Development and verification of an immune-related gene pairs prognostic signature in ovarian cancer

Ovarian cancer (OV) is the most common gynaecological cancer worldwide. Immunotherapy has recently been proven to be an effective treatment strategy. The work here attempts to produce a prognostic immune-related gene pair (IRGP) signature to estimate OV patient survival. The Gene Expression Omnibus (GEO) and Cancer Genome Atlas (TCGA) databases provided the genetic expression profiles and clinical data of OV patients. Based on the InnateDB database and the least absolute shrinkage and selection operator (LASSO) regression model, we first identified a 17-IRGP signature associated with survival. The average area under the curve (AUC) values of the training, validation, and all TCGA sets were 0.869, 0.712, and 0.778, respectively. The 17-IRGP signature noticeably split patients into high- and low-risk groups with different prognostic outcomes. As suggested by a functional study, some biological pathways, including the Toll-like receptor and chemokine signalling pathways, were significantly negatively correlated with risk scores; however, pathways such as the p53 and apoptosis signalling pathways had a positive correlation. Moreover, tumour stage III, IV, grade G1/G2, and G3/G4 samples had significant differences in risk scores. In conclusion, an effective 17-IRGP signature was produced to predict prognostic outcomes in OV, providing new insights into immunological biomarkers.     

Identification of two molecular subtypes of dysregulated immune lncRNAs in ovarian cancer

Long non-coding RNA (lncRNA) has increasingly been identified as a key regulator in pathologies such as cancer. Multiple platforms were used for comprehensive analysis of ovarian cancer to identify molecular subgroups. However, lncRNA and its role in mapping the ovarian cancer subpopulation are still largely unknown. RNA-sequencing and clinical characteristics of ovarian cancer were acquired from The Cancer Genome Atlas database (TCGA). A total of 52 lncRNAs were identified as aberrant immune lncRNAs specific to ovarian cancer. We redefined two different molecular subtypes, C1(188) and C2(184 samples), in “iClusterPlus” R package, among which C2 grouped ovarian cancer samples have higher survival probability and longer median survival time (P <0.05) with activated IFN-gamma response, Wound Healing and Cytotoxic lymphocytes signal; 456 differentially expressed genes were acquired in C1 and C2 subtypes using limma (3.40.6) package, among which 419 were up-regulated and 37 were down-regulated, in TCGA dataset. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) functional enrichment analysis revealed that these genes were actively involved in ECM-receptor interaction, PI3K-Akt signaling pathway interaction KEGG pathway. Compared with the existing immune subtype, the Cluster2 sample showed a substantial increase in the proportion of the existing C2 immune subtype, accounting for 81.37%, which was associated with good prognosis. Our C1 subtype contains only 56.49% of the existing immune C1 and C4, which also explains the poor prognosis of C1. Furthermore, 52 immune-related lncRNAs were used to divide the TCGA-endometrial cancer and cervical cancer samples into two categories, and C2 had a good prognosis. The differentially expressed genes were highly correlated with immune-cell-related pathways. Based on lncRNA, two molecular subtypes of ovarian cancer were identified and had significant prognostic differences and immunological characteristics.     

Identification of a prognostic signature based on immune-related genes in bladder cancer

Bladder cancer (BLCA) has a high incidence and recurrence rate, and the effect of immunotherapy varies from person to person. Immune-related genes (IRGs) have been shown to be associated with immunotherapy and prognosis in many other cancers, but their role in immunogenic BLCA is less well defined. In this study, we constructed an eight-IRG risk model, which demonstrated strong prognostic and immunotherapeutic predictive power. The signature was significantly related to tumor clinicopathological characteristics, tumor class, immune cell infiltration and mutation status. Additionally, a nomogram containing the risk score and other potential risk factors could effectively predict the long-term overall survival probability of BLCA patients. The enriched mechanisms identified by gene set enrichment analysis suggested that the reason why this signature can accurately distinguish high- and low-risk populations may be closely related to the different degrees of innate immune response and T cell activation in different patients.     

Construction of an immune-related LncRNA signature with prognostic significance for bladder cancer

Bladder cancer (BLCA) is one of the most common urological cancer with increasing cases and deaths every year. In the present study, we aim to construct an immune-related prognostic lncRNA signature (IRPLS) in bladder cancer (BLCA) patients and explore its immunogenomic implications in pan-cancers. First, the immune-related differentially expressed lncRNAs (IRDELs) were identified by ‘limma’ R package and the score of IRPLS in every patient were evaluated by Cox regression. The dysregulation of IRDELs expression between cancer and para-cancer normal tissues was validated through RT-qPCR. Then, we further explore the biological functions of a novel lncRNA from IRPLS, RP11-89 in BLCA using CCK8 assay, Transwell assay and Apoptosis analysis, which indicated that RP11-89 was able to promote cell proliferation and invasive capacity while inhibits cell apoptosis in BLCA. In addition, we performed bioinformatic methods and RIP to investigate and validate the RP11-89/miR-27a-3p/PPARγ pathway in order to explore the mechanism. Next, CIBERSORT and ESTIMATE algorithm were used to evaluate abundance of tumour-infiltrating immune cells and scores of tumour environment elements in BLCA with different level of IRPLS risk scores. Finally, multiple bioinformatic methods were performed to show us the immune landscape of these four lncRNAs for pan-cancers. In conclusion, this study first constructed an immune-related prognostic lncRNA signature, which consists of RP11-89, PSORS1C3, LINC02672 and MIR100HG and might shed lights on novel targets for individualized immunotherapy for BLCA patients.     

Identification of prognostic signature in cancer based on DNA methylation interaction network

Background

The identification of prognostic biomarkers for cancer patients is essential for cancer research. These days, DNA methylation has been proved to be associated with cancer prognosis. However, there are few methods which identify the prognostic markers based on DNA methylation data systematically, especially considering the interaction among DNA methylation sites.

Methods

In this paper, we first evaluated the stabilities of microRNA, mRNA, and DNA methylation data in prognosis of cancer. After that, a rank-based method was applied to construct a DNA methylation interaction network. In this network, nodes with the largest degrees (10% of all the nodes) were selected as hubs. Cox regression was applied to select the hubs as prognostic signature. In this prognostic signature, DNA methylation levels of each DNA methylation site are correlated with the outcomes of cancer patients. After obtaining these prognostic genes, we performed the survival analysis in the training group and the test group to verify the reliability of these genes.

Results

We applied our method in three cancers (ovarian cancer, breast cancer and Glioblastoma Multiforme).In all the three cancers, there are more common ones of prognostic genes selected from different samples in DNA methylation data, compared with gene expression data and miRNA expression data, which indicates the DNA methylation data may be more stable in cancer prognosis. Power-law distribution fitting suggests that the DNA methylation interaction networks are scale-free. And the hubs selected from the three networks are all enriched by cancer related pathways. The gene signatures were obtained for the three cancers respectively, and survival analysis shows they can distinguish the outcomes of tumor patients in both the training data sets and test data sets, which outperformed the control signatures.

Conclusions

A computational method was proposed to construct DNA methylation interaction network and this network could be used to select prognostic signatures in cancer.

     

Clinical prognostic evaluation of immunocytes in different molecular subtypes of breast cancer

To retrospectively analyze the relationship between preoperative blood parameters and postoperative clinical outcomes in patients with different molecular subtypes of breast cancer (BC), a cohort of 601 patients with BC in the Third Affiliated Hospital, Sun Yat-sen University, was retrospectively reviewed. They were categorized into four subtypes according to the expression of ER, PR, HER-2, and KI-67%. White blood cell, neutrophil, lymphocyte, monocyte, eosinophil, basophil, and platelet counts, the neutrophil-to-lymphocyte ratio (NLR), the neutrophil-to-monocyte ratio (NMR), the lymphocyte-to-monocyte ratio (LMR), and the platelet-to-lymphocyte ratio (PLR) were recorded. Univariate and multivariate analyses were performed to identify the relationship between parameters and ratios and disease-free survival (DFS) and overall survival (OS). Luminal subtypes of BC had smaller tumor volume, better differentiation degree of invasive ductal carcinoma, less lymph node metastasis, and better clinical outcome than the HER-2 overexpression and triple-negative BC (TNBC) subtypes. In multivariate analysis, age and LMR were the independent prognostic factors of DFS in patients with luminal A (age, p = 0.005; LMR, P = 0.026); PLR in patients with luminal B (DFS; p = 0.032; OS, p= 0.012); LMR in patients with HER-2 overexpression (DFS; p = 0.008; OS, p = 0.017); and NLR for DFS (p = 0.014); and WBC for OS (p = 0.008) in patients with TNBC. LMR was the benign predictor of luminal A and HER-2 overexpression. PLR was the adverse predictor of luminal B. WBC and NLR were the adverse predictors of TNBC. Therefore, these peripheral blood parameters can play an important role in the diagnosis and treatment of patients with different molecular subtypes of BC.     

Identification of a five-gene signature of the RGS gene family with prognostic value in ovarian cancer

The RGS (regulator of G protein signaling) gene family, which includes negative regulators of G protein-coupled receptors, comprises important drug targets for malignant tumors. It is thus of great significance to explore the value of RGS family genes for diagnostic and prognostic prediction in ovarian cancer. The RNA-seq, immunophenotype, and stem cell index data of pan-cancer, The Cancer Genome Atlas (TCGA) data, and GTEx data of ovarian cancer were downloaded from the UCSC Xena database. In the pan-cancer database, the expression level of RGS1, RGS18, RGS19, and RGS13 was positively correlated with stromal and immune cell scores. Cancer patients with high RGS18 expression were more sensitive to cyclophosphamide and nelarabine, whereas those with high RGS19 expression were more sensitive to cladribine and nelarabine. The relationship between RGS family gene expression and overall survival (OS) and progression-free survival (PFS) of ovarian cancer patients was analyzed using the KM-plotter database, RGS17, RGS16, RGS1, and RGS8 could be used as diagnostic biomarkers of the immune subtype of ovarian cancer, and RGS10 and RGS16 could be used as biomarkers to predict the clinical stage of this disease. Further, Lasso cox analysis identified a five-gene risk score (RGS11, RGS10, RGS13, RGS4, and RGS3). Multivariate COX analysis showed that the risk score was an independent prognostic factor for patients with ovarian cancer. Immunohistochemistry and the HPA protein database confirmed that the five-gene signature is overexpressed in ovarian cancer. GSEA showed that it is mainly involved in the ECM-receptor interaction, TGF-beta signaling pathway, Wnt signaling pathway, and chemokine signaling pathway, which promote the occurrence and development of ovarian cancer. The prediction model of ovarian cancer constructed using RGS family genes is of great significance for clinical decision making and the personalized treatment of patients with ovarian cancer.     

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.     

Prediction of overall survival and response to immune checkpoint inhibitors: An immune-related signature for gastric cancer

Gastric cancer (GC) is common in East Asia and South and Central America. Most GC patients miss the opportunities for surgery. Despite their therapeutic potential, immune checkpoint inhibitors (ICIs) only work in part of patients with GC. Thus, this study was aimed at constructing a signature for diagnosis, prognosis, and prediction of response to ICIs. A multivariate analysis showed that the 8-immune-related-gene (IRG) signature was an independent prognostic factor of overall survival among GC patients. In the high-risk group of 8IRG signature risk score, the fractions of CD4 T cells, macrophage M2 and monocyte, which is associated with the progression of cancers, were higher. The low-risk group had a higher immunophenoscore, which meant a better response to ICIs.