A novel molecular classification based on efferocytosis-related genes for predicting clinical outcome and treatment response in acute myeloid leukemia.

BACKGROUND: Previous studies have shown that macrophage-mediated efferocytosis is involved in immunosuppression in acute myeloid leukemia (AML). However, the regulatory role of efferocytosis in AML remains unclear and needs further elucidation. METHODS: We first identified the key efferocytosis-related genes (ERGs) based on the expression matrix. Efferocytosis-related molecular subtypes were obtained by consensus clustering algorithm. Differences in immune landscape and biological processes among molecular subtypes were further evaluated. The efferocytosis score model was constructed to quantify molecular subtypes and evaluate its value in prognosis prediction and treatment decision-making in AML. RESULTS: Three distinct efferocytosis-related molecular subtypes were identified and divided into immune activation, immune desert, and immunosuppression subtypes based on the characteristics of the immune landscape. We evaluated the differences in clinical and biological features among different molecular subtypes, and the construction of an efferocytosis score model can effectively quantify the subtypes. A low efferocytosis score is associated with immune activation and reduced mutation frequency, and patients have a better prognosis. A high efferocytosis score reflects immune exhaustion, increased activity of tumor marker pathways, and poor prognosis. The prognostic predictive value of the efferocytosis score model was confirmed in six AML cohorts. Patients exhibiting high efferocytosis scores may derive therapeutic benefits from anti-PD-1 immunotherapy, whereas those with low efferocytosis scores tend to exhibit greater sensitivity towards chemotherapy. Analysis of treatment data in ex vivo AML cells revealed a group of drugs with significant differences in sensitivity between different efferocytosis score groups. Finally, we validated model gene expression in a clinical cohort. CONCLUSIONS: This study reveals that efferocytosis plays a non-negligible role in shaping the diversity and complexity of the AML immune microenvironment. Assessing the individual efferocytosis-related molecular subtype in individuals will help to enhance our understanding of the characterization of the AML immune landscape and guide the establishment of more effective clinical treatment strategies.

Single-cell RNA sequencing in endometrial cancer: exploring the epithelial cells and the microenvironment landscape.

Single-cell RNA sequencing (scRNA-seq) technology has emerged as a powerful tool for dissecting cellular heterogeneity and understanding the intricate biology of diseases, including cancer. Endometrial cancer (EC) stands out as the most prevalent gynecological malignancy in Europe and the second most diagnosed worldwide, yet its cellular complexity remains poorly understood. In this review, we explore the contributions of scRNA-seq studies to shed light on the tumor cells and cellular landscape of EC. We discuss the diverse tumoral and microenvironmental populations identified through scRNA-seq, highlighting the implications for understanding disease progression. Furthermore, we address potential limitations inherent in scRNA-seq studies, such as technical biases and sample size constraints, emphasizing the need for larger-scale research encompassing a broader spectrum of EC histological subtypes. Notably, a significant proportion of scRNA-seq analyses have focused on primary endometrioid carcinoma tumors, underscoring the need to incorporate additional histological and aggressive types to comprehensively capture the heterogeneity of EC. By critically evaluating the current state of scRNA-seq research in EC, this review underscores the importance of advancing towards more comprehensive studies to accelerate our understanding of this complex disease.

Integrating Multi-omics to Identify Age-Related Macular Degeneration Subtypes and Biomarkers.

Age-related macular degeneration (AMD) is one of the most common causes of irreversible vision loss in the elderly. Its pathogenesis is likely multifactorial, involving a complex interaction of metabolic and environmental factors, and remains poorly understood. Previous studies have shown that mitochondrial dysfunction and oxidative stress play a crucial role in the development of AMD. Oxidative damage to the retinal pigment epithelium (RPE) has been identified as one of the major mediators in the pathogenesis of age-related macular degeneration (AMD). Therefore, this article combines transcriptome sequencing (RNA-seq) and single-cell sequencing (scRNA-seq) data to explore the role of mitochondria-related genes (MRGs) in AMD. Firstly, differential expression analysis was performed on the raw RNA-seq data. The intersection of differentially expressed genes (DEGs) and MRGs was performed. This paper proposes a deep subspace nonnegative matrix factorization (DS-NMF) algorithm to perform a multi-layer nonlinear transformation on the intersection of gene expression profiles corresponding to AMD samples. The age of AMD patients is used as prior information at the network's top level to change the data distribution. The classification is based on reconstructed data with altered distribution. The types obtained significantly differ in scores of multiple immune-related pathways and immune cell infiltration abundance. Secondly, an optimal AMD diagnosis model was constructed using multiple machine learning algorithms for external and qRT-PCR verification. Finally, ten potential therapeutic drugs for AMD were identified based on cMAP analysis. The AMD subtypes identified in this article and the diagnostic model constructed can provide a reference for treating AMD and discovering new drug targets.

Identification of immunity- and ferroptosis-related signature genes as potential design targets for mRNA vaccines in AML patients.

Immune-associated ferroptosis plays an important role in the progression of acute myeloid leukemia (AML); however, the targets that play key roles in this process are currently unknown. This limits the development of mRNA vaccines based on immune-associated ferroptosis for clinical therapeutic applications. In this study, based on the rich data resources of the TCGA-LAML cohort, we analyzed the tumor mutational burden (TMB), gene mutation status, and associations between immune and ferroptosis genes to reveal the disease characteristics of AML patients. To gain a deeper understanding of differentially expressed genes, we applied the Limma package for differential expression analysis and integrated data sources such as ImmPort Shared Data and FerrDb V2. Moreover, we established gene modules related to TMB according to weighted gene coexpression network analysis (WGCNA) and explored the functions of these modules in AML and their relationships with TMB. We focused on the top 30 most frequent genes through a detailed survey of missense mutations and single nucleotide polymorphisms (SNPs) and selected potentially critical gene targets for subsequent analysis. Based on the expression of these genes, we successfully subgrouped AML patients and found that the subgroups associated with TMB (C1 and C2) exhibited significant differences in survival. The differences in the tumor microenvironment and immune cells between C1 and C2 patients were investigated with the ESTIMATE and MCP-counter algorithms. A predictive model of TMB-related genes (TMBRGs) was constructed, and the validity of the model was demonstrated by categorizing patients into high-risk and low-risk groups. The differences in survival between the high-risk patients and high-TMB patients were further investigated, and potential vaccine targets were identified via immune cell-level analysis. The identification of immunity- and ferroptosis-associated signature genes is an independent predictor of survival in AML patients and provides new information on immunotherapy for AML.

A novel M2-like tumor associated macrophages-related gene signature for predicting the prognosis and immunotherapy efficacy in gastric cancer.

BACKGROUND: M2-like tumor-associated macrophages (M2-like TAMs) play key roles in tumor progression and the immune response. However, the clinical significance and prognostic value of M2-like TAMs-associated regulatory genes in gastric cancer (GC) have not been clarified. METHODS: Herein, we identified M2-like TAM-related genes by weighted gene coexpression network analysis of TCGA-STAD and GSE84437 cohort. Lasso-Cox regression analyses were then performed to screen for signature genes, and a novel signature was constructed to quantify the risk score for each patient. Tumor mutation burden (TMB), survival outcomes, immune cells, and immune function were analyzed in the risk groups to further reveal the immune status of GC patients. A gene-drug correlation analysis and sensitivity analysis of anticancer drugs were used to identify potential therapeutic agents. Finally, we verified the mRNA expression of signature genes in patient tissues by qRT-PCR, and analyzed the expression distribution of these genes by IHC. RESULTS: A 4-gene (SERPINE1, MATN3, CD36, and CNTN1) signature was developed and validated, and the risk score was shown to be an independent prognostic factor for GC patients. Further analyses revealed that GC patients in the high-risk group had a worse prognosis than those in the low-risk group, with significant differences in TMB, clinical features, enriched pathways, TIDE score, and tumor microenvironment features. Finally, we used qRT-PCR and IHC analysis to verify mRNA and protein level expression of signature genes. CONCLUSION: These findings highlight the importance of M2-like TAMs, provide a new perspective on individualized immunotherapy for GC patients.

Single-cell immune landscape of measurable residual disease in acute myeloid leukemia.

Measurable residual disease (MRD) is a powerful prognostic factor of relapse in acute myeloid leukemia (AML). We applied the single-cell RNA sequencing to bone marrow (BM) samples from patients with (n=20) and without (n=12) MRD after allogeneic hematopoietic stem cell transplantation. A comprehensive immune landscape with 184,231 cells was created. Compared with CD8+ T cells enriched in the MRD-negative group (MRD-_CD8), those enriched in the MRD-positive group (MRD+_CD8) showed lower expression levels of cytotoxicity-related genes. Three monocyte clusters (i.e., MRD+_M) and three B-cell clusters (i.e., MRD+_B) were enriched in the MRD-positive group. Conversion from an MRD-positive state to an MRD-negative state was accompanied by an increase in MRD-_CD8 clusters and vice versa. MRD-enriched cell clusters employed the macrophage migration inhibitory factor pathway to regulate MRD-_CD8 clusters. These findings revealed the characteristics of the immune cell landscape in MRD positivity, which will allow for a better understanding of the immune mechanisms for MRD conversion.

The cross-talk between the macro and micro-environment in precursor lesions of pancreatic cancer leads to new and promising circulating biomarkers.

Pancreatic cancer (PC) is a clinically challenging tumor to combat due to its advanced stage at diagnosis as well as its resistance to currently available therapies. The absence of early symptoms and known detectable biomarkers renders this disease incredibly difficult to detect/manage. Recent advances in the understanding of PC biology have highlighted the importance of cancer-immune cell interactions, not only in the tumor micro-environment but also in distant systemic sites, like the bone marrow, spleen and circulating immune cells, the so-called macro-environment. The response of the macro-environment is emerging as a determining factor in tumor development by contributing to the formation of an increasingly immunogenic micro-environment promoting tumor homeostasis and progression. We will summarize the key events associated with the feedback loop between the tumor immune micro-environment (TIME) and the tumor immune macroenvironment (TIMaE) in pancreatic precancerous lesions along with how it regulates disease development and progression. In addition, liquid biopsy biomarkers capable of diagnosing PC at an early stage of onset will also be discussed. A clearer understanding of the early crosstalk between micro-environment and macro-environment could contribute to identifying new molecular therapeutic targets and biomarkers, consequently improving early PC diagnosis and treatment.

Immune landscape of renal cell carcinoma with metastasis to the pancreas.

INTRODUCTION: Clear cell Renal Cell Carcinoma (ccRCC) has a poor prognosis once metastatic. However, certain metastatic sites have been reported to have a different impact on the patient prognosis. For example, patients with pancreatic metastases have a much more favorable prognosis than those with metastases to other organs. The biological basis for this observation remains poorly understood. The aim of this study was to characterize the immune landscape of pancreatic metastases and the corresponding primary tumors in order to identify possible immunological features that correlate with disease biology. PATIENTS AND METHODS: A detailed assessment of immune cell populations was performed using a total of 1,700 microscopic images from ccRCCs from 11 patients, their corresponding pancreatic metastases and ccRCCs from 10 patients without pancreatic metastases. Tumor specimens were stained for CD45, CD8, CD163 and FOXP3 and the densities of the respective immune cells were assessed semiquantitatively in the intratumoral and extratumoral compartment. Multispectral imaging was performed in selected tumors. RESULTS: We found that pancreatic metastases show the lowest intratumoral infiltration with CD8+ cytotoxic T lymphocytes of all tumor specimens analyzed. The frequency of CD8+ lymphocytes was on 1.9 fold lower in pancreatic metastases (median density 8.3 cells per field of view [FOV] = 1.23 mm2) when compared to the corresponding primary tumor (15.6 cells per FOV, P = 0.0002) and more than 3-fold lower when compared to ccRCCs without pancreatic metastases (27.2 cells per FOV, P = 0.0012). There was also a significantly reduced intratumoral infiltration with immunosuppressive FOXP3+ lymphocytes in pancreatic metastases (2.6 cells per FOV, P = 0.009) and corresponding primary tumors (2 cells per FOV, P = 0.028) when compared to ccRCCs without pancreatic metastases (5.6 cells per FOV). CONCLUSIONS: In this proof-of-concept study, we show that pancreatic metastases of ccRCC present with unique immunological features including a low intratumoral density of CD8+ and FOXP3+ lymphocytes. The low counts of CD8+ and FOXP3+ lymphocytes may reflect less aggressive features of ccRCC with pancreatic metastasis that may result in a more favorable patient prognosis.

Splenic marginal zone B cells restrict Mycobacterium tuberculosis infection by shaping the cytokine pattern and cell-mediated immunity.

Understanding the role of B cells in tuberculosis (TB) is crucial for developing new TB vaccines. However, the changes in B cell immune landscapes during TB and their functional implications remain incompletely explored. Using high-dimensional flow cytometry to map the immune landscape in response to Mycobacterium tuberculosis (Mtb) infection, our results show an accumulation of marginal zone B (MZB) cells and other unconventional B cell subsets in the lungs and spleen, shaping an unconventional B cell landscape. These MZB cells exhibit activated and memory-like phenotypes, distinguishing their functional profiles from those of conventional B cells. Notably, functional studies show that MZB cells produce multiple cytokines and contribute to systemic protection against TB by shaping cytokine patterns and cell-mediated immunity. These changes in the immune landscape are reversible upon successful TB chemotherapy. Our study suggests that, beyond antibody production, targeting the regulatory function of B cells may be a valuable strategy for TB vaccine development.

A novel artificial intelligence network to assess the prognosis of gastrointestinal cancer to immunotherapy based on genetic mutation features.

Background: Immune checkpoint inhibitors (ICIs) have revolutionized gastrointestinal cancer treatment, yet the absence of reliable biomarkers hampers precise patient response prediction. Methods: We developed and validated a genomic mutation signature (GMS) employing a novel artificial intelligence network to forecast the prognosis of gastrointestinal cancer patients undergoing ICIs therapy. Subsequently, we explored the underlying immune landscapes across different subtypes using multiomics data. Finally, UMI-77 was pinpointed through the analysis of drug sensitization data from the Genomics of Drug Sensitivity in Cancer (GDSC) database. The sensitivity of UMI-77 to the AGS and MKN45 cell lines was evaluated using the cell counting kit-8 (CCK8) assay and the plate clone formation assay. Results: Using the artificial intelligence network, we developed the GMS that independently predicts the prognosis of gastrointestinal cancer patients. The GMS demonstrated consistent performance across three public cohorts and exhibited high sensitivity and specificity for 6, 12, and 24-month overall survival (OS) in receiver operating characteristic (ROC) curve analysis. It outperformed conventional clinical and molecular features. Low-risk samples showed a higher presence of cytolytic immune cells and enhanced immunogenic potential compared to high-risk samples. Additionally, we identified the small molecule compound UMI-77. The half-maximal inhibitory concentration (IC50) of UMI-77 was inversely related to the GMS. Notably, the AGS cell line, classified as high-risk, displayed greater sensitivity to UMI-77, whereas the MKN45 cell line, classified as low-risk, showed less sensitivity. Conclusion: The GMS developed here can reliably predict survival benefit for gastrointestinal cancer patients on ICIs therapy.

A predictive radiotranscriptomics model based on DCE-MRI for tumor immune landscape and immunotherapy in cholangiocarcinoma.

This study aims to determine the predictive role of dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) derived radiomic model in tumor immune profiling and immunotherapy for cholangiocarcinoma. To perform radiomic analysis, immune related subgroup clustering was first performed by single sample gene set enrichment analysis (ssGSEA). Second, a total of 806 radiomic features for each phase of DCE-MRI were extracted by utilizing the Python package Pyradiomics. Then, a predictive radiomic signature model was constructed after a three-step features reduction and selection, and receiver operating characteristic (ROC) curve was employed to evaluate the performance of this model. In the end, an independent testing cohort involving cholangiocarcinoma patients with anti-PD-1 Sintilimab treatment after surgery was used to verify the potential application of the established radiomic model in immunotherapy for cholangiocarcinoma. Two distinct immune related subgroups were classified using ssGSEA based on transcriptome sequencing. For radiomic analysis, a total of 10 predictive radiomic features were finally identified to establish a radiomic signature model for immune landscape classification. Regarding to the predictive performance, the mean AUC of ROC curves was 0.80 in the training/validation cohort. For the independent testing cohort, the individual predictive probability by radiomic model and the corresponding immune score derived from ssGSEA was significantly correlated. In conclusion, radiomic signature model based on DCE-MRI was capable of predicting the immune landscape of chalangiocarcinoma. Consequently, a potentially clinical application of this developed radiomic model to guide immunotherapy for cholangiocarcinoma was suggested.

Exploring the immune landscape and drug prediction of an M2 tumor-associated macrophage-related gene signature in EGFR-negative lung adenocarcinoma.

BACKGROUND: Improving immunotherapy efficacy for EGFR-negative lung adenocarcinoma (LUAD) patients remains a critical challenge, and the therapeutic effect of immunotherapy is largely determined by the tumor microenvironment (TME). Tumor-associated macrophages (TAMs) are the top-ranked immune infiltrating cells in the TME, and M2-TAMs exert potent roles in tumor promotion and chemotherapy resistance. An M2-TAM-based prognostic signature was constructed by integrative analysis of single-cell RNA-seq (scRNA-seq) and bulk RNA-seq data to reveal the immune landscape and select drugs in EGFR-negative LUAD. METHODS: M2-TAM-based biomarkers were obtained from the intersection of bulk RNA-seq data and scRNA-seq data. After consensus clustering of EGFR-negative LUAD into different clusters based on M2-TAM-based genes, we compared the prognosis, clinical features, estimate scores, immune infiltration, and checkpoint genes among the clusters. Next, we combined univariate Cox and LASSO regression analyses to establish an M2-TAM-based prognostic signature. RESULTS: CCL20, HLA-DMA, HLA-DRB5, KLF4, and TMSB4X were verified as prognostic M2-like TAM-related genes by univariate Cox and LASSO regression analyses. IPS and TMB analyses revealed that the high-risk group responded better to common immunotherapy. CONCLUSION: The study shows the potential of the M2-like TAM-related gene signature in EGFR-negative LUAD, explores the immune landscape based on M2-like TAM-related genes, and predict immunotherapy response of patients with EGFR-negative LUAD, providing a new insight for individualized treatment.

Understanding the heterogeneity in liver hepatocellular carcinoma with a special focus on malignant cell through single-cell analysis.

INTRODUCTION: Hepatocellular carcinoma (HCC) is the most common form of liver cancer globally and remains a major cause of cancer-related deaths. HCC exhibits significant intra-tumoral and interpatient heterogeneity, impacting treatment efficacy and patient prognosis. METHODS: We acquired transcriptome data from the TCGA and ICGC databases, as well as liver cancer chip data from the GEO database, and processed the data for subsequent analysis. We also obtained single cell data from the GEO database and performed data analysis using the Seurat package. To further investigate epithelial cell subgroups and their copy number variations, we used the Seurat workflow for subgroup classification and the InferCNV software for CNV analysis, utilizing endothelial cells as a reference. Pseudo-time analysis and transcription factor analysis of epithelial cells were performed using the monocle2 and SCENIC software, respectively. To assess intercellular communication, we employed the CellChat package to identify potential ligand-receptor interactions. We also analyzed gene expression differences and conducted enrichment analysis using the limma and clusterProfiler packages. Additionally, we established tumor-related risk characteristics using Cox analysis and Lasso regression, and predicted immunotherapy response using various datasets. RESULTS: The samples were classified into 23 clusters, with malignant epithelial cells being the majority. Trajectory analysis revealed the differentiation states of the malignant epithelial cells, with cluster 1 being in the terminal state. Functional analysis revealed higher aggressiveness and epithelial-mesenchymal transition (EMT) scores in cluster 1, indicating a higher propensity for metastasis. RBP4+ tumor cells were highly enriched with hypoxia process and intensive cell-to-cell communication. A prognostic model was established, and immune infiltration analysis showed increased infiltration in the high-risk group. TP53 demonstrated significant differences in mutation rate between the two risk groups. Validation analysis confirmed the up-regulation of model genes, including AKR1B10, ARL6IP4, ATP6V0B, and BSG in tumor tissues. CONCLUSION: A prognostic model was established based on HCC malignant cell associated gene signature, displaying decent prognosis guiding effectiveness in the multiple cohorts. The study provided comprehensive insights into the heterogeneity and potential therapeutic targets of LIHC.

Monitoring melanoma patients on treatment reveals a distinct macrophage population driving targeted therapy resistance.

Resistance to targeted therapy remains a major clinical challenge in melanoma. To uncover resistance mechanisms, we perform single-cell RNA sequencing on fine-needle aspirates from resistant and responding tumors of patients undergoing BRAFi/MEKi treatment. Among the genes most prominently expressed in resistant tumors is POSTN, predicted to signal to a macrophage population associated with targeted therapy resistance (TTR). Accordingly, tumors from patients with fast disease progression after therapy exhibit high POSTN expression levels and high numbers of TTR macrophages. POSTN polarizes human macrophages toward a TTR phenotype and promotes resistance to targeted therapy in a melanoma mouse model, which is associated with a phenotype change in intratumoral macrophages. Finally, polarized TTR macrophages directly protect human melanoma cells from MEKi-induced killing via CD44 receptor expression on melanoma cells. Thus, interfering with the protective activity of TTR macrophages may offer a strategy to overcome resistance to targeted therapy in melanoma.

A novel signature based on twelve programmed cell death patterns to predict the prognosis of lung adenocarcinoma.

Programmed cell death (PCD) plays a pivotal role in tumor initiation and progression. However, the prognostic value and clinical characteristics of PCD-related genes (PRGs) remain unclear. We collected and analyzed genes associated with twelve PCD patterns, including apoptosis, necroptosis, pyroptosis, ferroptosis, cuproptosis, entotic cell death, netotic cell death, parthanatos, lysosome-dependent cell death, autophagy-dependent cell death, alkaliptosis, and oxeiptosis to construct a gene signature. Our analysis identified 215 differentially expressed PRGs out of 1254 in lung adenocarcinoma (LUAD) and normal lung tissues. Subsequently, we performed univariate Cox regression analysis and identified 58 prognostic PRGs. Based on LASSO Cox regression analysis, we constructed a risk score using the expression levels of seven genes: DAPK2, DDIT4, E2F2, GAPDH, MET, PIM2, and FOXF1. Patients with lower risk scores showed earlier stages of cancer, longer survival times, and better immune infiltrations and functions. Notably, we found that knockdown of DDIT4 significantly increased apoptosis and impaired the proliferation of human LUAD cell lines. Our study proposes a PRG-based prognostic signature that sheds light on the potential role of PCD-related genes in LUAD and provides valuable insights into future therapeutic strategies.

A novel mitochondrial-related lncRNA signature mediated prediction of overall survival, immune landscape, and the chemotherapeutic outcomes for bladder cancer patients.

OBJECTIVE: To develop a prognostic risk model for Bladder Cancer (BLCA) based on mitochondrial-related long non-coding RNAs (lncRNAs). METHODS: Transcriptome and clinical data of BLCA patients were retrieved from the TCGA database. Mitochondrial-related lncRNAs with independent prognostic significance were screened to develop a prognostic risk model. Patients were categorized into high- and low-risk groups using the model. Various methods including Kaplan-Meier (KM) analysis, ROC curve analysis, Gene Set Enrichment Analysis (GSEA), immune analysis, and chemotherapy drug analysis were used to verify and evaluate the model. RESULTS: A mitochondrial-associated lncRNA prognostic risk model with independent prognostic significance was developed. High-risk group (HRG) patients exhibited significantly shorter survival periods compared to low-risk group (LRG) patients (P < 0.01). The risk score from the model was an independent predictor of BLCA prognosis, correlating with tumor grade, pathological stage, and lymph node metastasis (P < 0.05). The HRG showed significant positive correlations with high expressions of immune checkpoints (CTLA4, LAG3, PD-1, TIGIT, PD-L1, PD-L2, and TIM-3) and lower IC50 for chemotherapy drugs (cisplatin, docetaxel, paclitaxel, methotrexate, and vinblastine) (P < 0.001). CONCLUSIONS: The mitochondrial-related lncRNA-based prognostic risk model effectively predicts BLCA prognosis and can guide individualized treatment for BLCA patients.

Identification of apoptosis-immune-related gene signature and construction of diagnostic model for sepsis based on single-cell sequencing and bulk transcriptome analysis.

Introduction: Sepsis leads to multi-organ dysfunction due to disorders of the host response to infections, which makes diagnosis and prognosis challenging. Apoptosis, a classic programmed cell death, contributes to the pathogenesis of various diseases. However, there is much uncertainty about its mechanism in sepsis. Methods: Three sepsis gene expression profiles (GSE65682, GSE13904, and GSE26378) were downloaded from the Gene Expression Omnibus database. Apoptosis-related genes were obtained from the Kyoto Encyclopedia of Genes and Genomes Pathway database. We utilized LASSO regression and SVM-RFE algorithms to identify characteristic genes associated with sepsis. CIBERSORT and single cell sequencing analysis were employed to explore the potential relationship between hub genes and immune cell infiltration. The diagnostic capability of hub genes was validated across multiple external datasets. Subsequently, the animal sepsis model was established to assess the expression levels of hub genes in distinct target organs through RT-qPCR and Immunohistochemistry analysis. Results: We identified 11 apoptosis-related genes as characteristic diagnostic markers for sepsis: CASP8, VDAC2, CHMP1A, CHMP5, FASLG, IFNAR1, JAK1, JAK3, STAT4, IRF9, and BCL2. Subsequently, a prognostic model was constructed using LASSO regression with BCL2, FASLG, IRF9 and JAK3 identified as hub genes. Apoptosis-related genes were closely associated with the immune response during the sepsis process. Furthermore, in the validation datasets, aside from IRF9, other hub genes demonstrated similar expression patterns and diagnostic abilities as observed in GSE65682 dataset. In the mouse model, the expression differences of hub genes between sepsis and control group revealed the potential impacts on sepsis-induced organ injury. Conclusion: The current findings indicated the participant of apoptosis in sepsis, and apoptosis-related differentially expressed genes could be used for diagnosis biomarkers. BCL2, FASLG, IRF9 and JAK3 might be key regulatory genes affecting apoptosis in sepsis. Our findings provided a novel aspect for further exploration of the pathological mechanisms in sepsis.

Prognostic significance and immune landscape of a cell cycle progression-related risk model in bladder cancer.

BACKGROUND: A greater emphasis has been placed on the part of cell cycle progression (CCP) in cancer in recent years. Nevertheless, the precise connection between CCP-related genes and bladder cancer (BCa) has remained elusive. This study endeavors to establish and validate a reliable risk model incorporating CCP-related factors, aiming to predict both the prognosis and immune landscape of BCa. METHODS: Clinical information and RNA sequencing data were collected from the GEO and TCGA databases. Univariate and multivariate Cox regression analyses were conducted to construct a risk model associated with CCP. The performance of the model was assessed using ROC and Kaplan-Meier survival analyses. Functional enrichment analysis was employed to investigate potential cellular functions and signaling pathways. The immune landscape was characterized using CIBERSORT algorithms. Integration of the risk model with various clinical variables led to the development of a nomogram. RESULTS: To build the risk model, three CCP-related genes (RAD54B, KPNA2, and TPM1) were carefully chosen. ROC and Kaplan-Meier survival analysis confirm that our model has good performance. About immunological infiltration, the high-risk group showed decreased levels of regulatory T cells and dendritic cells coupled with increased levels of activated CD4 + memory T cells, M2 macrophages, and neutrophils. Furthermore, the nomogram showed impressive predictive power for OS at 1, 3, and 5 years. CONCLUSION: This study provides new insights into the association between the CCP-related risk model and the prognosis of BCa, as well as its impact on the immune landscape.

Novel kinase 1 regulates CD8+T cells as a potential therapeutic mechanism for idiopathic pulmonary fibrosis.

Idiopathic pulmonary fibrosis (IPF) is a rare, chronic and progressively worsening lung disease that poses a significant threat to patient prognosis, with a mortality rate exceeding that of some common malignancies. Effective methods for early diagnosis and treatment remain for this condition are elusive. In our study, we used the GEO database to access second-generation sequencing data and associated clinical information from IPF patients. By utilizing bioinformatics techniques, we identified crucial disease-related genes and their biological functions, and characterized their expression patterns. Furthermore, we mapped out the immune landscape of IPF, which revealed potential roles for novel kinase 1 and CD8+T cells in disease progression and outcome. These findings can aid the development of new strategies for the clinical diagnosis and treatment of IPF.

Immune cell patterns before and after neoadjuvant immune checkpoint blockade combined with chemoradiotherapy in locally advanced esophageal squamous cell carcinoma.

BACKGROUND: Neoadjuvant immune checkpoint blockade (ICB) combined with chemoradiotherapy offers high pathologic complete response (pCR) rate for patients with locally advanced esophageal squamous cell carcinomas (ESCC). But the dynamic tumor immune microenvironment modulated by such neoadjuvant therapy remains unclear. PATIENTS AND METHODS: A total of 41 patients with locally advanced ESCC were recruited. All patients received neoadjuvant toripalimab combined with concurrent chemoradiotherapy. Matched pre- and post-treatment tissues were obtained for fluorescent multiplex immunohistochemistry (mIHC) and IHC analyses. The densities and spatial distributions of immune cells were determined by HALO modules. The differences of immune cell patterns before and after neoadjuvant treatment were investigated. RESULTS: In the pre-treatment tissues, more stromal CD3 + FoxP3 + Tregs and CD86+/CD163 + macrophages were observed in patients with residual tumor existed in the resected lymph nodes (pN1), compared with patients with pCR. The majority of macrophages were distributed in close proximity to tumor nest in pN1 patients. In the post-treatment tissues, pCR patients had less CD86 + cell infiltration, whereas higher CD86 + cell density was significantly associated with higher tumor regression grades (TRG) in non-pCR patients. When comparing the paired pre- and post-treatment samples, heterogeneous therapy-associated immune cell patterns were found. Upon to the treatment, CD3 + T lymphocytes were slightly increased in pCR patients, but markedly decreased in non-pCR patients. In contrast, a noticeable increase and a less obvious decrease of CD86 + cell infiltration were respectively depicted in non-pCR and pCR patients. Furthermore, opposite trends of the treatment-induced alterations of CD8 + and CD15 + cell infiltrations were observed between pN0 and pN1 patients. CONCLUSIONS: Collectively, our data demonstrate a comprehensive picture of tumor immune landscape before and after neoadjuvant ICB combined with chemoradiotherapy in ESCC. The infiltration of CD86 + macrophage may serve as an unfavorable indicator for neoadjuvant toripalimab combined with chemoradiotherapy.