Identification of a prognostic signature of epithelial ovarian cancer based on tumor immune microenvironment exploration

This study aims to develop an immune-related genes (IRGs) prognostic signature to stratify the epithelial ovarian cancer (EOC) patients. We identified 332 up- and 154 down-regulated EOC-specific IRGs. As a result, candidate IRGs were idendified to construct prognostic models respectivy for overall survial and progression-free survival. The risk score was validated as a risk factor for prognosis and was used to built a combined nomogram. According to the IRG-related prognostic model, EOC patients were divided into high- and low- risk group and were further explored their association with tumor immune microenvironment (TME). CIBERSORT algorithm showed higher macrophages M1 cell, T cells follicular helper cell and plasma cells infiltrating levels in the low-risk group. In addition, the low-risk group was found with higher immunophenoscore and distinct mutation signatures compared with the high-risk group. These findings may shed light on the development of novel immune biomarkers and target therapy of EOC.     

Development and Validation of a DNA Methylation-related Classifier of Circulating Tumour Cells to Predict Prognosis and to provide a therapeutic strategy in Lung Adenocarcinoma

Background: A significant factor influencing the prognosis of lung adenocarcinoma (LUAD) is tumor metastasis. Studies have shown that abnormal DNA methylation in circulating tumor cells (CTCs) is associated with tumour metastasis. Based on the genes expressed in CTCs that play an important role in DNA methylation, we hope to build a risk model to predict prognosis and provide a therapeutic strategy in LUAD.Methods: The CTC sequencing data for LUAD were obtained from {"type":"entrez-geo","attrs":{"text":"GSE74639","term_id":"74639"}}GSE74639, which contains 10 CTC samples and 6 primary tumour samples. To carefully assess the clinical value, functional status, involvement of the tumor microenvironment (TME) based on the risk model, and genetic variants based on based on data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO), a reliable risk model was successfully built.Results: Three differentially methylated genes (DMGs) of CTCs for LUAD, including mitochondrial ribosomal protein L51 (MRPL51), STE20-like kinase (SLK), and protein regulator of cytokinesis 1(PRC1), were effectively used to construct a risk model. Both the training and validation cohorts'' stability and accuracy of the risk model were evaluated. Each patient in the TCGA-LUAD cohort received a risk score, and based on the median score, they were divided into high- and low-risk groups. The tumors in the high-risk group in this study were classified as "cold" and immunosuppressed, which may be linked to a poor prognosis. The tumors in the low-risk group, however, were deemed "hot" and had immune hyperfunction linked to a positive prognosis. Additionally, patients in the low-risk group showed greater sensitivity to immunotherapy than those in the high-risk group.Conclusions: Based on DMGs of CTCs from LUAD, we successfully developed a predictive risk model and discovered differences in biological function, TME, genetic variation, and clinical outcomes between those at high and low risk group.     

Identification of immunogenic cell death-associated subtypes and characterization of the tumor microenvironment in endometrial cancer

Immunogenic cell death (ICD) is one of the mechanisms regulating cell death, which activates adaptive immunity in immunocompetent hosts and is associated with tumor progression, prognosis and therapeutic response. Endometrial cancer (EC) is one of the most common malignancies of the female genital tract, and the potential role of immunogenic cell death-related genes (IRGs) in the tumor microenvironment (TME) remains unclear. We describe the variation of IRGs and assess the expression patterns in EC samples from The Cancer Genome Atlas and Gene Expression Omnibus cohorts. Based on the expression of 34 IRGs, we identified two different ICD-related clusters and subsequently differentially expressed genes between the two ICD-related clusters were used for the identification of two ICD gene clusters. We identified the clusters and found that alterations in the multilayer IRG were associated with patient prognosis and TME cell infiltration characteristics. On this basis, ICD score risk scores were calculated, and ICD signatures were constructed and validated for their predictive power in EC patients. To help clinicians better apply the ICD signature, an accurate nomogram was constructed. The low ICD risk group was characterized by high microsatellite instability, high tumor mutational load, high IPS score and stronger immune activation. Our comprehensive analysis of IRGs in EC patients suggested a potential role in the tumor immune interstitial microenvironment, clinicopathological features and prognosis. These findings may improve our understanding of the role of ICDs, and provide a new basis for assessing prognosis and developing more effective immunotherapeutic strategies in EC.     

A signature of immune-related gene pairs predicts oncologic outcomes and response to immunotherapy in lung adenocarcinoma

In this study, we established the predictive model for lung adenocarcinoma (LUAD) depending on immune-related gene pairs (IRGPs) signature, which could not consider the technical bias of different platforms. Furthermore, we explored the predictive model with regard to the immune microenvironment and response to immunotherapy and identified specific drugs targeting the IRGPs model. Twenty-three IRGPs were identified and comprised the predictive model. When compared with the high-risk group, the low-risk group displayed a distinctly favorable prognosis and was characterized by increased immune score and decreased tumor purity. In addition, the low-risk group exhibited higher expression of immune checkpoint molecules, lower tumor stemness index, and was much more sensitive to immunotherapy. Lastly, candidate drugs that aimed at LUAD subtype differentiation were identified. The derived IRGPs model is an adverse independent biomarker for estimating oncologic outcomes in LUAD patients, and may be helpful to formulate personalized immunotherapy strategy.     

Integrated pathological analysis to develop a Gal-9 based immune survival stratification to predict the outcome of lung large cell neuroendocrine carcinoma and its usefulness in immunotherapy

This study aimed to integrate the cell spatial organization to develop a Gal-9-based immune survival stratification in the lung large cell neuroendocrine carcinoma (LCNEC) and investigate its potentials to immunotherapy.The expression of Gal-9 and other twelve immune markers were evaluated in 122 cases of surgical LCNEC samples from our center using immunohistochemistry. The Gal-9-based immune survival stratification risk score was constructed and its predictive performance was evaluated. Then, we thoroughly explored the effects of Gal-9 and immune risk score on LCNEC immune pathways, immune micro-environment and immunotherapy sensitivity in different cohort and platform, and made a validation in pathology images using Histology-based Digital-Staining (HDS).In 122 LCNEC samples, 43 cases were positive Gal-9 expression on tumor cells (Gal-9 TC). Increased Gal-9 TC predicted worse overall survival. Gal-9''s interaction with other immune markers added to the immune suppression and immune tolerance in LCNEC. Immune protein marker-based risk score consisting of Gal-9, CD3, CD4, PD-L1, and PD-1 was developed and validated to robustly discriminate survival high-risk or low-risk in LCNEC patients. The high-risk group characterized by immune-desert tumor had less various T cells. The low-risk group featuring immune-inflamed tumor was more likely to respond to anti-PD1 immunotherapy. HDS in 122 LCNEC samples'' 108,369 cells validated that the high-risk group had more tumor cells, less stromal cells, less lymphocytes, higher tumor cell nucleic solidity and lower stromal cells nucleic solidity.An integrated pathological analysis confirms the Gal-9 based immune survival stratification is distinctively related to micro-environment status involved in immune suppression and immune tolerance and could act as a combinatorial biomarker to predict the outcome of LCNEC. These findings may help effectively stratify LCNEC patients sensitive to immunotherapy.     

Survival prediction and response to immune checkpoint inhibitors: A prognostic immune signature for hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the most common cancers all over the world. Several studies have explored if immune-related genes and tumor immune microenvironment could play roles in HCC prognoses. This study is aimed at developing a prognostic signature of HCC based on immune-related genes or tumor immune microenvironment to predict survival and response to immune checkpoint inhibitors (ICIs). We constructed a prognostic signature using bioinformatics method and validated its predictive capability. The mechanisms of the signature prediction were explored with The Cancer Immunome Atlas (TCIA) and mutation analysis. We also explored the association between the signature and immunophenoscore (IPS), which is the marker of ICIs response. A 6 immune-related-gene (6-IRG) signature was developed. It was revealed in a multivariate analysis that the 6-IRG signature was an independent prognostic factor of overall survival and progression-free interval among HCC patients. In the high-risk group of 6-IRG signature score, macrophage M0 cells and regulatory T cells, which are observed associated with poor overall survival in our study, were higher. The low-risk group had a higher IPS, which meant a better response to ICIs. Taken together, we constructed a reliable 6-IRG signature for prediction of survival and response to ICIs. The signature needs further testing for clinical application.     

Comprehensive molecular analysis of a four-pyroptosis-gene signature with prognosis and immune landscape in lung adenocarcinoma

Pyroptosis plays an important role in tumor immunity. However, the biological behavior and prognostic significance of pyroptosis remain unclear. We identified 41 pyroptosis regulators differently expressed in lung adenocarcinoma (LUAD). All cases of LUAD can be classified into two molecular subtypes using unsupervised clustering algorithm. Using multiple analyses, a four-pyroptosis-gene signature was constructed, and all LUAD patients were categorized as low-risk or high-risk with a longer overall survival (OS) time in the low-risk group(P < 0.001). This signature had power prognosis and stratification which was validated by six independent datasets and clinical subtypes. Besides, this signature showed distinct clinical outcomes, immune landscapes in different risk groups. Moreover, the low-risk group had a higher response against immunotherapy with a lower TIDE score. Importantly, this signature surpassed other biomarkers (TIDE, TMB, PD-L1) in predicting prognosis. Overall, the current study might help with precise prognostic prediction and crucial treatment strategies, eventually promoting tailored therapy for LUAD patients.     

Comprehensive analysis of prognostic immune-related genes in the tumor microenvironment of cutaneous melanoma

Cutaneous malignant melanoma (hereafter called melanoma) is one of the most aggressive cancers with increasing incidence and mortality rates worldwide. In this study, we performed a systematic investigation of the tumor microenvironmental and genetic factors associated with melanoma to identify prognostic biomarkers for melanoma. We calculated the immune and stromal scores of melanoma patients from the Cancer Genome Atlas (TCGA) using the ESTIMATE algorithm and found that they were closely associated with patients’ prognosis. Then the differentially expressed genes were obtained based on the immune and stromal scores, and prognostic immune-related genes further identified. Functional analysis and the protein–protein interaction network further revealed that these genes enriched in many immune-related biological processes. In addition, the abundance of six infiltrating immune cells was analyzed using prognostic immune-related genes by TIMER algorithm. The unsupervised clustering analysis using immune-cell proportions revealed eight clusters with distinct survival patterns, suggesting that dendritic cells were most abundant in the microenvironment and CD8⁺ T cells and neutrophils were significantly related to patients’ prognosis. Finally, we validated these genes in three independent cohorts from the Gene Expression Omnibus database. In conclusion, this study comprehensively analyzed the tumor microenvironment and identified prognostic immune-related biomarkers for melanoma.     

基于肿瘤免疫微环境鉴定IDH突变型弥漫性胶质瘤的预后标志物

异柠檬酸脱氢酶(IDH)突变存在于大多数低级别胶质细胞瘤中,免疫逃逸是肿瘤标志性特征之一,免疫治疗在胶质瘤的治疗中的作用越来越重要。利用生信手段分析TCGA、CGGA、GEO数据集中IDH突变胶质瘤的770个免疫相关基因及其临床相关数据,从而获得每个患者的免疫风险评分(IMRS);结合IMRS和临床信息,筛选出6个差异表达基因(TRAF3、ATG10、BID、TAB1、MAP3K1、RPS6)组成IMRS模型并生成诺莫图对患者预后进行评估,发现低风险组患者的总生存期(OS)较高风险组均明显延长。此外,签名相关免疫细胞浸润分析发现肿瘤相关巨噬细胞浸润评分(TAM)与肿瘤相关T细胞浸润评分(TIS)呈明显的负相关,表明高IMRS富集了促肿瘤免疫浸润,而低IMRS则富集了相对较多的抗肿瘤免疫浸润。     

Prognostic and Predictive Value of Tumor-infiltrating Leukocytes and of Immune Checkpoint Molecules PD1 and PDL1 in Clear Cell Renal Cell Carcinoma

INTRODUCTION: Immune checkpoint inhibitors (ICI) have been approved for patients with clear cell renal cell carcinoma (ccRCC), but not all patients benefit from ICI. One reason is the tumor microenvironment (TME) that has substantial influence on patient's prognosis and therapy response. Thus, we comprehensively analyzed the TME of ccRCC regarding prognostic and predictive properties. METHODS: Tumor-infiltrating CD3-positive T-cells, CD8-positive cytotoxic T-lymphocytes (CTLs), regulatory T-cells, B-cells, plasma cells, macrophages, granulocytes, programmed cell death receptor 1 (PD-1), and its ligand PD-L1 were examined in a large hospital-based series of ccRCC with long-term follow-up information (n = 756) and in another patient collective with information on response to nivolumab therapy (n = 8). Tissue microarray technique and digital image analysis were used. Relationship between immune cell infiltration and tumor characteristics, cancer-specific survival (CSS), or response to ICI was examined. RESULTS: Univariate survival analysis revealed that increased tumor-infiltrating B-cells, T-cells, and PD-1-positive cells were significantly associated with favorable CSS and high levels of intratumoral granulocytes, macrophages, cytotoxic T-cells, and PD-L1 significantly with poor CSS. High CTL or B-cell infiltration and high PD-L1 expression of ccRCC tumor cells qualified as independent prognostic biomarkers for patients' CSS. Significantly higher densities of intratumoral T-cells, CTLs, and PD-1-positive immune cells were observed in ccRCC with response to ICI compared with patients with mixed or no response (CD3: p = 0.003; CD8: p = 0.006; PD-1: p = 0.01). DISCUSSION: This study shows that subsets of tumor-infiltrating leukocytes in the TME and also PD-1/PD-L1 provide prognostic and predictive information for patients with ccRCC.     

Characterizing intrinsic molecular features of the immune subtypes of salivary mucoepidermoid carcinoma

IntroductionCharacterizing the tumor microenvironment (TME) and immune landscape of cancer has been a promising step towards discovering new therapeutic biomarkers and guiding precision medicine; however, its application in mucoepidermoid carcinoma (MEC) has been sparse. Here, we conducted a comprehensive study to understand the properties of the TME and immune profiles of MEC.Method20 patients with MEC were collected from Yonsei Head and Neck Cancer Centre, Yonsei University, South Korea. Total RNA sequencing was conducted to determine gene expression profiles. Bioinformatic and immunoinformatic analyses were applied to characterize the TME and identify immunophenotypic subgroups, and to investigate the molecular features that explain the distinct phenotypes.ResultsThe MEC samples were subdivided into two groups, immune hot and immune cold, based on the heterogenous immune cell-infiltration and activation level. The immune-hot subgroup exhibited a higher level of immune activity, including T cell infiltration, cytolytic score, IFN-γ, antigen-presenting machinery, and immune modulator genes. Further characterizing molecular features of two subgroups, downregulation of lipid metabolic regulators, including MLXIPL and FASN, and the migration of chemokines and leukocytes were observed, respectively. And, Group-specific expression of immune checkpoint molecules, such as TIGIT, PD-L2, and CTLA-4, was observed in the immune-hot group, which can be exploited as a potential immunotherapeutic biomarker.ConclusionsImmunophenotypically heterogeneous MEC subgroups analysis has shown distinctive molecular characteristics and provided potential treatment options. These findings yield new insights into TME of MEC and may help next step to study this uncharted cancer.     

Immune-related long noncoding RNA signature for predicting survival and immune checkpoint blockade in hepatocellular carcinoma

Long noncoding RNAs (lncRNAs) show multiple functions, including immune response. Recently, the immune-related lncRNAs have been reported in some cancers. We first investigated the immune-related lncRNA signature as a potential target in hepatocellular carcinoma (HCC) survival. The training set (n = 368) and the independent external validation cohort (n = 115) were used. Immune genes and lncRNAs coexpression were constructed to identify immune-related lncRNAs. Cox regression analyses were perfumed to establish the immune-related lncRNA signature. Regulatory roles of this signature on cancer pathways and the immunologic features were investigated. The correlation between immune checkpoint inhibitors and this signature was examined. In this study, the immune-related lncRNA signature was identified in HCC, which could stratify patients into high- and low-risk groups. This immune-related lncRNA signature was correlated with disease progression and worse survival and was an independent prognostic biomarker. Our immune-related lncRNA signature was still a powerful tool in predicting survival in each stratum of age, gender, and tumor stage. This signature mediated cell cycle, glycolysis, DNA repair, mammalian target of rapamycin signaling, and immunologic characteristics (i.e., natural killer cells vs. Th1 cells down, etc). This signature was associated with immune cell infiltration (i.e., macrophages M0, Tregs, CD4 memory T cells, and macrophages M1, etc.,) and immune checkpoint blockade (ICB) immunotherapy-related molecules (i.e., PD-L1, PD-L2, and IDO1). Our findings suggested that the immune-related lncRNA signature had an important value for survival prediction and may have the potential to measure the response to ICB immunotherapy. This signature may guide the selection of the immunotherapy for HCC.     

Inflammatory response mediates cross-talk with immune function and reveals clinical features in acute myeloid leukemia

Accumulated genetic mutations are an important cause for the development of acute myeloid leukemia (AML), but abnormal changes in the inflammatory microenvironment also have regulatory effects on AML. Exploring the relationship between inflammatory response and pathological features of AML has implications for clinical diagnosis, treatment and prognosis evaluation. We analyzed the expression variation landscape of inflammatory response-related genes (IRRGs) and calculated an inflammatory response score for each sample using the gene set variation analysis (GSVA) algorithm. The differences in clinical- and immune-related characteristics between high- and low-inflammatory response groups were further analyzed. We found that most IRRGs were highly expressed in AML samples, and patients with high inflammatory response had poor prognosis and were accompanied with highly activated chemokine-, cytokine- and adhesion molecule-related signaling pathways, higher infiltration ratios of monocytes, neutrophils and M2 macrophages, high activity of type I/II interferon (IFN) response, and higher expression of immune checkpoints. We also used the Genomics of Drug Sensitivity in Cancer (GDSC) database to predict the sensitivity of AML samples with different inflammatory responses to common drugs, and found that AML samples with low inflammatory response were more sensitive to cytarabine, doxorubicin and midostaurin. SubMap algorithm also demonstrated that high-inflammatory response patients are more suitable for anti-PD-1 immunotherapy. Finally, we constructed a prognostic risk score model to predict the overall survival (OS) of AML patients. Patients with higher risk score had significantly shorter OS, which was confirmed in two validation cohorts. The analysis of inflammatory response patterns can help us better understand the differences in tumor microenvironment (TME) of AML patients, and guide clinical medication and prognosis prediction.     

Characterization of the relationship between FLI1 and immune infiltrate level in tumour immune microenvironment for breast cancer

Breast cancer is the most common cancer and the leading cause of cancer death among women in the world. Tumour-infiltrating lymphocytes were defined as the white blood cells left in the vasculature and localized in tumours. Recently, tumour-infiltrating lymphocytes were found to be associated with good prognosis and response to immunotherapy in tumours. In this study, to examine the influence of FLI1 in immune system in breast cancer, we interrogated the relationship between the FLI1 expression levels with infiltration levels of 28 immune cell types. By splitting the breast cancer samples into high and low expression FLI1 subtypes, we found that the high expression FLI1 subtype was enriched in many immune cell types, and the up-regulated differentially expressed genes between them were enriched in immune system processes, immune-related KEGG pathways and biological processes. In addition, many important immune-related features were found to be positively correlated with the FLI1 expression level. Furthermore, we found that the FLI1 was correlated with the immune-related genes. Our findings may provide useful help for recognizing the relationship between tumour immune microenvironment and FLI1, and may unravel clinical outcomes and immunotherapy utility for FLI1 in breast cancer.     

Tumor microenvironment disparity in multiple primary lung cancers: Impact of non-intrinsic factors,histological subtypes,and genetic aberrations

IntroductionMultiple primary lung cancers (MPLCs) occur in common carcinogenetic risks such as lifestyle, biological aging, immune responses, hormones, and metabolism. Although MPLCs harbor various genetic profiles within the same individuals, differences in the tumor microenvironment (TME) are unclear. We investigated the impact of genetic aberrations, non-intrinsic factors, and pathological subtypes on tumor immunity.Materials and MethodsIn total, 73 surgically resected specimens from 32 patients with MPLC were analyzed. PD-L1 expression in tumor cells (TCs) and immune cells (ICs), CD3-positive tumor-infiltrating lymphocytes (TILs), CD8/CD3 ratios, and FOXP3-positive TILs that compose TMEs were evaluated by immunohistochemistry and classified on a score of 0–2. 38 tumors were sequenced for somatic mutations in 409 cancer-associated genes.ResultsFemales and never or light smokers had a higher incidence of PD-L1-negative tumors and a higher concordance rate. PD-L1 positivity in TCs and ICs was significantly less frequent in EGFR-mutated than in wild-type tumors. Differences in the score of TMEs were observed between the KRAS-mutated-only tumor and the KRAS and TP53-co-mutated tumors, and between the KRAS-mutated-only tumor and the KRAS and STK11-co-mutated tumors. Significantly more FOXP3-high TILs were observed in invasive pathological subtypes than in non-invasive ones.ConclusionComparing TMEs among MPLCs revealed that non-smokers or light smokers and females were unlikely to express PD-L1 regardless of tumor site and confirmed that the EGFR mutations and co-occurring KRAS and STK11 or TP53 mutations were associated with TME. Pathological subtypes may impact the efficacy of immune therapy due to their potential correlations with regulatory T cells.     

High Expression of Tumor HLA-DR Predicts Better Prognosis and Response to Anti-PD-1 Therapy in Laryngeal Squamous Cell Carcinoma

BackgroundHLA-DR is expressed in epithelial and several types of tumor cells. However, the correlation between tumor-expressed HLA-DR (teHLA-DR) and patient outcome as well as its regulation on the tumor microenvironment (TME) of laryngeal squamous cell carcinoma (LSCC) are yet to be elucidated.MethodsHematoxylin and eosin (HE) staining were performed to define the tumor nest and stroma of LSCC tissue microarrays. teHLA-DR tumor cell, CD4⁺ and CD8⁺ tumor-infiltrating T lymphocytes (TITLs) were obtained and analyzed through double-labeling immunofluorescence and immunohistochemical staining. The recurrence-free (RFS) and overall survival (OS) curves were plotted using the Kaplan-Meier method and tested by the log-rank test method. Expression of teHLA-DR⁺ tumor cells and infiltration of T lymphocytes and their corresponding subgroups were analyzed by flow cytometry using fresh LSCC tissue samples.ResultsOur research discovered elevated expressions of multiple MHC-II-related genes in tumor compared to the adjacent normal tissue samples of LSCC patients. We also found that patients in the teHLA-DR high-expression group (teHLA-DR^high) tend to have less tumor recurrence and better survival outcomes compared to those in the teHLA-DR^low group. Intriguingly, teHLA-DR⁺ tumor cells had significantly higher PD-L1 and PD-L2 expression and their TME showed increased infiltrated T lymphocytes (TITLs). Flow cytometry analysis and IHC staining indicated that CD4⁺ TITLs but not CD3⁺ total TITLs or CD8⁺ TITLs were significantly enriched in teHLA-DR⁺ tumors.ConclusionsteHLA-DR may be a predictive marker for favorable prognosis and response to anti-PD-1/PD-L1 therapy of LSCC, possibly due to the increased CD4⁺ TITLs in the TME.     

Identification of an immune-related RNA-binding protein signature to predict survival and targeted therapy responses in liver cancer

RNA-binding proteins (RBPs) play crucial roles in multiple cancers. However, very few RBPs and their association with immune genes have been systematically studied in liver cancer (LC). We aimed to identify an immune-related RBP signature to predict the survival of LC patients. Bioinformatics methods were used to identify differentially expressed, immune-related, and prognostic RBPs and to develop an immune-related RBP signature based on data from the Cancer Genome Atlas (TCGA) cohort. We obtained eight differentially expressed, immune-related, and prognostic RBPs to construct a risk signature. The signature could effectively distinguish between high- and low-risk patients, and its predictive capacity was validated in the International Cancer Genomics Consortium (ICGC) cohort. We speculated that the high-risk group was more sensitive to targeted therapy. The immune-related RBP signature is an independent prognostic biomarker for LC patients and can expand the application of targeted therapy through patient stratification.