Immune Landscape Research Articles

Immune checkpoints PD1/PDL1, TIM3/GAL9 and key immune mediators landscape reveal differential expression dynamics on imatinib response in chronic myeloid leukemia.

The immune system of chronic myeloid leukemia (CML) patients is severely impaired, hampering anti-tumor responses, and maximal immune recovery occurs after achieving deep molecular responses to tyrosine kinase inhibitors. This study aimed to discern the expression patterns of NCR2, IL2, IL4, EOMES, FOXP3, GATA3, RORGT, PD1/PDL1 and TIM3/GAL9, expanding our previous dataset up to 19 key immune mediators, during the initial year on imatinib. Gene expression dynamics were evaluated in 171 peripheral blood samples from 89 CML patients, including 43 longitudinally monitored individuals, and 52 healthy donors. Univariate and unsupervised analyses confirmed diminished expression of most studied immune mediators, except for TNF, ARG1 and IL4, differentiating between baseline and 3-month samples. Most of the studied mediators normalized along treatment, with a transient increase of TNF and IL6 levels at 3-months, especially in optimal responders (BCR::ABL1 < 0.1%). Univariate and multivariate analyses showed heightened ARG1 levels and a transition from PD1/PDL1 dominance at 3 months to TIM3/GAL9 at 12 months in non-optimal responders (BCR::ABL1 ≥ 0.1%). Our longitudinal design offers a deeper exploration of immune gene expression dynamics in CML patients on imatinib, highlighting its potential implications for therapy outcomes.

A novel hypoxia- and lactate metabolism-related prognostic signature to characterize the immune landscape and predict immunotherapy response in osteosarcoma

BackgroundImmunotherapy has shown considerable promise in cancer treatment, yet only a minority of osteosarcoma patients derive benefits from this approach. Hypoxia and lactate metabolism are two predominant characteristics of the tumor microenvironment. These features are crucial for molding the immune landscape and thus have the potential to act as predictive indicators for immunotherapy response.MethodsPrognostic modeled genes were identified through univariate and multivariate Cox regression as well as LASSO regression analyses. The tumor microenvironment was evaluated using ESTIMATE, CIBERSORT, and ImmuCellAI analyses. Tide prediction and expression of immune checkpoints, MHC molecules, chemokines, interleukins, interferons, receptors, and other cytokines were utilized to estimate immunotherapy efficacy. Single-cell analysis was performed to demonstrate the expression of modeled genes among various immune cell types. Experimental validation was carried out to verify the expression and functions of SFXN4 and SQOR.ResultsA potent signature was constructed with 8 genes related to hypoxia and lactate metabolism, including MAFF, COL5A2, FAM162A, SQOR, UQCRB, SFXN4, PFKFB2 and COX6A2. A nomogram incorporating risk scores and other clinical features demonstrated excellent predictive capacity. Osteosarcoma patients with high-risk scores exhibited poor prognosis and more “cold” tumor characteristics. According to the ESTIMATE algorithm, these patients displayed lower immune, stromal, and ESTIMATE scores, partially attributed to inadequate infiltration of key immunocytes. The Ciborsort analysis similarly indicated that high-risk individuals had diminished infiltration of critical anti-tumor immune cells such as Cytotoxic T cells, CD4+ T cells, and NK cells. The low expression levels of certain immune checkpoints, MHC molecules, chemokines, interleukins, interferons, receptors, and other cytokines in high-risk cases suggested their unsatisfactory responses to immune treatment. Tide prediction further demonstrated that fewer individuals classified as high risk may exhibit sensitivity to immune checkpoint inhibitor therapy. Notably, SFXN4 was found to be highly expressed in osteosarcoma tissues and cells; it promoted the growth, migration, and invasion of osteosarcoma cells, while SQOR had the opposite effect.ConclusionOur research has developed a robust hypoxia- and lactate metabolism-related gene signature, providing a solid theoretical foundation for prognosis prediction, classification of “cold” and “hot” tumors, accessing immunotherapy response, and directing personalized treatment for osteosarcoma.

Multi-omics reveal immune microenvironment alterations in multiple myeloma and its precursor stages.

Tumor immune microenvironmental alterations occur early in multiple myeloma (MM) development. In this study, we aim to systematically characterize the tumor immune microenvironment (TME) and the tumor-immune interactions from precursor stages, i.e., monoclonal gammopathy of undetermined significance (MGUS) and smoldering MM (SMM), to newly diagnosed MM, comparing these to healthy donors. Using CIBERSORT, mass cytometry (CyTOF), and single-cell RNA sequencing (scRNA-Seq), we examined innate and adaptive immune changes across these stages. We found a decrease in granulocytes in the TME predicts MM outcomes. HLA-DR is reduced in CD16+ monocytes and plasmacytoid dendritic cells, while myeloid dendritic cells show decreased expression of stress and immune-response genes. NK cells and CD8+ T cells shift from a GZMK+ to a GZMB+ cytotoxic phenotype in the TME, with increased inhibitory markers TIM3 and TIGIT. In paired samples, the proportion and gene expression pattern in patient-specific GZMB+CD8+ T cells remain largely unchanged despite MM progression. Our findings provide a comprehensive immune landscape of MM and its precursors, offering insights into therapeutic strategies. Enhancing neutrophil and NK cell cytotoxicity, tumor antigen presentation, and CD8+ T cell versatility in precursor stages may prevent MM progression.

Systematic multiomics analysis and in vitro experiments suggest that ITGA5 could serve as a promising therapeutic target for ccRCC

BackgroundIntegrin alpha 5 (ITGA5) was previously confirmed to be related to prognosis in several cancer types; however, its function in clear cell renal cell carcinoma (ccRCC) and how this molecule regulates tumor progression and the tumor microenvironment (TME) remain to be elucidated.MethodsWe investigated the prognostic implications of ITGA5 with a machine learning model and evaluated biological behaviors of different levels of ITGA5 expression in vitro. Bioinformatic analysis was performed to explain the comprehensive effect of ITGA5 on the TME and drug sensitivity.ResultsWe constructed a machine learning model to elaborate the prognostic implication of ITGA5. As tumorigenesis of ccRCC was tightly relevant with several mutant genes, we investigated the correlation between ITGA5 expression and frequent mutations and found ITGA5 upregulation in VHL mutant ccRCC (P = 0.016). Through overexpressing, silencing, and blocking ITGA5, we verified the role of ITGA5 in promoting ccRCC adverse biological activities; and the potential functions of ITGA5 in ccRCC were bioinformatically demonstrated, summarizing as cell proliferation, migration, and angiogenesis. The localization of ITGA5 primarily in endothelia and macrophages further verified its magnitude in angiogenesis and aroused our excavation in ITGA5 regulation of immune infiltration landscape. Generally, ITGA5-high ccRCC presented an immunosuppressive TME by inducing a lower level of CD8 + T cell infiltration. For the last part we predicted drug sensitivity relevant to ITGA5 and concluded that a joint medication of ITGA5 inhibitors and VEGFR-target drugs (including sunitinib, axitinib, pazopanib, and motesanib) might be a promising therapeutic strategy.ConclusionOur findings clarified the adverse outcome induced by high expression of ITGA5 in ccRCC patients. In vitro experiments and bioinformatical analysis identified ITGA5 function as predominantly cell proliferation, migration, angiogenesis, and macrophage recruitment. Further, we predicted immune infiltration and medication sensitivity regulation by ITGA5 and proposed a joint use of ITGA5 inhibitors and anti-angiogenetic drugs as a potential potent therapeutic strategy.

Navigating the immune landscape with plasma cells: A pan-cancer signature for precision immunotherapy.

Immunotherapy has revolutionized cancer treatment; however, predicting patient response remains a significant challenge. Our study identified a novel plasma cell signature, Plasma cell.Sig, through a pan-cancer single-cell RNA sequencing analysis, which predicts patient outcomes to immunotherapy with remarkable accuracy. The signature was developed using rigorous machine learning algorithms and validated across multiple cohorts, demonstrating superior predictive power with an area under the curve (AUC) exceeding 0.7. Notably, the low-risk group, as classified by Plasma cell.Sig, exhibited enriched immune cell infiltration and heightened tumor immunogenicity, indicating an enhanced responsiveness to immunotherapy. Conversely, the high-risk group showed reduced immune activity and potential mechanisms of immune evasion. These findings not only enhance understanding of the intrinsic and extrinsic immune landscapes within the tumor microenvironment but also pave the way for more precise, biomarker-guided immunotherapy approaches in oncology.

Comprehensive Analysis and Verification of the Prognostic Significance of Cuproptosis-Related Genes in Colon Adenocarcinoma

Colon adenocarcinoma (COAD) is a frequently occurring and lethal cancer. Cuproptosis is an emerging type of cell death, and the underlying pathways involved in this process in COAD remain poorly understood. Transcriptomic and clinical data for COAD patients were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. We investigated alterations in DNA and chromatin of cuproptosis-related genes (CRGs) in COAD. In order to identify predictive differentially expressed genes (DEGs) and various molecular subtypes, we used consensus cluster analysis. Through univariate, multivariate, and Lasso Cox regression analyses, four CRGs were identified. A risk prognostic model for cuproptosis characteristics was constructed based on four CRGs. This study also examined the association between the risk score and the tumor microenvironment (TME), the immune landscape, and drug sensitivity. We distinguished two unique molecular subtypes using consensus clustering analysis. We discovered that the clinical characteristics, prognosis, and TME cell infiltration characteristics of patients with multilayer CRG subtypes were all connected. The internal and external evaluations of the predicted accuracy of the prognostic model built using data derived from a cuproptosis risk score were completed at the same time. A nomogram and a clinical pathological analysis make it more useful in the field of medicine. A significant rise in immunosuppressive cells was observed in the high cuproptosis risk score group, with a correlation identified between the cuproptosis risk score and immune cell infiltration. Despite generally poor prognoses, the patients with a high cuproptosis risk but low tumor mutation burden (TMB), cancer stem cell (CSC) index, or microsatellite instability (MSI) may still benefit from immunotherapy. Furthermore, the cuproptosis risk score positively correlated with immune checkpoint gene expression. Analyzing the potential sensitivity to medications could aid in the development of clinical chemotherapy regimens and decision-making. CRGs are the subject of our in-depth study, which exposed an array of regulatory mechanisms impacting TME. In addition, we performed additional data mining into clinical features, prognosis effectiveness, and possible treatment medications. COAD’s molecular pathways will be better understood, leading to more precise treatment options.

Obesity uncovers presence of inflammatory lung macrophage subsets with adipose tissue transcriptomic signature in influenza virus infection.

Obesity is a risk factor for increased lung damage and disease severity during influenza virus infection. White adipose tissue (WAT) inflammation is a key driver of disease pathogenesis in obesity. Whether and how obesity modifies lung and WAT immune cell character and function in obesity to amplify influenza disease severity remains unknown. We show that obesity establishes a proinflammatory transcriptome in lung immune cells that is further augmented upon influenza virus infection. Unexpectedly, we also show that influenza virus infection induces expression of inflammatory genes in visceral WAT and modifies WAT immune cell milieu in obesity. Notably, a decrease in WAT macrophage (ATM) populations inversely correlates to increase in infiltrating lung macrophage numbers in obese influenza virus-infected mice. Comparison of both lung and WAT immune cell transcriptional landscapes uncovers a presence of a macrophage subset in the lungs whose transcriptomic signatures matched those of an inflammatory ATM subset preferentially found in obese mice. Adoptive transfer of ATMs from obese mice into lean influenza-virus infected mice promotes host immune cell infiltration to the lungs. Together, our novel findings provide evidence of immune cell transcriptome and character changes in the lungs and WAT of influenza virus infected obese, but not lean, mice and suggest that visceral ATMs may contribute to the overall inflammatory milieu in this setting.

Retraction Note: Immune landscape of periodontitis unveils alterations of infiltrating immunocytes and molecular networks-aggregating into an interactive web-tool for periodontitis related immune analysis and visualization

Retraction Note: Immune landscape of periodontitis unveils alterations of infiltrating immunocytes and molecular networks-aggregating into an interactive web-tool for periodontitis related immune analysis and visualization

Cryoablation synergizes with anti-PD-1 immunotherapy induces an effective abscopal effect in murine model of cervical cancer

BackgroundImmune checkpoint inhibitors (ICIs), especially anti-PD-1/PD-L1 antibodies, have emerged as promising therapeutic options for cervical cancer. However, the efficacy of anti-PD-1 antibody monotherapy is limited. Cryoablation could elicit an anti-tumor immune response, thereby presenting itself as a potential approach to augment the response of ICIs. The aim of our study was to investigate the systemic immunological effects of cryoablation and the potential synergistic anti-tumor effects of cryoablation and anti-PD-1 antibody in cervical cancer. MethodsWe established U14 murine bilateral subcutaneous cervical cancer model, wherein the primary tumors were treated with cryoablation. Flow cytometry, immunohistochemistry and RNA-seq were used to analyze the immune cell infiltration and immune-associated pathways in the secondary tumor. ResultsOur study revealed that cryoablation reprogrammed the immune landscape, leading to an enhanced infiltration of CD8+ T cell in distant tumors. Cryoablation created a conducive environment for increasing the efficacy of anti-PD-1 immunotherapy. Cryoablation in combination with anti-PD-1 antibody inhibited distant tumors growth and improved mouse survival. Mechanistically, this combination therapy could augment the infiltration of CD8+ T cells, CD4+ T cells, dendritic cells and M1-like tumor-associated macrophages, enhance multiple aspects of antitumor immune response, and reduce immunosuppressive cells such as M2-like tumor-associated macrophages and myeloid-derived suppressor cells in distant tumors. ConclusionsCombination therapy with cryoablation and anti-PD-1 antibody induces an effective abscopal effect in murine model of cervical cancer and may be a novel therapeutic approach for patients with advanced/recurrent cervical cancer.

Sex-Based Differences in Thyroid Plasma B Cell Infiltration: Implications for Autoimmune Disease Susceptibility.

Thyroid autoimmune diseases, such as Hashimoto's thyroiditis and Graves' disease, are significantly more prevalent in women than in men, suggesting underlying biological differences in immune system function and regulation between sexes. Plasma B cells are crucial in autoimmunity due to their role in producing antibodies targeting self-antigens, but their presence in the thyroids of women without clinical autoimmune diseases remains largely unexplored. This study investigates the infiltration of plasma B cells in female thyroids specifically excluding those with any clinical signs of autoimmune diseases. Using bulk RNA-seq analysis, we identified significant sex differences in gene expression profiles, particularly in genes associated with plasma B cells. Single-cell RNA-seq and spatial transcriptomic analyses further revealed that the CXCL13-CXCR5 signaling axis plays a pivotal role in recruiting and organizing plasma B cells within the thyroid tissue. These findings suggest that the inherent presence of plasma B cells in the female thyroid, driven by CXCL13, may contribute to the higher risk of developing autoimmune thyroid diseases in women. Our study provides new insights into the immune landscape of the thyroid and underscores the importance of understanding sex-specific differences in immune cell distribution and function.

The imprint of viral oncoproteins on the variable clinical behavior among human papilloma virus-related oropharyngeal squamous cell carcinomas

Human papilloma virus-related (HPV+) oropharyngeal squamous cell carcinomas (OPSCCs) are variable in their progression, immune landscape, treatment responses, and clinical outcomes. Their behavior is impacted not only by differences in host genomic alterations but also by diversity in levels and activity of HPV-encoded oncoproteins. Striking differences in HPV mRNA levels are found among HPV+ OPSCCs and likely derive in part from variations in the structurally diverse mix of integrated and episomal HPV genomes they often contain. Viral oncoprotein levels and function are also impacted by differential splicing of the two long polycistronic transcripts of HPV16, the HPV type within most HPV+ OPSCCs. Further variation in viral oncoprotein function arises from the distinct lineages and sub-lineages of HPV16, which encode polymorphisms in functionally important portions of oncogenes. Here we review the limited current knowledge linking HPV mRNA expression and splicing to differences in oncoprotein function that likely influence OPSCC behavior. We also summarize the evolving understanding of HPV16 physical genome state and genetic variants and their potential contributions to HPV oncoprotein levels and function. Addressing considerable remaining challenges in defining the quantitative and qualitative imprint of HPV oncoproteins on each OPSCC holds promise to guide personalization of therapy for this disease.

The immune landscape in apical periodontitis: From mechanism to therapy.

Apical periodontitis (AP) is featured by a persistent inflammatory response and alveolar bone resorption initiated by microorganisms, posing risks to both dental and systemic health. Nonsurgical endodontic treatment is the recommended treatment plan for AP with a high success rate, but in some cases, periapical lesions may persist despite standard endodontic treatment. Better comprehension of the AP inflammatory microenvironment can help develop adjunct therapies to improve the outcome of endodontic treatment. This review presents an overview of the immune landscape in AP, elucidating how microbial invasion triggers host immune activation and shapes the inflammatory microenvironment, ultimately impacting bone homeostasis. The destructive effect of excessive immune activation on periapical tissues is emphasized. This review aimed to systematically discuss the immunological basis of AP, the inflammatory bone resorption and the immune cell network in AP, thereby providing insights into potential immunotherapeutic strategies such as targeted therapy, antioxidant therapy, adoptive cell therapy and cytokine therapy to mitigate AP-associated tissue destruction.

A ferroptosis-associated prognostic model correlated with immune landscape and radiotherapy response in low-grade gliomas (LGGs)

Despite receiving comprehensive treatment, the prognosis for low-grade gliomas (LGGs) patients varies considerably. Recent studies have focused extensively on ferroptosis, across a range of tumor types. Nevertheless, methodologies to evaluate the efficacy of radiotherapy for LGGs, from the perspective of ferroptosis-related genes (FRGs), remain strikingly rare. In this study, we conducted a retrospective study on the transcriptional profiles of LGG patients from the public databases and a local cohort. An FRG model was developed and validated, exhibits heightened robustness when contrasted with the traditional ssGSEA model. Patients demonstrating higher FRG scores were identified as a high-risk group, displaying a worse prognosis. By incorporating the FRG score alongside other prognosis-associated clinical indicators, we formulated an enhanced nomogram to achieve a higher level of prediction performance. Additionally, among LGG patients receiving radiotherapy, a poorer prognosis was observed in the high-risk group. Further investigation revealed that samples from the high-risk group generally exhibit a TME in an immuno-suppressive state. Collectively, we developed an FRG model and a robust nomogram for LGG prognostication. This study suggests that a high FRG score, indicative of an immunosuppressive TME, could potentially lead to a less favorable prognosis for certain LGG patients receiving radiotherapy.

Modelling plausible scenarios for the Omicron SARS-CoV-2 variant from early-stage surveillance

We used a spatially explicit agent-based model of SARS-CoV-2 transmission combined with spatially fine-grained COVID-19 observation data from Public Health Scotland to investigate the initial rise of the Omicron (BA.1) variant of concern. We evaluated plausible scenarios for transmission rate advantage and vaccine immune escape relative to the Delta variant based on the data that would have been available at that time. We also explored possible outcomes of different levels of imposed non-pharmaceutical intervention. The initial results of these scenarios were used to inform the Scottish Government in the early outbreak stages of the Omicron variant.Using the model with parameters fit over the Delta variant epidemic, some initial assumptions about Omicron transmission rate advantage and vaccine escape, and a simple growth rate fitting procedure, we were able to capture the initial outbreak dynamics for Omicron. We found that the modelled dynamics hold up to retrospective scrutiny. The modelled imposition of extra non-pharmaceutical interventions planned by the Scottish Government at the time would likely have little effect in light of the transmission rate advantage held by the Omicron variant and the fact that the planned interventions would have occurred too late in the outbreak’s trajectory. Finally, we found that any assumptions made about the projected distribution of vaccines in the model population had little bearing on the outcome, in terms of outbreak size and timing. Instead, it was the landscape of prior immunity that was most important.

Co-mutation of TP53 and TTN is Correlated with the Efficacy of Immunotherapy in Lung Squamous Cell Carcinoma.

Immunotherapy has been a promising treatment in advanced lung cancer. However, only a few patients could benefit from it. Herein, we aimed to explore mutationrelated predictive biomarkers in lung squamous cell carcinoma (LUSC), which could help develop clinical immunotherapy strategies and screen beneficial populations. Co-occurrence and mutually exclusive analysis was conducted on the TCGA-LUSC cohort. Correlations between the gene mutation status and tumor mutation burden (TMB) levels, and neo-antigen levels were analyzed by Wilcoxon test. Kaplan-Meier method was employed to analyze the progression-free survival (PFS) of lung cancer patients with immunotherapy. Gene set enrichment analysis (GSEA) was used to investigate the functional changes affected by TP53mut/TTNmut. The immune cell infiltration landscape in co-mutation subgroups was analyzed using CIBERSORT. 1) TP53, TTN, CSMD3, MUC16, RYR2, LRP1B, USH2A, SYNE1, ZFHX4, FAM135B, KMT2D, and NAV3 were frequently mutated in LUSC patients. 2) TMB levels in highly mutated groups were higher than that in wild type groups. 3) There were higher neoantigen levels in mutation group compared to the wild-type group, and LUSC patients in mutation group had longer PFS. 4) TP53mut/TTNmut co-mutation group exhibited higher TMB levels and better response to immunotherapy. 5) A host of immune-related signaling pathways was inhibited in TP53mut/TTNmut subgroup. 6) There were more T follicular helper cells and NK cells were in TP53mut/TTNmut subgroup than in the WT subgroup. The LUSC patients with TP53 and TTN co-mutation had higher TMB levels and better response to immunotherapy. The TP53 and TTN co-mutation is a promising novel biomarker to assist LUSC immunotherapy evaluation.

A Novel Gene Signature Based on Immune Cell Infiltration Landscape Predicts Prognosis in Lung Adenocarcinoma Patients.

The tumor microenvironment (TME) is created by the tumor and dominated by tumor-induced interactions. Long-term survival of lung adenocarcinoma (LUAD) patients is strongly influenced by immune cell infiltration in TME. The current article intends to construct a gene signature from LUAD ICI for predicting patient outcomes. For the initial phase of the study, the TCGA-LUAD dataset was chosen as the training group for dataset selection. We found two datasets named GSE72094 and GSE68465 in the Gene Expression Omnibus (GEO) database for model validation. Unsupervised clustering was performed on the training cohort patients using the ICI profiles. We employed Kaplan-Meier estimators and univariate Cox proportional-hazard models to identify prognostic differentially expressed genes in immune cell infiltration (ICI) clusters. These prognostic genes are then used to develop a LASSO Cox model that generates a prognostic gene signature. Validation was performed using Kaplan-Meier estimation, Cox, and ROC analysis. Our signature and vital immune-relevant signatures were analyzed. Finally, we performed gene set enrichment analysis (GSEA) and immune infiltration analysis on our finding gene signature to further examine the functional mechanisms and immune cellular interactions. Our study found a sixteen-gene signature (EREG, HPGDS, TSPAN32, ACSM5, SFTPD, SCN7A, CCR2, S100P, KLK12, MS4A1, INHA, HOXB9, CYP4B1, SPOCK1, STAP1, and ACAP1) to be prognostic based on data from the training cohort. This prognostic signature was certified by Kaplan-Meier, Cox proportional-hazards, and ROC curves. 11/15 immune-relevant signatures were related to our signature. The GSEA results indicated our gene signature strongly correlates with immune-related pathways. Based on the immune infiltration analysis findings, it can be deduced that a significant portion of the prognostic significance of the signature can be attributed to resting mast cells. We used bioinformatics to determine a new, robust sixteen-gene signature. We also found that this signature's prognostic ability was closely related to the resting mast cell infiltration of LUAD patients.

Navigating the Roadblocks: Progress and Challenges in Cell-Based Therapies for Human Immunodeficiency Virus.

Cell-based therapies represent a major advancement in the treatment and management of HIV/AIDS, with a goal to overcome the limitations of traditional antiretroviral therapy (ART). These innovative approaches not only promise a functional cure by reconstructing the immune landscape but also address the persistent viral reservoirs. For example, stem cell therapies have emerged from the foundational success of allogeneic hematopoietic stem cell transplantation in curing HIV infection in a limited number of cases. B cell therapies make use of genetically modified B cells constitutively expressing broadly neutralizing antibodies (bNAbs) against target viral particles and infected cells. Adoptive cell transfer (ACT), including TCR-T therapy, CAR-T cells, NK-CAR cells, and DC-based therapy, is adapted from cancer immunotherapy and repurposed for HIV eradication. In this review, we summarize the mechanisms through which these engineered cells recognize and destroy HIV-infected cells, the modification strategies, and their role in sustaining remission in the absence of ART. The review also addresses the challenges to cell-based therapies against HIV and discusses the recent advancements aimed at overcoming them.

Development and validation of preeclampsia predictive models using key genes from bioinformatics and machine learning approaches

BackgroundPreeclampsia (PE) poses significant diagnostic and therapeutic challenges. This study aims to identify novel genes for potential diagnostic and therapeutic targets, illuminating the immune mechanisms involved.MethodsThree GEO datasets were analyzed, merging two for training set, and using the third for external validation. Intersection analysis of differentially expressed genes (DEGs) and WGCNA highlighted candidate genes. These were further refined through LASSO, SVM-RFE, and RF algorithms to identify diagnostic hub genes. Diagnostic efficacy was assessed using ROC curves. A predictive nomogram and fully Connected Neural Network (FCNN) were developed for PE prediction. ssGSEA and correlation analysis were employed to investigate the immune landscape. Further validation was provided by qRT-PCR on human placental samples.ResultFive biomarkers were identified with validation AUCs: CGB5 (0.663, 95% CI: 0.577-0.750), LEP (0.850, 95% CI: 0.792-0.908), LRRC1 (0.797, 95% CI: 0.728-0.867), PAPPA2 (0.839, 95% CI: 0.775-0.902), and SLC20A1 (0.811, 95% CI: 0.742-0.880), all of which are involved in key biological processes. The nomogram showed strong predictive power (C-index 0.873), while FCNN achieved an optimal AUC of 0.911 (95% CI: 0.732-1.000) in five-fold cross-validation. Immune infiltration analysis revealed the importance of T cell subsets, neutrophils, and NK cells in PE, linking these genes to immune mechanisms underlying PE pathogenesis.ConclusionCGB5, LEP, LRRC1, PAPPA2, and SLC20A1 are validated as key diagnostic biomarkers for PE. Nomogram and FCNN could credibly predict PE. Their association with immune infiltration underscores the crucial role of immune responses in PE pathogenesis.

Decoding marker genes and immune landscape of unstable carotid plaques from cellular senescence

Recently, cellular senescence-induced unstable carotid plaques have gained increasing attention. In this study, we utilized bioinformatics and machine learning methods to investigate the correlation between cellular senescence and the pathological mechanisms of unstable carotid plaques. Our aim was to elucidate the causes of unstable carotid plaque progression and identify new therapeutic strategies. First, differential expression analysis was performed on the test set GSE43292 to identify differentially expressed genes (DEGs) between the unstable plaque group and the control group. These DEGs were intersected with cellular senescence-associated genes to obtain 40 cellular senescence-associated DEGs. Subsequently, key genes were then identified through weighted gene co-expression network analysis, random forest, Recursive Feature Elimination for Support Vector Machines algorithm and cytoHubba plugin. The intersection yielded 3 CSA-signature genes, which were validated in the external validation set GSE163154. Additionally, we assessed the relationship between these CSA-signature genes and the immune landscape of the unstable plaque group. This study suggests that cellular senescence may play an important role in the progression mechanism of unstable plaques and is closely related to the influence of the immune microenvironment. Our research lays the foundation for studying the progression mechanism of unstable carotid plaques and provides some reference for targeted therapy.

Discovery of PANoptosis-related signatures correlates with immune cell infiltration in psoriasis.

Psoriasis is an inflammatory skin disease that relapses frequently. Keratinocyte apoptosis dysregulation plays a crucial role in the pathological mechanisms of psoriasis. PANoptosis is a process with intermolecular interaction among pyroptosis, apoptosis, and necroptosis. The mechanism of PANoptosis in the occurrence and development of psoriasis is still unclear. Here we present a novel approach by identifying PANoptosis-related signatures (PANoptosis-sig) from skin tissue of psoriasis patients and healthy controls on transcriptional and protein levels. Five PANoptosis-sig (TYMP, S100A8, S100A9, NAMPT, LCN2) were identified. Enrichment analysis showed they were mainly enriched in response to leukocyte aggregation, leukocyte migration, chronic inflammatory response and IL-17 signaling pathway. Single cell transcriptome analysis showed TYMP and NAMPT were expressed in almost all cell populations, while LCN2, S100A8 and S100A9 were significantly highly expressed in keratinocyte. We then constructed predictive and diagnostic models with the PANoptosis-sig and evaluated their performance. Finally, unsupervised consensus clustering analysis was conducted to ascertain psoriasis molecular subtypes by the PANoptosis-sig. The psoriasis cohort was divided into two distinct subtypes. Immune landscape showed that the stromal score of cluster 1 was significantly higher than cluster 2, while the immune and estimate scores of cluster 2 were expressively higher than cluster 1. Cluster 1 exhibited high expression of Plasma cells, Tregs and Mast cells resting, while cluster 2 showed high expression of T cells, Macrophages M1, Dendritic cells activated, and Neutrophils in immune infiltration analysis. And cluster 2 was more sensitive to immune checkpoints. In conclusion, our findings revealed potential biomarkers and therapeutic targets for the prevention, diagnosis, and treatment of psoriasis, enhancing our understanding of the molecular mechanisms underlying PANoptosis.