Immune Microenvironment Landscape Research Articles

Loss of p53 and SMAD4 induces adenosquamous subtype pancreatic cancer in the absence of an oncogenic KRAS mutation

Pancreatic cancer is associated with an oncogenic KRAS mutation in approximately 90% of cases. However, a non-negligible proportion of pancreatic cancer cases harbor wild-type KRAS (KRAS-WT). This study establishes genetically engineered mouse models that develop spontaneous pancreatic cancer in the context of KRAS-WT. The Trp53loxP/loxP;Smad4loxP/loxP;Pdx1-Cre (PPSSC) mouse model harbors KRAS-WT and loss of Trp53/Smad4. The Trp53loxP/loxP;Tgfbr2loxP/loxP;Pdx1-Cre (PPTTC) mouse model harbors KRAS-WT and loss of Trp53/Tgfbr2. We identify that either Trp53/Smad4 loss or Trp53/Tgfbr2 loss can induce spontaneous pancreatic tumor formation in the absence of an oncogenic KRAS mutation. The Trp53/Smad4 loss and Trp53/Tgfbr2 loss mouse models exhibit distinct pancreatic tumor histological features, as compared to oncogenic KRAS-driven mouse models. Furthermore, KRAS-WT pancreatic tumors with Trp53/Smad4 loss reveal unique histological features of pancreatic adenosquamous carcinoma (PASC). Single-cell RNA sequencing (scRNA-seq) analysis reveals the distinct tumor immune microenvironment landscape of KRAS-WT (PPSSC) pancreatic tumors as compared with that of oncogenic KRAS-driven pancreatic tumors.

Remodeling the hepatic immune microenvironment and demolishing T cell traps to enhance immunotherapy efficacy in liver metastasis

Immune checkpoint inhibitors (ICIs) exhibit compromised therapeutic efficacy in many patients with advanced cancers, particularly those with liver metastases. Much of this incapability can be ascribed as an irresponsiveness resulting from the “cold” hepatic tumor microenvironment that acts as T cell “traps” for which there currently lack countermeasures. We report a novel nanomedicine that converts the hepatic immune microenvironment to a “hot” phenotype by targeting hepatic macrophage-centric T cell elimination. Using the nanomedicine, composed of KIRA6 (an endothelium reticulum stress inhibitor), α-Tocopherol nanoemulsions, and anti-PD1 antibodies, we found its potency in murine models of orthotopic colorectal tumors and hepatic metastases, restoring immune responses and enhancing anti-tumor effects. A post-treatment scrutiny of the immune microenvironment landscape in the liver reveals repolarization of immunosuppressive hepatic macrophages, upregulation of Th1-like effector CD4+ T cells, and rejuvenation of dendritic cells along with CD8+ T cells. These findings suggest adaptations of liver-centric immune milieu modulation strategies to improve the efficacy of ICIs for a variety of “cold” tumors and their liver metastases.

Machine learning-based biomarker screening for acute myeloid leukemia prognosis and therapy from diverse cell-death patterns

Acute myeloid leukemia (AML) exhibits pronounced heterogeneity and chemotherapy resistance. Aberrant programmed cell death (PCD) implicated in AML pathogenesis suggests PCD-related signatures could serve as biomarkers to predict clinical outcomes and drug response. We utilized 13 PCD pathways, including apoptosis, pyroptosis, ferroptosis, autophagy, necroptosis, cuproptosis, parthanatos, entotic cell death, netotic cell death, lysosome-dependent cell death, alkaliptosis, oxeiptosis, and disulfidptosis to develop predictive models based on 73 machine learning combinations from 10 algorithms. Bulk RNA-sequencing, single-cell RNA-sequencing transcriptomic data, and matched clinicopathological information were obtained from the TCGA-AML, Tyner, and GSE37642-GPL96 cohorts. These datasets were leveraged to construct and validate the models. Additionally, in vitro experiments were conducted to substantiate the bioinformatics findings. The machine learning approach established a 6-gene pan-programmed cell death-related genes index (PPCDI) signature. Validation in two external cohorts showed high PPCDI associated with worse prognosis in AML patients. Incorporating PPCDI with clinical variables, we constructed several robust prognostic nomograms that accurately predicted prognosis of AML patients. Multi-omics analysis integrating bulk and single-cell transcriptomics revealed correlations between PPCDI and immunological features, delineating the immune microenvironment landscape in AML. Patients with high PPCDI exhibited resistance to conventional chemotherapy like doxorubicin but retained sensitivity to dasatinib and methotrexate (FDA-approved drugs for other leukemias), suggesting the potential of PPCDI to guide personalized therapy selection in AML. In summary, we developed a novel PPCDI model through comprehensive analysis of diverse programmed cell death pathways. This PPCDI signature demonstrates great potential in predicting clinical prognosis and drug sensitivity phenotypes in AML patients.

Single-cell hdWGCNA reveals metastatic protective macrophages and development of deep learning model in uveal melanoma

BackgroundAlthough there has been some progress in the treatment of primary uveal melanoma (UVM), distant metastasis remains the leading cause of death in patients. Monitoring, staging, and treatment of metastatic disease have not yet reached consensus. Although more than half of metastatic tumors (62%) are diagnosed within five years after primary tumor treatment, the remainder are only detected in the following 25 years. The mechanisms of UVM metastasis and its impact on prognosis are not yet fully understood.MethodsscRNA-seq data of UVM samples were obtained and processed, followed by cell type identification and characterization of macrophage subpopulations. High-dimensional weighted gene co-expression network analysis (HdWGCNA) was performed to identify key gene modules associated with metastatic protective macrophages (MPMφ) in primary samples, and functional analyses were conducted. Non-negative matrix factorization (NMF) clustering and immune cell infiltration analyses were performed using the MPMφ gene signatures. Machine learning models were developed using the identified metastatic protective macrophages related genes (MPMRGs) to distinguish primary from metastatic patients. A deep learning convolutional neural network (CNN) model was constructed based on MPMRGs and cell type associations. Lastly, a prognostic model was established using the MPMRGs and validated in independent cohorts.ResultsSingle-cell RNA-seq analysis revealed a unique immune microenvironment landscape in primary samples compared to metastatic samples, with an enrichment of macrophage cells. Using HdWGCNA, MPMφ and marker genes were identified. Functional analysis showed an enrichment of genes related to antigen processing progress and immune response. Machine learning and deep learning models based on key genes showed significant effectiveness in distinguishing between primary and metastatic patients. The prognostic model based on key genes demonstrated substantial predictive value for the survival of UVM patients.ConclusionOur study identified key macrophage subpopulations related to metastatic samples, which have a profound impact on shaping the tumor immune microenvironment. A prognostic model based on macrophage cell genes can be used to predict the prognosis of UVM patients.

The Immune Microenvironment Landscape of Pituitary NeuroEndocrine Tumors, a Transcriptomic Approach.

Pituitary neuroendocrine tumors (PitNET) are known to be variably infiltrated by different immune cells. Nonetheless, their role in pituitary oncogenesis has only begun to be unveiled. The immune microenvironment could determine the biological and clinical behavior of a neoplasm and may have prognostic implications. To evaluate the expression of immune-related genes and to correlate such expression with the presence of infiltrating immune cells in forty-two PitNETs of different lineages, we performed whole transcriptome analysis and RT-qPCR. Deconvolution analysis was carried out to infer the immune cell types present in each tumor and the presence of immune cells was confirmed by immunofluorescence. We found characteristic expression profiles of immune-related genes including those encoding interleukins and chemokines for each tumor lineage. Genes such as IL4-I1, IL-36A, TIRAP, IL-17REL, and CCL5 were upregulated in all PitNETS, whereas IL34, IL20RA, and IL-2RB characterize the NR5A1-, TBX19-, and POU1F1-derived tumors, respectively. Transcriptome deconvolution analysis showed that M2 macrophages, CD4+ T cells, CD8+ T cells, NK cells, and neutrophils can potentially infiltrate PitNET. Furthermore, CD4+ and CD8+ T cells and NK cells infiltration was validated by immunofluorescence. Expression of CCL18, IL-5RA, and HLA-B as well as macrophage tumor infiltration could identify patients who can potentially benefit from treatment with immune checkpoint inhibitors.

Machine learning to establish three sphingolipid metabolism genes signature to characterize the immune landscape and prognosis of patients with gastric cancer

BackgroundGastric cancer (GC) is one of the most common malignant tumors worldwide. Nevertheless, GC still lacks effective diagnosed and monitoring method and treating targets. This study used multi omics data to explore novel biomarkers and immune therapy targets around sphingolipids metabolism genes (SMGs).MethodLASSO regression analysis was performed to filter prognostic and differently expression SMGs among TCGA and GTEx data. Risk score model and Kaplan-Meier were built to validate the prognostic SMG signature and prognostic nomogram was further constructed. The biological functions of SMG signature were annotated via multi omics. The heterogeneity landscape of immune microenvironment in GC was explored. qRT-PCR was performed to validate the expression level of SMG signature. Competing endogenous RNA regulatory network was established to explore the molecular regulatory mechanisms.Result3-SMGs prognostic signature (GLA, LAMC1, TRAF2) and related nomogram were constructed combing several clinical characterizes. The expression difference and diagnostic value were validated by PCR data. Multi omics data reveals 3-SMG signature affects cell cycle and death via several signaling pathways to regulate GC progression. Overexpression of 3-SMG signature influenced various immune cell infiltration in GC microenvironment. RBP-SMGs-miRNA-mRNAs/lncRNAs regulatory network was built to annotate regulatory system.ConclusionUpregulated 3-SMGs signature are excellent predictive diagnosed and prognostic biomarkers, providing a new perspective for future GC immunotherapy.

Construction of S100 family members prognosis prediction model and analysis of immune microenvironment landscape at single-cell level in pancreatic adenocarcinoma: a tumor marker prognostic study.

Pancreatic adenocarcinoma characterized by a mere 10% 5-year survival rate, poses a formidable challenge due to its specific anatomical location, making tumor tissue acquisition difficult. This limitation underscores the critical need for novel biomarkers to stratify this patient population. Accordingly, this study aimed to construct a prognosis prediction model centered on S100 family members. Leveraging six S100 genes and their corresponding coefficients, an S100 score was calculated to predict survival outcomes. The present study provided comprehensive internal and external validation along with power evaluation results, substantiating the efficacy of the proposed model. Additionally, the study explored the S100-driven potential mechanisms underlying malignant progression. By comparing immune cell infiltration proportions in distinct patient groups with varying prognoses, the research identified differences driven by S100 expression. Furthermore, the analysis explored significant ligand-receptor pairs between malignant cells and immune cells influenced by S100 genes, uncovering crucial insights. Notably, the study identified a novel biomarker capable of predicting the sensitivity of neoadjuvant chemotherapy, offering promising avenues for further research and clinical application.

Single-cell atlas reveals the immunosuppressive microenvironment and Treg cells landscapes in recurrent Glioblastoma.

Patients diagnosed with glioblastoma (GBM) have the most aggressive tumor progression and lethal recurrence. Research on the immune microenvironment landscape of tumor and cerebrospinal fluid (CSF) is limited. At the single-cell level, we aim to reveal the recurrent immune microenvironment of GBM and the potential CSF biomarkers and compare tumor locations. We collected four clinical samples from two patients: malignant samples from one recurrent GBM patient and non-malignant samples from a patient with brain tumor. We performed single-cell RNA sequencing (scRNA-seq) to reveal the immune landscape of recurrent GBM and CSF. T cells were enriched in the malignant tumors, while Treg cells were predominately found in malignant CSF, which indicated an inhibitory microenvironment in recurrent GBM. Moreover, macrophages and neutrophils were significantly enriched in malignant CSF. This indicates that they an important role in GBM progression. S100A9, extensively expressed in malignant CSF, is a promising biomarker for GBM diagnosis and recurrence. Our study reveals GBM's recurrent immune microenvironment after chemoradiotherapy and compares malignant and non-malignant CSF samples. We provide novel targets and confirm the promise of liquid CSF biopsy for patients with GBM.

Machine learning-based endoplasmic reticulum-related diagnostic biomarker and immune microenvironment landscape for osteoarthritis.

Osteoarthritis (OA) is the most common degenerative joint disease worldwide. Further improving the current limited understanding of osteoarthritis has positive clinical value. OA samples were collected from GEO database and endoplasmic reticulum related genes (ERRGs) were identified. The WGCNA network was further built to identify the crucial gene module. Based on the expression profiles of characteristic ERRGs, LASSO algorithm was used to select key factors according to the minimum λ value. Random forest (RF) algorithm was used to calculate the importance of ERRGs. Subsequently, overlapping genes based on LASSO and RF algorithms were identified as ERRGs-related diagnostic biomarkers. In addition, OA specimens were also collected and performed qRT-PCR quantitative analysis of selected ERRGs. We identified four ERRGs associated with OA risk assessment through machine learning methods, and verified the abnormal expressions of these screened markers in OA patients through in vitro experiments. The influence of selected markers on OA immune infiltration was also evaluated. Our results provide new evidence for the role of ER stress in the OA progression, as well as new markers and potential intervention targets for OA.

Metabolism pathway-based subtyping in endometrial cancer: An integrated study by multi-omics analysis and machine learning algorithms

Endometrial cancer (EC), the second most common malignancy in the female reproductive system, has garnered increasing attention for its genomic heterogeneity, but understanding of its metabolic characteristics is still poor. We explored metabolic dysfunctions in EC through comprehensive multi-omics analysis (RNA-seq datasets from The Cancer Genome Atlas (TCGA), Cancer Cell Line Encyclopedia (CCLE), and GEO datasets; the Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteomics; CCLE metabolomics) to develop useful molecular targets for precision therapy. Unsupervised consensus clustering was performed to categorize EC patients into three metabolism-pathways-based subgroups (MPS). These MPS subgroups had distinct clinical prognoses, transcriptomic and genomic alterations, immune microenvironment landscape, and unique patterns of chemotherapy sensitivity. Moreover, the MPS2 subgroup has a better response to immunotherapy. Finally, three machine learning algorithms (LASSO, random forest, and stepwise multivariate Cox regression) were used for developing a prognostic “Metagene” signature based on metabolic molecules. Thus, a thirteen-hub-gene-based classifier was constructed to predict patients’ MPS subtype offering a more accessible and practical approach. This metabolism-based classification system can potentially enhance prognostic predictions and guide clinical strategies for immunotherapy and metabolism-targeted therapy in EC.

ScRNA-seq revealed high stemness epithelial malignant cell clusters and prognostic models of lung adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the sole causes of death in lung cancer patients. This study combined with single-cell RNA-seq analysis to identify tumor stem-related prognostic models to predict the prognosis of lung adenocarcinoma, chemotherapy agents, and immunotherapy efficacy. mRNA expression-based stemness index (mRNAsi) was determined by One Class Linear Regression (OCLR). Differentially expressed genes (DEGs) were detected by limma package. Single-cell RNA-seq analysis in GSE123902 dataset was performed using Seurat package. Weighted Co-Expression Network Analysis (WGCNA) was built by rms package. Cell differentiation ability was determined by CytoTRACE. Cell communication analysis was performed by CellCall and CellChat package. Prognosis model was constructed by 10 machine learning and 101 combinations. Drug predictive analysis was conducted by pRRophetic package. Immune microenvironment landscape was determined by ESTIMATE, MCP-Counter, ssGSEA analysis. Tumor samples have higher mRNAsi, and the high mRNAsi group presents a worse prognosis. Turquoise module was highly correlated with mRNAsi in TCGA-LUAD dataset. scRNA analysis showed that 22 epithelial cell clusters were obtained, and higher CSCs malignant epithelial cells have more complex cellular communication with other cells and presented dedifferentiation phenomenon. Cellular senescence and Hippo signaling pathway are the major difference pathways between high- and low CSCs malignant epithelial cells. The pseudo-temporal analysis shows that cluster1, 2, high CSC epithelial cells, are concentrated at the end of the differentiation trajectory. Finally, 13 genes were obtained by intersecting genes in turquoise module, Top200 genes in hdWGCNA, DEGs in high- and low- mRNAsi group as well as DEGs in tumor samples vs. normal group. Among 101 prognostic models, average c-index (0.71) was highest in CoxBoost + RSF model. The high-risk group samples had immunosuppressive status, higher tumor malignancy and low benefit from immunotherapy. This work found that malignant tumors and malignant epithelial cells have high CSC characteristics, and identified a model that could predict the prognosis, immune microenvironment, and immunotherapy of LUAD, based on CSC-related genes. These results provided reference value for the clinical diagnosis and treatment of LUAD.

Single-cell sequencing reveals the immune landscape of breast cancer patients with brain metastasis.

Breast cancer has the highest incidence rate of cancer worldwide, and brain metastases (BrM) are among the most malignant cases. While some patients have benefited from immune checkpoint inhibitors (ICIs), the complex anatomical structure of the brain and the heterogeneity of metastatic tumors have made it difficult to characterize the tumor immune microenvironment (TME) of metastatic tumors. To address this, we used single-cell RNA sequencing (scRNA-seq) to analyze immune cells in the cerebrospinal fluid (CSF) of BrM patients with breast cancer, thereby providing a comprehensive view of the immune microenvironment landscape of BrM. Based on canonical marker genes, we identified nine cell types, and further identified their subtypes through differential expression gene (DEG) analysis. We compared the changes in cells and functions in the immune microenvironment of patients with different prognoses. Our analysis revealed a series of genes that promote tumor immune function (CCR5, LYZ, IGKC, MS4A1, etc.) and inhibit tumor immune function (SCGB2A2, CD24, etc.). The scRNA-seq in CSF provides a noninvasive method to describe the TME of breast cancer patients and guide immunotherapy.

Construction and validation of molecular subtype and signature of immune cell-related telomeric genes and prediction of prognosis and immunotherapy efficacy in ovarian cancer patients.

Ovarian cancer (OVC) has emerged as a fatal gynecological malignancy as a result of a lack of reliable methods for early detection, limited biomarkers and few treatment options. Immune cell-related telomeric genes (ICRTGs) show promise as potential biomarkers. ICRTGs were discovered using weighted gene co-expression network analysis (WGCNA). ICRTGs were screened for significant prognosis using one-way Cox regression analysis. Subsequently, molecular subtypes of prognosis-relevant ICRTGs were constructed and validated for OVC, and the immune microenvironment's landscape across subtypes was compared. OVC prognostic models were built and validated using prognosis-relevant ICRTGs. Additionally, chemotherapy susceptibility drugs for OVC patients in the low- and high-risk groups of ICRTGs were screened using genomics of drug susceptibility to cancer (GDSC). Finally, the immunotherapy response in the low- and high-risk groups was detected using the data from GSE78220. We conducted an immune index correlation analysis of ICRTGs with significant prognoses. The MAP3K4 gene, for which the prognostic correlation coefficient is the highest, was validated using tissue microarrays for a prognostic-immune index correlation. WGCNA analysis constructed a gene set of ICRTGs and screened 22 genes with prognostic significance. Unsupervised clustering analysis revealed the best molecular typing for two subtypes. The Gene Set Variation Analysis algorithm was used to calculate telomere scores and validate the molecular subtyping. A prognostic model was constructed using 17 ICRTGs. In the The Cancer Genome Atlas-OVC training set and the Gene Expression Omnibus validation set (GSE30161), the risk score model's predicted risk groups and the actual prognosis were shown to be significantly correlated. GDSC screened Axitinib, Bexarotene, Embelin and the GSE78220 datasets and demonstrated that ICRTGs effectively distinguished the group that responds to immunotherapy from the non-responsive group. Additionally, tissue microarray validation results revealed that MAP3K4 significantly predicted patient prognosis. Furthermore, MAP3K4 exhibited a positive association with PD-L1 and a negative relationship with the M1 macrophage markers CD86 and INOS. ICRTGs may be reliable biomarkers for the molecular typing of patients with OVC, enabling the prediction of prognosis and immunotherapy efficacy.

Demystifying the landscape of endometrial immune microenvironment in luteal-phase from cuprotosis: Implications for the mechanism and treatment of RPL

BackgroundAdaptive changes in the endometrial immune microenvironment during the luteal phase are essential for pregnancy, and their abnormalities are associated with recurrent pregnancy loss (RPL). Nevertheless, the specific mechanism is still unknown. Cuprotosis, an innovatively discovered type of programmed cell death, provides us with a pioneering perspective to decipher the landscape of luteal-phase endometrial immune microenvironment in RPL. This study aimed to analyze the immune landscape of luteal-phase endometrial microenvironment in RPL and explore the association of cuprotosis with it through integrative bioinformatics analysis. MethodsThe microarrays involving the luteal phase endometrial tissue of RPL were obtained from the GEO database. Differentially expressed genes (DEGs) of RPL were screened and key modules were detected by WGCNA. GO, KEGG, and GSEA immune enrichment analyses were performed on the DEGs in the most relevant modules to RPL. Then, the endometrial immune microenvironment landscape of RPL was analyzed, including immune infiltration analysis and correlation analysis between immune cells or immune functions. The interaction of cuprotosis-related genes (CRGs), the expression level between groups, the immune localization and their correlation with immune cells and immune function were analyzed. LASSO regression and Nomogram evaluated the diagnostic value of immune-related CRGS in RPL. Functional enrichment analysis was performed on the RPL signature CRGs. And RPL samples were grouped according to the expression of 7 RPL signature CRGs through unsupervised clustering analysis. After that, we analyzed the expression level of CRGs and immune infiltration, as well as performed immune function enrichment analysis in subtypes. In addition, we also screened potential drugs that might act on CRGs to improve the pathological mechanism of RPL. ResultsIn this study, we uncovered that DEGs and genes in key modules derived from weighted gene co-expression network analysis (WGCNA) were involved in immune regulation. And the immune infiltration landscape of RPL was significantly different from healthy controls. Furthermore, six hub genes were screened from CRGs based on Cytohubba, and their expression profilings were verified in RPL and normal mouse samples. Besides, seven CRGs closely associated with the immune regulation of RPL were identified by Spearman correlation analysis, including SLC31A1, LIAS, DLD, DLAT, DBT, ATP7B, and ATP7A, named as immune-related CRGs. Furthermore, three subgroups clustered according to these seven genes showed significant differences in immune landscape, suggesting a remarkable effect of CRGs on immune regulation. Last but not least, we analyzed the regulation network of transcription factors, miRNAs, and CRGs, and screened potential compounds for the treatment of RPL by targeting CRGs. ConclusionsThe abnormal endometrial immune microenvironment in the luteal phase was associated with the pathomechanism of RPL, and cuprotosis was closely involved in the immune microenvironment in the luteal phase endometrium of RPL. Collectively, this study revealed the potential contribution of CRGs to the pathogenesis of RPL, providing a novel breakthroughs in insights into the pathogenesis, diagnosis, and treatment of RPL.

Single-cell RNA sequencing reveals the immune microenvironment landscape of osteosarcoma before and after chemotherapy

Chemotherapy, a primary treatment for osteosarcoma (OS), has limited knowledge regarding its impact on tumor immune microenvironment (TIME). Here, tissues from 6 chemotherapy-naive OS patients underwent single-cell RNA sequencing (scRNA-seq) and were analyzed alongside public dataset (GSE152048) containing 7 post-chemotherapy OS tissues. CD45+ (PTPRC+) cells were used for cell clustering and annotation. Changes in immune cell composition pre- and post-chemotherapy were characterized. Totally, 28,636 high-quality CD45+ (PTPRC+) cells were extracted. Following chemotherapy, the proportions of regulatory T cells (Tregs) and activated CD8 T cells decreased, while CD8 effector T cells increased. GO analysis indicated that differentially expressed genes (DEGs) in T cells were associated with cell activation, adaptive immune response, and immune response to tumor cells. Furthermore, the proportions of plasma cells increased, while naive B cells decreased. B cell surface receptors expression was upregulated, and GO analysis revealed DEGs of B cells were mainly enriched in B cell-mediated immunity and B cell activation. Moreover, M2 polarization of macrophages was suppressed post-chemotherapy. Overall, this study elucidates chemotherapy remodels the OS TIME landscape, triggering immune heterogeneity and enhancing anti-tumor properties.

Integrating single-cell and bulk RNA sequencing to predict prognosis and immunotherapy response in prostate cancer

Prostate cancer (PCa) stands as a prominent contributor to morbidity and mortality among males on a global scale. Cancer-associated fibroblasts (CAFs) are considered to be closely connected to tumour growth, invasion, and metastasis. We explored the role and characteristics of CAFs in PCa through bioinformatics analysis and built a CAFs-based risk model to predict prognostic treatment and treatment response in PCa patients. First, we downloaded the scRNA-seq data for PCa from the GEO. We extracted bulk RNA-seq data for PCa from the TCGA and GEO and adopted “ComBat” to remove batch effects. Then, we created a Seurat object for the scRNA-seq data using the package “Seurat” in R and identified CAF clusters based on the CAF-related genes (CAFRGs). Based on CAFRGs, a prognostic model was constructed by univariate Cox, LASSO, and multivariate Cox analyses. And the model was validated internally and externally by Kaplan–Meier analysis, respectively. We further performed GO and KEGG analyses of DEGs between risk groups. Besides, we investigated differences in somatic mutations between different risk groups. We explored differences in the immune microenvironment landscape and ICG expression levels in the different groups. Finally, we predicted the response to immunotherapy and the sensitivity of antitumour drugs between the different groups. We screened 4 CAF clusters and identified 463 CAFRGs in PCa scRNA-seq. We constructed a model containing 10 prognostic CAFRGs by univariate Cox, LASSO, and multivariate Cox analysis. Somatic mutation analysis revealed that TTN and TP53 were significantly more mutated in the high-risk group. Finally, we screened 31 chemotherapeutic drugs and targeted therapeutic drugs for PCa. In conclusion, we identified four clusters based on CAFs and constructed a new CAFs-based prognostic signature that could predict PCa patient prognosis and response to immunotherapy and might suggest meaningful clinical options for the treatment of PCa.

Demystifying the impact of prenatal tobacco exposure on the placental immune microenvironment: Avoiding the tragedy of mending the fold after death.

Prenatal tobacco exposure (PTE) correlates significantly with a surge in adverse pregnancy outcomes, yet its pathological mechanisms remain partially unexplored. This study aims to meticulously examine the repercussions of PTE on placental immune landscapes, employing a coordinated research methodology encompassing bioinformatics, machine learning and animal studies. Concurrently, it aims to screen biomarkers and potential compounds that could sensitively indicate and mitigate placental immune disorders. In the course of this research, two gene expression omnibus (GEO) microarrays, namely GSE27272 and GSE7434, were included. Gene set enrichment analysis (GSEA) and immune enrichment investigations on differentially expressed genes (DEGs) indicated that PTE might perturb numerous innate or adaptive immune-related biological processes. A cohort of 52 immune-associated DEGs was acquired by cross-referencing the DEGs with gene sets derived from the ImmPort database. A protein-protein interaction (PPI) network was subsequently established, from which 10 hub genes were extracted using the maximal clique centrality (MCC) algorithm (JUN, NPY, SST, FLT4, FGF13, HBEGF, NR0B2, AREG, NR1I2, SEMA5B). Moreover, we substantiated the elevated affinity of tobacco reproductive toxicants, specifically nicotine and nitrosamine, with hub genes through molecular docking (JUN, FGF13 and NR1I2). This suggested that these genes could potentially serve as crucial loci for tobacco's influence on the placental immune microenvironment. To further elucidate the immune microenvironment landscape, consistent clustering analysis was conducted, yielding three subtypes, where the abundance of follicular helper T cells (p < 0.05) in subtype A, M2 macrophages (p < 0.01), neutrophils (p < 0.05) in subtype B and CD8+ T cells (p < 0.05), resting NK cells (p < 0.05), M2 macrophages (p < 0.05) in subtype C were significantly different from the control group. Additionally, three pivotal modules, designated as red, blue and green, were identified, each bearing a close association with differentially infiltrated immunocytes, as discerned by the weighted gene co-expression network analysis (WGCNA). Functional enrichment analysis was subsequently conducted on these modules. To further probe into the mechanisms by which immune-associated DEGs are implicated in intercellular communication, 20 genes serving as ligands or receptors and connected to differentially infiltrating immunocytes were isolated. Employing a variety of machine learning techniques, including one-way logistic regression, LASSO regression, random forest and artificial neural networks, we screened 11 signature genes from the intersection of immune-associated DEGs and secretory protein-encoding genes derived from the Human Protein Atlas. Notably, CCL18 and IFNA4 emerged as prospective peripheral blood markers capable of identifying PTE-induced immune disorders. These markers demonstrated impressive predictive power, as indicated by the area under the curve (AUC) of 0.713 (0.548-0.857) and 0.780 (0.618-0.914), respectively. Furthermore, we predicted 34 potential compounds, including cyclosporine, oestrogen and so on, which may engage with hub genes and attenuate immune disorders instigated by PTE. The diagnostic performance of these biomarkers, alongside the interventional effect of cyclosporine, was further corroborated in animal studies via ELISA, Western blot and immunofluorescence assays. In summary, this study identifies a disturbance in the placental immune landscape, a secondary effect of PTE, which may underlie multiple pregnancy complications. Importantly, our research contributes to the noninvasive and timely detection of PTE-induced placental immune disorders, while also offering innovative therapeutic strategies for their treatment.

Mass cytometry reveals the corneal immune cell changes at single cell level in diabetic mice.

Diabetic ocular complications include sight-threatening consequences and decreased corneal sensitivity, characterized by decreased tear production, corneal sensitivity and delayed corneal epithelial wound healing. The pathogenesis of diabetic corneal disorders remains largely unknown. Growing evidence implies the participation of immune cells in the development of diabetic corneal diseases. Nonetheless, the immunological changes that result in diabetic corneal problems are largely unknown. Mass cytometry by time of flight (CyTOF) was used to investigate immune cell cluster alterations associated with diabetic corneal disorders. CyTOF test was performed on corneal cells at a single level from 21-week-old diabetic (db/db) and non-diabetic (db/m) mice. A panel of 41 immune-related markers monitored different immune cell types in diabetic corneas. To investigate the proportion of each immune cell subpopulation, an unsupervised clustering method was employed, and T-distributed stochastic neighbor embedding was used to visualize the distinctions between different immune cell subsets. Through CyTOF test, we identified 10 immune cell subsets in the corneal tissues. In a novel way, we discovered significant immune alterations in diabetic corneas, including pronounced alterations in T cells and myeloid cell subgroups in diabetic corneas linked to potential biomarkers, including CD103, CCR2, SiglecF, Ly6G, and CD172a. Comprehensive immunological profiling indicated remarkable changes in the immune microenvironment in diabetic corneas, characterized by a notable decrease in CD103+CD8+ tissue-resident memory T (TRM) cells and Tregs, as well as a dramatic increase of γδT cells and subsets of CD11b+Ly6G+ myeloid-derived suppressor cells (MDSCs). CyTOF analysis revealed significant alterations in the immune microenvironment during the development of diabetic corneal complications. This study mapped the immune microenvironment landscape of type 2 diabetic corneas, providing a fundamental understanding of immune-driven diabetic corneal disorders.

Construction and Validation of a Reliable Disulfidptosis-Related LncRNAs Signature of the Subtype, Prognostic, and Immune Landscape in Colon Cancer.

Disulfidptosis, a novel form of regulated cell death (RCD) associated with metabolism, represents a promising intervention target in cancer therapy. While abnormal lncRNA expression is associated with colon cancer development, the prognostic potential and biological characteristics of disulfidptosis-related lncRNAs (DRLs) remain unclear. Consequently, the research aimed to discover a novel indication of DRLs with significant prognostic implications, and to investigate their possible molecular role in the advancement of colon cancer. Here, we acquired RNA-seq data, pertinent clinical data, and genomic mutations of colon adenocarcinoma (COAD) from the TCGA database, and then DRLs were determined through Pearson correlation analysis. A total of 434 COAD patients were divided in to three subgroups through clustering analysis based on DRLs. By utilizing univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) algorithm, and multivariate Cox regression analysis, we ultimately created a prognostic model consisting of four DRLs (AC007728.3, AP003555.1, ATP2B1.AS1, and NSMCE1.DT), and an external database was used to validate the prognostic features of the risk model. According to the Kaplan-Meier curve analysis, patients in the low-risk group exhibited a considerably superior survival time in comparison to those in the high-risk group. Enrichment analysis revealed a significant association between metabolic processes and the genes that were differentially expressed in the high- and low-risk groups. Additionally, significant differences in the tumor immune microenvironment landscape were observed, specifically pertaining to immune cells, function, and checkpoints. High-risk patients exhibited a low likelihood of immune evasion, as indicated by the Tumor Immune Dysfunction and Exclusion (TIDE) analysis. Patients who exhibit both a high risk and high Tumor Mutational Burden (TMB) experience the least amount of time for survival, whereas those belonging to the low-risk and low-TMB category demonstrate the most favorable prognosis. In addition, the risk groups determined by the 4-DRLs signature displayed distinct drug sensitivities. Finally, we confirmed the levels of expression for four DRLs through rt-qPCR in both tissue samples from colon cancer patients and cell lines. Taken together, the first 4-DRLs-based signature we proposed may serve for a hopeful instrument for forecasting the prognosis, immune landscape, and therapeutic responses in colon cancer patients, thereby facilitating optimal clinical decision-making.

Tetraspanins predict the prognosis and characterize the tumor immune microenvironment of glioblastoma

Glioblastoma (GBM) is the most aggressive and lethal primary brain tumor. Conventional treatments have not achieved breakthroughs in improving survival. Therefore, novel molecular targets and biomarkers need to be identified. As signal transduction docks on the cell membrane, tetraspanins (TSPANs) are associated with various tumors; however, research on their role in GBM remains extremely scarce. Gene expression and clinicopathological characteristic data were obtained from GEPIA, CGGA, HPA, cBioPortal, and GSCA databases to analyze the mRNA and protein expression levels, prognostic value, clinical relevance, mutation status, and targeted drug sensitivity of TSPANs in GBM. Gene set enrichment analysis (GSEA), Gene Ontology (GO), and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analysis were used for biological process enrichment. Data from TCGA and TCIA were used to construct the tumor immune microenvironment landscape of TSPANs. Different R software algorithms were used to analyze the immune score, immune cell infiltration, and immune checkpoint correlation. Univariate and multivariate analyses were performed for TSPAN4, which had the most significant predictive prognostic value, and a nomogram model was constructed to predict individual outcomes. The expression and function of TSPAN4 were verified in vitro. TSPAN3/4/6/11/12/18/23/24/25/26/27/28/29/30/31expressions were significantly upregulated in GBM, and TSPAN3/4/6/11/18/24/25/26/29/30 were strongly correlated with prognosis. The expression of multiple TSPANs significantly correlated with 1p/19q co-deletion status, IDH mutation status, recurrence, age, and tumor grade. GSEA and GO analyses revealed the potential contribution of TSPANs in cell adhesion and migration. Immune correlation analysis revealed that TSPANs are related to the formation of the GBM tumor microenvironment (TME) and may influence immunotherapy outcomes. TSPAN4 is an independent prognostic factor and TSPAN4 knockdown has been demonstrated to strongly inhibit glioma cell proliferation, invasion, and migration in vitro. We comprehensively elaborated the prognostic value and potential role of differentially expressed TSPANs in GBM, including molecules that scientists have previously overlooked. This study provides a novel and comprehensive perspective on the pathological mechanisms of GBM and the future direction of individualized tumor immunotherapy, which may be a critical link between GBM malignant progression and TME remodeling.