Background: Integrating immune checkpoint inhibitors with multi-target tyrosine kinase inhibitors presents an innovative and hopeful strategy in liver cancer treatment. Nonetheless, a degree of resistance to this treatment is noticeable in certain patients. Alternative splicing (AS) represents a common biological process that controls the variety of life functions via isoforms. Purpose: Investigating how gene AS affects the effectiveness of combined immunotherapy in treating hepatocellular carcinoma (HCC). Methods: Our retrospective examination focused on AS\'s effect on immune therapy effectiveness, utilizing accessible tissue sequencing and clinical records for HCC. For corroborating our results, we gathered samples of drug-resistant HCC tissue, nearby tissues, HCC tissue with high drug responsiveness, and healthy liver tissue from clinical studies. Results: The study revealed a link between the frequency of AS occurrences, the expression levels of programmed cell death 1 ligand 1, and the resistance to tumor medications. Our study detailed the AS occurrences in HCC, leading to the creation of a risk-assessment function and a predictive model using AS data. The results of our study revealed that the risk score effectively distinguished between various immune subtypes and the effectiveness of immune therapy. Additional examination of the chosen AS occurrences uncovered their effects on both the immune microenvironment and cellular immunity. Our investigation also delved into the regulatory framework of AS, uncovering the role of stringently controlled splicing factors in the emergence of tumors and the modulation of the body\'s immune response. Conclusions: Increased AS in HCC diminishes the efficacy of immunotherapy; conversely, more AS in peritumoral tissue elevates the likelihood of tumor immune evasion.

BACKGROUND: NR4A family genes play crucial roles in cancers. However, the role of NR4A family genes in cancers remains paradoxical as they promote or suppress tumorigenesis.
OBJECTIVE: We aimed to conduct comprehensive analyses of the association between the expression of NR4A family genes and tumor microenvironment (TME) based on bioinformatics methods.
METHODS: We collected RNA-seq data from 33 cancer types and 20 normal tissue sites from the TCGA and GTEx databases. Expression patterns of NR4A family genes and their associations with DNA methylation, miRNA, overall survival, drug responses, and tumor microenvironment were investigated.
RESULTS: Significant downregulation of all NR4A family genes was observed in 15 cancer types. DNA promoter methylation and expression of NR4A family genes were negatively correlated in five cancers. The expression of 10 miRNAs targeting NR4A family genes was negatively correlated with the expression of NR4A family genes. High expression of all NR4A family genes was associated with poor prognosis in stomach adenocarcinoma and increased expressions of NR4A2 and NR4A3 were associated with poor prognosis in adrenocortical carcinoma. In addition, we found an elevated expression of NR4A2, which enhances the response to various chemotherapeutic drugs, whereas NR4A3 decreases drug sensitivity. Interestingly, in breast cancer, NR4A3 was significantly associated with C2 (IFN-γ dominant), C3 (inflammatory), and C6 (TGF-β dominant) immune subtypes and infiltrated immune cell types, implying both oncogenic and tumor-suppressive functions of NR4A3 in breast cancer.
CONCLUSIONS: The NR4A family genes have the potential to serve as a diagnostic, prognostic, and immunological marker of human cancers.

BACKGROUND: The advent of immunotherapy targeting immune checkpoints has conferred significant clinical advantages to patients with lung adenocarcinoma (LUAD); However, only a limited subset of patients exhibit responsiveness to this treatment. Consequently, there is an imperative need to stratify LUAD patients based on their response to immunotherapy and enhance the therapeutic efficacy of these treatments.
METHODS: The differentially co-expressed genes associated with CD8 + T cells were identified through weighted gene co-expression network analysis (WGCNA) and the Search Tool for the Retrieval of Interacting Genes (STRING) database. These gene signatures facilitated consensus clustering for TCGA-LUAD and GEO cohorts, categorizing them into distinct immune subtypes (C1, C2, C3, and C4). The Tumor Immune Dysfunction and Exclusion (TIDE) model and Immunophenoscore (IPS) analysis were employed to assess the immunotherapy response of these subtypes. Additionally, the impact of inhibitors targeting five hub genes on the interaction between CD8 + T cells and LUAD cells was evaluated using CCK8 and EDU assays. To ascertain the effects of these inhibitors on immune checkpoint genes and the cytotoxicity mediated by CD8 + T cells, flow cytometry, qPCR, and ELISA methods were utilized.
RESULTS: Among the identified immune subtypes, subtypes C1 and C3 were characterized by an abundance of immune components and enhanced immunogenicity. Notably, both C1 and C3 exhibited higher T cell dysfunction scores and elevated expression of immune checkpoint genes. Multi-cohort analysis of Lung Adenocarcinoma (LUAD) suggested that these subtypes might elicit superior responses to immunotherapy and chemotherapy. In vitro experiments involved co-culturing LUAD cells with CD8 + T cells and implementing the inhibition of five pivotal genes to assess their function. The inhibition of these genes mitigated the immunosuppression on CD8 + T cells, reduced the levels of PD1 and PD-L1, and promoted the secretion of IFN-γ and IL-2.
CONCLUSIONS: Collectively, this study delineated LUAD into four distinct subtypes and identified five hub genes correlated with CD8 + T cell activity. It lays the groundwork for refining personalized therapy and immunotherapy strategies for patients with LUAD.

Tuberculosis (TB) is caused by Mycobacterium tuberculosis and is a major global health concern. Neutrophils play a significant role in TB infection and patient outcomes. This study aimed to identify gene modules associated with neutrophil infiltration in TB samples using WGCNA. Gene ontology and enrichment analyses were performed, and a random forest model was constructed to identify differentially expressed genes. K-means clustering was used to classify samples into subtypes, and immune-related scores, PD-L1 expression, HLA expression, and gene enrichment analysis were evaluated. The blue module showed significant correlation with neutrophils and enrichment in immune-related processes. The model exhibited good classification performance, and subtype 1 demonstrated higher immune-related scores, PD-L1 expression, HLA class I molecule expression, and immune-related pathway enrichment. These findings enhance our understanding of TB pathogenesis and provide potential targets for diagnosis and treatment strategies.

OBJECTIVE: The aim of our study is to explore the transcriptional and microbial characteristics of head and neck cancer\'s immune phenotypes using a multi-omics approach.
METHODS: Employing TCGA data, we analyzed head and neck squamous cell carcinoma (HNSCC) immune cells with CIBERSORT and identified differentially expressed genes using DESeq2. Microbial profiles, obtained from the TCMA database, were analyzed using LEfSe algorithm to identify differential microbes in immune cell infiltration (ICI) subgroups. Random Forest algorithm and deep neural network (DNN) were employed to select microbial features and developed a prognosis model.
RESULTS: We categorized HNSCC into three immune subtypes, finding ICI-2 with the worst prognosis and distinct microbial diversity. Our immune-related microbiome (IRM) model outperformed the TNM staging model in predicting survival, linking higher IRM model scores with poorer prognosis, and demonstrating clinical utility over TNM staging. Patients categorized as low-risk by the IRM model showed higher sensitivity to cisplatin and sorafenib treatments.
CONCLUSIONS: This study offers a comprehensive exploration of the ICI landscape in HNSCC. We provide a detailed scenario of immune regulation in HNSCC and report a correlation between differing ICI patterns, intratumor microbiome, and prognosis. This research aids in identifying prime candidates for optimizing treatment strategies in HNSCC.
CONCLUSIONS: This study revealed the microbial signatures associated with immunophenotyping of HNSCC and further found the microbial signatures associated with prognosis. The prognostic model based on IRM microbes is helpful for early prediction of patient prognosis and assisting clinical decision-making.

BACKGROUND: Immunotherapy is applied to breast cancer to resolve the limitations of survival gain in existing treatment modalities. With immunotherapy, a tumor can be classified into immune-inflamed, excluded and desert based on the distribution of immune cells. We assessed the clinicopathological features, each subtype\'s prognostic value and differentially expressed proteins between immune subtypes.
METHODS: Immune subtyping and proteomic analysis were performed on 56 breast cancer cases with neoadjuvant chemotherapy. The immune subtyping was based on the level of tumor-infiltrating lymphocytes (TILs) and Klintrup criteria. If the level of TILs was ≥ 10%, it was classified as immune-inflamed type without consideration of the Klintrup criteria. In cases of 1-9% TIL, Klintrup criteria 1-3 were classified as the immune-excluded subtype and Klintrup criteria not available (NA) was classified as NA. Cases of 1% TILs and Klintrup 0 were classified as the immune-desert subtype. Mass spectrometry was used to identify differentially expressed proteins in formalin-fixed paraffin-embedded biopsy tissues.
RESULTS: Of the 56 cases, 31 (55%) were immune-inflamed, 21 (38%) were immune-excluded, 2 (4%) were immune-desert and 2 (4%) were NA. Welch\'s t-test revealed two differentially expressed proteins between immune-inflamed and immune-excluded/desert subtypes. Coronin-1A was upregulated in immune-inflamed tumors (adjusted p = 0.008) and α-1-antitrypsin was upregulated in immune-excluded/desert tumors (adjusted p = 0.008). Titin was upregulated in pathologic complete response (pCR) than non-pCR among immune-inflamed tumors (adjusted p = 0.036).
CONCLUSIONS: Coronin-1A and α-1-antitrypsin were upregulated in immune-inflamed and immune-excluded/desert subtypes, respectively. Titin\'s elevated expression in pCR within the immune-inflamed subtype may indicate a favorable prognosis. Further studies involving large representative cohorts are necessary to validate these findings.

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%-20% cancer cases have demonstrated durable responses from immune checkpoint blockade. To enhance the efficacy of immunotherapies, combination therapies suppressing multiple immune evasion mechanisms are increasingly contemplated. To better understand immune cell surveillance and diverse immune evasion responses in tumor tissues, we comprehensively characterized the immune landscape of more than 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct immune subtypes based on integrative learning of cell type compositions and pathway activities. We then thoroughly categorized unique genomic, epigenetic, transcriptomic, and proteomic changes associated with each subtype. Further leveraging the deep phosphoproteomic data, we studied kinase activities in different immune subtypes, which revealed potential subtype-specific therapeutic targets. Insights from this work will facilitate the development of future immunotherapy strategies and enhance precision targeting with existing agents.

OBJECTIVE: Although mRNA vaccines have shown certain clinical benefits in multiple malignancies, their therapeutic efficacies against hepatocellular carcinoma (HCC) remains uncertain. This study focused on establishing a novel risk score system based on immune subtypes so as to identify optimal HCC mRNA vaccination population.
METHODS: GEPIA, cBioPortal and TIMER databases were utilized to identify candidate genes for mRNA vaccination in HCC. Subsequently, immune subtypes were constructed based on the candidate genes. According to the differential expressed genes among various immune subtypes, a risk score system was established using machine learning algorithm. Besides, multi-color immunofluorescence of tumor tissues from 72 HCC patients were applied to validate the feasibility and efficiency of the risk score system.
RESULTS: Twelve overexpressed and mutated genes associated with poor survival and APCs infiltration were identified as potential candidate targets for mRNA vaccination. Three immune subtypes (e.g. IS1, IS2 and IS3) with distinct clinicopathological and molecular profiles were constructed according to the 12 candidate genes. Based on the immune subtype, a risk score system was developed, and according to the risk score from low to high, HCC patients were classified into four subgroups on average (e.g. RS1, RS2, RS3 and RS4). RS4 mainly overlapped with IS3, RS1 with IS2, and RS2+RS3 with IS1. ROC analysis also suggested the significant capacity of the risk score to distinguish between the three immune subtypes. Higher risk score exhibited robustly predictive ability for worse survival, which was further independently proved by multi-color immunofluorescence of HCC samples. Notably, RS4 tumors exhibited an increased immunosuppressive phenotype, higher expression of the twelve potential candidate targets and increased genome altered fraction, and therefore might benefit more from vaccination.
CONCLUSIONS: This novel risk score system based on immune subtypes enabled the identification of RS4 tumor that, due to its highly immunosuppressive microenvironment, may benefit from HCC mRNA vaccination.

Sepsis is a life-threatening syndrome resulting from immune system dysfunction that is caused by infection. It is of great importance to analyze the immune characteristics of sepsis, identify the key immune system related genes, and construct diagnostic models for sepsis. In this study, the sepsis transcriptome and expression profiling data were merged into an integrated dataset containing 277 sepsis samples and 117 non-sepsis control samples. Single-sample gene set enrichment analysis (ssGSEA) was used to assess the immune cell infiltration. Two sepsis immune subtypes were identified based on the 22 differential immune cells between the sepsis and the healthy control groups. Weighted gene co-expression network analysis (WCGNA) was used to identify the key module genes. Then, 36 differentially expressed immune-related genes were identified, based on which a robust diagnostic model was constructed with 11 diagnostic genes. The expression of 11 diagnostic genes was finally assessed in the training and validation datasets respectively. In this study, we provide comprehensive insight into the immune features of sepsis and establish a robust diagnostic model for sepsis. These findings may provide new strategies for the early diagnosis of sepsis in the future.

Immunotherapy has transformed treatment for various types of malignancy. However, the benefit of immunotherapy is limited to a minority of patients with mismatch-repair-deficient (dMMR) and microsatellite instability-high (MSI-H) (dMMR-MSI-H) colorectal cancer (CRC). Understanding the complexity and heterogeneity of the tumor immune microenvironment (TIME) and identifying immune-related CRC subtypes will improve antitumor immunotherapy. Here, we review the current status of immunotherapy and typing schemes for CRC. Immune subtypes have been identified based on TIME and prognostic gene signatures that can both partially explain clinical responses to immune checkpoint inhibitors and the prognosis of patients with CRC. Identifying immune subtypes will improve understanding of complex CRC tumor heterogeneity and refine current immunotherapeutic strategies.