller friction coefficient and monomer penetration than neutral polymer brushes with the identical grafting density and chain length, and the polyelectrolyte brushes supported a much higher normal load than the neutral brushes for the same degree of compression. Charged and neutral brushes with their grafting densities chosen so that they support the same load exhibited approximately the same degree of interpenetration, but the polyelectrolyte brush exhibited a significantly lower friction coefficient. We present evidence that the reason for this is that the extra normal force contribution provided by the counterion osmotic pressure that exists for polyelectrolyte brushes permits them to support the same load as an identical neutral polymer brush of higher grafting density. Because of the resulting lower monomer density for the charged brushes, fewer monomer collisions take place per unit time, resulting in a lower friction coefficient.