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Muscle‐invasive bladder cancer (MIBC) is a disease characterized by molecular and clinical heterogeneity, posing challenges in selecting the most appropriate treatment in clinical settings. Considering the significant role of CD4+ T cells, there is an emerging need to integrate CD4+ T cells with molecular subtypes to refine classification. We conducted a comprehensive study involving 895 MIBC patients from four independent cohorts. The Zhongshan Hospital (ZSHS) and The Cancer Genome Atlas (TCGA) cohorts were included to investigate chemotherapeutic response. The IMvigor210 cohort was included to assess the immunotherapeutic response. NCT03179943 was used to evaluate the clinical response to a combination of immune checkpoint blockade (ICB) and chemotherapy. Additionally, we evaluated genomic characteristics and the immune microenvironment to gain deeper insights into the distinctive features of each subtype. We unveiled four immune‐molecular subtypes, each exhibiting distinct clinical outcomes and molecular characteristics. These subtypes include luminal CD4+ Thigh, which demonstrated benefits from both immunotherapy and chemotherapy; luminal CD4+ Tlow, characterized by the highest level of fibroblast growth factor receptor 3 (FGFR3) mutation, thus indicating potential responsiveness to FGFR inhibitors; basal CD4+ Thigh, which could benefit from a combination of ICB and chemotherapy; and basal CD4+ Tlow, characterized by an immune suppression microenvironment and likely to benefit from transforming growth factor‐β (TGF‐β) inhibition. This immune‐molecular classification offers new possibilities for optimizing therapeutic interventions in MIBC. This study defined an immune‐molecular subtyping classification with specific genomic characteristics, immune phenotypes, oncogenic pathways, gene expression, and clinical outcomes based on integrating CD4+ T cells and molecular subtypes, which is hoped to serve as a foundation for developing tailored therapeutic approaches for MIBC patients.

Objective Due to limited immunological profiles of high‐grade serous ovarian cancer (HGSOC), we aimed to characterize its molecular features to determine whether a specific subset that can respond to immunotherapy exists. Materials and Methods A training cohort of 418 HGSOC samples from TCGA was analysed by consensus non‐negative matrix factorization. We correlated the expression patterns with the presence of immune cell infiltrates, immune regulatory molecules and other genomic or epigenetic features. Two independent cohorts containing 482 HGSOCs and in vitro experiments were used for validation. Results We identified immune and non‐immune groups where the former was enriched in signatures that reflect immune cells, infiltration and PD‐1 signalling (all, P < 0.001), and presented with a lower chromosomal aberrations but increased neoantigens, tumour mutation burden, and microsatellite instability (all, P < 0.05); this group was further refined into two microenvironment‐based subtypes characterized by either immunoactivation or carcinoma‐associated fibroblasts (CAFs) and distinct prognosis. CAFs‐immune subtype was enriched for factors that mediate immunosuppression and promote tumour progression, including highly expressed stromal signature, TGF‐β signalling, epithelial‐mesenchymal transition and tumour‐associated M2‐polarized macrophages (all, P < 0.001). Robustness of these immune‐specific subtypes was verified in validation cohorts, and in vitro experiments indicated that activated‐immune subtype may benefit from anti‐PD1 antibody therapy (P < 0.05). Conclusion Our findings revealed two immune subtypes with different responses to immunotherapy and indicated that some HGSOCs may be susceptible to immunotherapies or combination therapies. A comprehensive profiling of high‐grade serous ovarian cancer based on multifarious molecular features identified an immune group in high‐grade serous ovarian cancer that contains two robust microenvironment‐based subtypes with distinct likelihoods of response to immunotherapies that may also represent ideal immunotherapy candidates.

Hepatocellular carcinoma (HCC) is one of the most prevalent and fatal digestive tumors. Treatment for this disease has been constraint by heterogeneity of this group of tumors, which has greatly limited the progress in personalized therapy. Although existing studies have revealed the genetic and epigenetic blueprints that drive HCCs, many of the molecular mechanisms that lead to HCCs remain elusive. Recent advances in techniques for studying functional genomics, such as genome sequencing and transcriptomic analyses, have led to the discovery of molecular mechanisms that participate in the initiation and evolution of HCC. Integrative multi‐omics analyses have identified several molecular subtypes of HCC associated with specific molecular characteristics and clinical outcomes. Deciphering similar molecular features among highly heterogeneous HCC patients is a prerequisite to implementation of personalized therapeutics. This review summarizes the current research progresses in precision therapy on the backbone of molecular subtypes of HCC.

Background The mRNA-based cancer vaccine has been considered a promising strategy and the next hotspot in cancer immunotherapy. However, its application on cholangiocarcinoma remains largely uncharacterized. This study aimed to identify potential antigens of cholangiocarcinoma for development of anti-cholangiocarcinoma mRNA vaccine, and determine immune subtypes of cholangiocarcinoma for selection of suitable patients from an extremely heterogeneous population. Methods Gene expression profiles and corresponding clinical information were collected from GEO and TCGA, respectively. cBioPortal was used to visualize and compare genetic alterations. GEPIA2 was used to calculate the prognostic index of the selected antigens. TIMER was used to visualize the correlation between the infiltration of antigen-presenting cells and the expression of the identified antigens. Consensus clustering analysis was performed to identify the immune subtypes. Graph learning-based dimensionality reduction analysis was conducted to visualize the immune landscape of cholangiocarcinoma. Results Three tumor antigens, such as CD247, FCGR1A, and TRRAP, correlated with superior prognoses and infiltration of antigen-presenting cells were identified in cholangiocarcinoma. Cholangiocarcinoma patients were stratified into two immune subtypes characterized by differential molecular, cellular and clinical features. Patients with the IS1 tumor had immune “hot” and immunosuppressive phenotype, whereas those with the IS2 tumor had immune “cold” phenotype. Interestingly, patients with the IS2 tumor had a superior survival than those with the IS1 tumor. Furthermore, distinct expression of immune checkpoints and immunogenic cell death modulators was observed between different immune subtype tumors. Finally, the immune landscape of cholangiocarcinoma revealed immune cell components in individual patient. Conclusions CD247, FCGR1A, and TRRAP are potential antigens for mRNA vaccine development against cholangiocarcinoma, specifically for patients with IS2 tumors. Therefore, this study provides a theoretical basis for the anti-cholangiocarcinoma mRNA vaccine and defines suitable patients for vaccination.

Mounting evidence shows that MicroRNAs (miRNAs) and their target genes are aberrantly expressed in many cancers and are linked to tumor occurrence and progression, especially in esophageal cancer (EC). This study purposed to explore new biomarkers related to the prognosis of EC and to uncover their potential mechanisms in promoting tumor progression. We identified 162 differentially expressed miRNAs and 4555 differentially expressed mRNAs in EC. Then, a risk model involving three miRNAs (miR‐4521, miR‐3682‐3p, and miR‐1269a) was designed to predict prognosis in EC patients. Furthermore, 7 target genes (Rho GTPase‐activating protein 24, Chromobox 3, Contactin‐associated protein 2, ELOVL fatty acid elongase 5, LIF receptor subunit alpha, transmembrane protein 44, and transmembrane protein 67) were selected for Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses to reveal their potential mechanisms in promoting EC progression. After a series of correlation analyses, miRNA target genes were found to be significantly positively or negatively associated with immune infiltration, tumor microenvironment, cancer stemness properties, and tumor mutation burden at different degrees in EC. To further elucidate the role of miRNA signature in cancer progression, we performed a pan‐cancer analysis to determine whether these genes exert similar effects on other tumors. Interestingly, the miRNA target genes altered expression on tumor immunity; however, pan‐cancer progression was the same as that of EC. Thus, we explored the immune landscape of the miRNA signature and its target genes in EC and pan‐cancer. These findings demonstrated the versatility and effectiveness of our model in various cancers and provided a new direction for cancer management. MicroRNAs and their target genes play pivotal role in esophageal cancer (EC) progression. Here, we established a miRNA‐based prediction model in predicting patient prognosis and explored their target genes. We further detected the role of miRNA target genes in immune landscape in EC and pan‐cancer, which could provide with a new direction for cancer management in EC.

Pancreatic cancer is characterized by inter-tumoral and intra-tumoral heterogeneity, especially in genetic alteration and microenvironment. Conventional therapeutic strategies for pancreatic cancer usually suffer resistance, highlighting the necessity for personalized precise treatment. Cancer vaccines have become promising alternatives for pancreatic cancer treatment because of their multifaceted advantages including multiple targeting, minimal nonspecific effects, broad therapeutic window, low toxicity, and induction of persistent immunological memory. Multiple conventional vaccines based on the cells, microorganisms, exosomes, proteins, peptides, or DNA against pancreatic cancer have been developed; however, their overall efficacy remains unsatisfactory. Compared with these vaccine modalities, messager RNA (mRNA)-based vaccines offer technical and conceptional advances in personalized precise treatment, and thus represent a potentially cutting-edge option in novel therapeutic approaches for pancreatic cancer. This review summarizes the current progress on pancreatic cancer vaccines, highlights the superiority of mRNA vaccines over other conventional vaccines, and proposes the viable tactic for designing and applying personalized mRNA vaccines for the precise treatment of pancreatic cancer.

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.

BackgroundThe heterogeneity limits the effective application of immune checkpoint inhibitors for patients with stomach adenocarcinoma (STAD). Precise immunotyping can help select people who may benefit from immunotherapy and guide postoperative management by describing the characteristics of tumor microenvironment.MethodsGene expression profiles and clinical information of patients were collected from ACRG and TCGA-STAD datasets. The immune subtypes (ISs) were identified by consensus clustering analysis. The tumor immune microenvironments (TIME) of each IS were characterized using a series of immunogenomics methods and further confirmed by multiplex immunohistochemistry (mIHC) staining in clinical samples. Two online datasets and one in-house dataset were utilized to construct and validate a prognostic immune-related gene (IRG) signature.ResultsSTAD patients were stratified into five reproducible ISs. IS1 (immune deserve subtype) had low immune infiltration and the highest degree of HER2 gene mutation. With abundant CD8+ T cells infiltration and activated cytotoxicity reaction, patients in the IS2 (immune-activated subtype) had the best overall survival (OS). IS3 and IS4 subtypes were both in the reactive stroma state and indicated the worst prognosis. However, IS3 (immune-inhibited subtype) was characterized by enrichment of FAP+ fibroblasts and upregulated TGF-β signaling pathway, while IS4 (activated stroma subtype) was characterized by enrichment of ACTA2+ fibroblasts. In addition, mIHC staining confirmed that TGF-β upregulated FAP+ fibroblasts were independent risk factor of OS. IS5 (chronic inflammation subtype) displayed moderate immune cells infiltration and had a relatively good survival. Lastly, we developed a nine-IRG signature model with a robust performance on overall survival prognostication.ConclusionsThe immunotyping is indicative for characterize the TIME heterogeneity and the prediction of tumor prognosis for STADs, which may provide valuable stratification for the design of future immunotherapy.

Background Considering the molecular heterogeneity of sarcomas and their immunologically quiet character, immunotherapy (e.g., immune checkpoint inhibitors) plays a viable role in only a subset of these tumors. This study aimed to determine the immune subtypes (IMSs) of sarcomas for selecting suitable patients from an extremely heterogeneous population. Results By performing consensus clustering analysis of the gene expression profiles of 538 patients with sarcomas in online databases, we stratified sarcomas into three IMSs characterized by different immune cell features, tumor mutational burdens (TMBs), gene mutations, and clinical outcomes. IMS1 showed an immune “hot” and immunosuppressive phenotype, the highest frequencies of CSMD3 mutation but the lowest frequencies of HMCN1 and LAMA2 mutations; these patients had the worst progression-free survival (PFS). IMS2 was defined by a high TMB and more gene mutations, but had the lowest frequency of MND1 mutations. IMS3 displayed the highest MDN1 expression level and an immune “cold” phenotype, these patients had the worst PFS. Each subtype was associated with different expression levels of immunogenic cell death modulators and immune checkpoints. Moreover, we applied graph learning-based dimensionality reduction to the immune landscape and identified significant intra-cluster heterogeneity within each IMS. Finally, we developed and validated an immune gene signature with good prognostic performance. Conclusions Our results provide a conceptual framework for understanding the immunological heterogeneity of sarcomas. The identification of immune-related subtypes may facilitate optimal selection of sarcoma patients who will respond to appropriate therapeutic strategies.

Background Inhibitors targeting immune checkpoints, such as PD-1/PD-L1 and CTLA-4, have prolonged survival in small groups of non-small cell lung cancer (NSCLC) patients, but biomarkers predictive of the response to the immune checkpoint inhibitors (ICIs) remain rare. Methods The nonnegative matrix factorization (NMF) was performed for TCGA-NSCLC tumor samples based on the LM22 immune signature to construct subgroups. Characterization of NMF subgroups involved the single sample gene set variation analysis (ssGSVA), and mutation/copy number alteration and methylation analyses. Construction of RNA interaction network was based on the identification of differentially expressed RNAs (DERs). The prognostic predictor was constructed by a LASSO-Cox regression model. Four GEO datasets were used for the validation analysis. Results Four immune based NMF subgroups among NSCLC patients were identified. Genetic and epigenetic analyses between subgroups revealed an important role of somatic copy number alterations in determining the immune checkpoint expression on specific immune cells. Seven hub genes were recognized in the regulatory network closely related to the immune phenotype, and a three-gene prognosis predictor was constructed. Conclusions Our study established an immune-based prognosis predictor, which might have the potential to select subgroups benefiting from the ICI treatment, for NSCLC patients using publicly available databases.