A novel tailored immune gene pairs signature for overall survival prediction in lower-grade gliomas

Lower-grade gliomas (LGGs) have a good prognosis with a wide range of overall survival (OS) outcomes. An accurate prognostic system can better predict survival time. An RNA-Sequencing (RNA-seq) prognostic signature showed a better predictive power than clinical predictor models. A signature constructed using gene pairs can transcend changes from biological heterogeneity, technical biases, and different measurement platforms. RNA-seq coupled with corresponding clinical information were extracted from The Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA). Immune-related gene pairs (IRGPs) were used to establish a prognostic signature through univariate and multivariate Cox proportional hazards regression. Weighted gene co-expression network analysis (WGCNA) was used to evaluate module eigengenes correlating with immune cell infiltration and to construct gene co-expression networks. Samples in the training and testing cohorts were dichotomized into high- and low-risk groups. Risk score was identified as an independent predictor, and exhibited a closed relationship with prognosis. WGCNA presented a gene set that was positively correlated with age, WHO grade, isocitrate dehydrogenase (IDH) mutation status, 1p/19 codeletion, risk score, and immune cell infiltrations (CD4 T cells, B cells, dendritic cells, and macrophages). A nomogram comprising of age, WHO grade, 1p/19q codeletion, and three gene pairs (BIRC5|SSTR2, BMP2|TNFRSF12A, and NRG3|TGFB2) was established as a tool for predicting OS. The IPGPs signature, which is associated with immune cell infiltration, is a novel tailored tool for individual-level prediction.     

Profiles of immune-related genes and immune cell infiltration in the tumor microenvironment of diffuse lower-grade gliomas

The tumor microenvironment is highly correlated with tumor occurrence, progress, and prognosis. We aimed to investigate the immune-related gene (IRG) expression and immune infiltration pattern in the tumor microenvironment of lower-grade glioma (LGG). We employed the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) algorithm to calculate immune and stromal scores and identify prognostic IRG based on The Cancer Genome Atlas data set. The potential molecular functions of these genes were explored with the help of functional enrichment analysis and the protein–protein interaction network. Remarkably, three cohorts that were downloaded from the Chinese Glioma Genome Atlas database were analyzed to further verify the prognostic values of these genes. Moreover, the Tumor IMmune Estimation Resource (TIMER) algorithm was used to estimate the abundance of infiltrating immune cells and explore the immune infiltration pattern in LGG. And unsupervised cluster analysis determined three clusters of the immune infiltration pattern and indicated that CD8⁺ T cells and macrophages were significantly associated with LGG outcomes. Altogether, our study identified a list of prognostic IRGs and provided a perspective to explore the immune infiltration pattern in LGG.     

Ferroptosis-related gene CHAC1 is a valid indicator for the poor prognosis of kidney renal clear cell carcinoma

To evaluate the validity of CHAC1 for predicting the prognosis of kidney renal clear cell carcinoma (KIRC) and to explore its therapeutic potential for KIRC, we conducted several bioinformatic analyses using the sequencing data and clinical information derived from online databases. We found CHAC1 is down-regulated in KIRC samples when compared with normal samples but up-regulated in KIRC samples with relatively higher malignancy and later stages. Univariate cox analysis and multivariate cox regression analysis were conducted and the results revealed up-regulated CHAC1 is an independent risk factor for poor prognosis of KIRC. Further, the nomogram model based on the result of multivariate cox regression analysis was constructed and effectively predicted patients' 1-year, 3-year and 5-year survival respectively. The correlation analyses showed CHAC1 is associated with the immune pathway markers of memory B cell, natural killer cell and type1 T helper cell as well as the checkpoint genes like ADORA2A, CD200, CD44, CD70, HHLA2, NRP1, PDCD1LG2 and TNFRSF18. Furthermore, experiments in vitro indicated CHAC1 could induce cell death in KIRC cell lines but had limited influence on cell migration and cell invasion. In conclusion, CHAC1 is found a valid indicator for poor prognosis of kidney renal clear cell carcinoma.     

Identification and validation of a six-lncRNA prognostic signature with its ceRNA networks and candidate drugs in lower-grade gliomas

Gliomas account for 75% of the primary malignant brain tumors and a majority of lower-grade gliomas (LGG) inevitably develop into glioblastoma. The dysregulation of lncRNAs play a crucial role in LGG. In the present study, we first screened out six differentially expressed lncRNAs (AC021739.2, AL031722.1, AL354740.1, FGD5-AS1, LINC00844, and NEAT1) based on TCGA and GTEx RNA-seq databases. LncRNA prognostic signature was then established by Kaplan–Meier and multivariate Cox proportional hazards regression, with its predictive value validated by time-dependent receiver operating characteristic (ROC) curves. After lncRNA-miRNA-mRNA regulatory networks were established by Cytoscape 3.7.2, Gene Oncology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were performed, with results enriched in various malignancy-related functions and pathways. Finally, six putative drugs (irinotecan, camptothecin, mitoxantrone, azacitidine, mestranol, and enilconazole) were predicted by Connectivity Map. In conclusion, we identified a 6-lncRNA prognostic signature with its ceRNA networks, and six candidate drugs against LGG.     

NAMPT,GRN, and SERPINE1 signature as predictor of disease progression and survival in gliomas

Adipose tissue is an important source of adipokines involved in anti- and pro-inflammatory effects. Their involvement in certain cancers such as breast and colon cancer is known but in gliomas it remains unexplored till date. The aim of this study was to assess the status of adipokines as prognostic markers of gliomas (low grade gliomas [LGG] and glioblastoma mutiforme [GBM]). Expression status (messenger RNA [mRNA]), overall survival (OS) and disease-free survival (DFS) was identified using gene expression profiling interactive analysis server. Clinicopathological analysis and correlation between different adipokines was performed using Xena server. Protein expression status was analyzed using tissue sections from the Human Protein Atlas. Out of 11 adipokines studied visfatin (NAMPT), apelin (APLN), granulin (GRN), serpin peptidase inhibitor/plasminogen activator inhibitor type 1 (PAI-1) member 1 (SERPINE1), and chemokine (C-C motif) ligand 2 (CCL2) mRNA levels were significantly upregulated in both LGG and GBM. Interleukin 6 (IL6) mRNA was found be significantly upregulated only in GBM. NAMPT, GRN, SERPINE1, and IL6 showed reduced OS as well as worst DFS for patients having higher mRNA expression in LGG. Increased expression of CCL2 showed worst OS in LGG patients while resistin (RETN) and GRN showed the worst OS in GBM patients. Higher expression of RETN, GRN, IL6, SERPINE1, and CCL2 were found to be positively correlated with shorter DFS in GBM. In the clinicopathological analysis, NAMPT, GRN, IL6, SERPINE1, and CCL2 expressions were significantly associated between the neoplasm histological G2 and G3 grades. Furthermore, expression of NAMPT, GRN, tumor necrosis factor, IL6, SERPINE1, and CCL2 were significantly associated with histological type in LGG patients. NAMPT, GRN, SERPINE1, CCL2, and RETN expression were found to be correlated with each other in gliomas. Finally, NAMPT, GRN, and SERPINE1 were also found to be upregulated using immunohistochemistry in a lower grade and high grade gliomas as compared to normal cells. In conclusion, we have identified key adipokines, namely NAMPT, GRN, and SERPINE1 as potential diagnostic and prognostic markers that might be instrumental in the development and progression of gliomas.     

Identification of a 4-miRNA signature as a potential prognostic biomarker for pancreatic adenocarcinoma

An microRNA (miRNA) signature to predict the clinical outcome of pancreatic adenocarcinoma (PAAD) is still lacking. In the current study, we aimed at identifying and evaluating a prognostic miRNA signature for patients with PAAD. The miRNA expression profile and the clinical information regarding patients with PAAD were recruited from The Cancer Genome Atlas database. Differentially expressed miRNAs were identified between normal and tumor samples. By means of survival analysis, a 4-miRNA signature for predicting patients' with PAAD overall survival (OS) was constructed. Receiver operating characteristic (ROC) analysis was applied to determine the efficiency of survival prediction. Furthermore, the biological function of the predicted miRNAs was evaluated using a bioinformatics approach. Four (hsa-mir-126, hsa-mir-3613, hsa-mir-424, and hsa-mir-4772) out of 17 differentially expressed miRNAs were associated to the OS of patients with PAAD. Moreover, the area under the curve (AUC) of the constructed 4-miRNA signature associated to patients' with PAAD 2-year survival was 0.789. The multivariate Cox's proportional hazards regression model suggested that this 4-miRNA signature was an independent prognostic factor of other clinical parameters in patients with PAAD. Further pathway enrichment analyses revealed that the miRNAs in the 4-miRNA signature might regulate genes that affect focal adhesion, Wnt signaling pathway, and PI3K-Akt signaling pathway. Thus, these findings indicated that the 4-miRNA signature might be an effective independent prognostic biomarker in the prediction of PAAD patients' survival.     

Characterization and prognostic significance of alternative splicing events in lower‐grade diffuse gliomas

Alternative splicing (AS) is assumed to play important roles in the progression and prognosis of cancer. Currently, the comprehensive analysis and clinical relevance of AS in lower‐grade diffuse gliomas have not been systematically addressed. Here, we gathered alternative splicing data of lower‐grade diffuse gliomas from SpliceSeq. Based on the Percent Spliced In (PSI) values of 515 lower‐grade diffuse glioma patients from the Cancer Genome Atlas (TCGA), we performed subtype‐differential AS analysis and consensus clustering to determine robust clusters of patients. A total of 48 050 AS events in 10 787 genes in lower‐grade diffuse gliomas were profiled. Subtype‐differential splicing analysis and functional annotation revealed that spliced genes were significantly enriched in numerous cancer‐related biological phenotypes and signalling pathways. Consensus clustering using AS events identified three robust clusters of patients with distinguished pathological and prognostic features. Moreover, each cluster was also associated with distinct genomic alterations. Finally, we developed and validated an AS‐related signature with Cox proportional hazards model. The signature, significantly associated with clinical and molecular features, could serve as an independent prognostic factor for lower‐grade diffuse gliomas. Thus, our results indicated that AS events could discriminate molecular subtypes and have prognostic impact in lower‐grade diffuse gliomas.     

Osteopontin in metastatic lesions as a prognostic marker in ovarian cancers

Summary Osteopontin (OPN) is expressed in various human cancers and associated with tumor progression, invasion and metastasis in many manners. The purpose of this study is to investigate the clinical significance of OPN expression in metastatic lesions of ovarian cancers, since the prognosis of the patients with peritoneal dissemination is extremely poor. In primary tumors and peritoneal metastatic lesions from 40 patients with stage III ovarian cancers, the protein levels of OPN and histoscores were determined by enzyme immunoassay and immunohistochemistry, respectively. Immunohistochemical staining revealed OPN was distributed in the cytoplasm and nuclear compartments of the cancer and stromal cells within and around the tumor. The OPN level was significantly (p < 0.05) increased in 32 of 40 metastatic lesions of ovarian cancers. The OPN increased cases identified by immunohistochemical staining were consistent with those identified by the sandwich immunoassay. The prognosis of the 32 patients with significant increase of OPN in ovarian cancers was extremely poor, whereas the 36-month survival rate of the 8 patients with no increase of OPN was 75%. Multivariate analysis revealed that the levels of OPN were independent predictors of prognosis from clinical characteristics (age, lesion size, histological types). OPN might be associated with peritoneal metastasis and its advancement, and that the OPN level in metastatic lesion may be a prognostic indicator in ovarian cancers.     

The promising novel biomarkers and candidate small molecule drugs in lower-grade glioma: Evidence from bioinformatics analysis of high-throughput data

Overall survival of patients with low-grade glioma (LGG) has shown no significant improvement over the past 30 years, with survival averaging approximately 7 years. This study aimed to identify novel promising biomarkers of LGG and reveal its potential molecular mechanisms by integrated bioinformatics analysis. The microarray datasets of GSE68848 and GSE4290 were selected from GEO database for integrated analysis. In total, 293 overlapping differentially expressed genes (DEGs) were detected using the limma package. One hundred and eighty-eight nodes with 603 interactions were obtained from the establishment of protein-protein interaction (PPI) network. Functional and signaling pathway enriched were significantly correlated with the synapse and calcium signaling pathway, respectively. Module analysis revealed eight hub genes with high connectivity, which included CHRM1, DLG2, GABRD, GRIN1, HTR2A, KCNJ3, KCNJ9, and NUSAP1, and they were markedly correlated with patients’ prognosis. The mining of the Gene Expression Profiling Interactive Analysis database and qPCR further confirmed the abnormal expression of these key genes with their prognostic value in LGG. We eventually predicted the 20 most vital small molecule drugs, which potentially reverse the carcinogenic state of LGG, as per the CMap (connectivity map) database and these DEGs, and MS-275 (enrichment score = −0.939) was considered as the most promising small molecule to treat LGG. In conclusion, our study provided eight reliable novel molecular biomarkers for diagnosis, prognosis prediction, and treatment targets for LGG. These conclusions will contribute to a better comprehension of molecular mechanisms fundamental to LGG occurrence and progression, and providing new insights for future development of genomic individualized treatment in LGG.     

Nuclear NHERF1 expression as a prognostic marker in breast cancer

Our purpose was to investigate whether Na⁺/H⁺ exchanger regulatory factor 1 (NHERF1) expression could be linked to prognosis in invasive breast carcinomas. NHERF1, an ezrin-radixin-moesin (ERM) binding phosphoprotein 50, is involved in the linkage of integral membrane proteins to the cytoskeleton. It is therefore believed to have an important role in cell signaling associated with changes in cell cytoarchitecture. NHERF1 expression is observed in various types of cancer and is related to tumor aggressiveness. To date the most extensive analyses of the influence of NHERF1 in cancer development have been performed on breast cancer. However, the underlying mechanism and its prognostic significance are still undefined. NHERF1 expression was studied by immunohistochemistry (IHC) in a cohort of 222 breast carcinoma patients. Association of cytoplasmic and nuclear NHERF1 expression with survival was analyzed. Disease-free survival (DFS) and overall survival (OS) were determined based on the Kaplan–Meier method. Cytoplasmic NHERF1 expression was associated with negative progesterone receptor (PgR) (P=0.017) and positive HER2 expression (P=0.023). NHERF1 also showed a nuclear localization and this correlated with small tumor size (P=0.026) and positive estrogen receptor (ER) expression (P=0.010). Multivariate analysis identified large tumor size (P=0.011) and nuclear NHERF1 expression (P=0.049) to be independent prognostic variables for DFS. Moreover, the nuclear NHERF1(−)/ER(−) immunophenotype (27%) was statistically associated with large tumor size (P=0.0276), high histological grade (P=0.0411), PgR-negative tumors (P<0.0001) and high proliferative activity (P=0.0027). These patients had worse DFS compared with patients with nuclear NHERF1(+)/ER(+) tumors (75.4% versus 92.6% P=0.010). These results show that the loss of nuclear NHERF1 expression is associated with reduced survival, and the link between nuclear NHERF1 and ER expression may serve as a prognostic marker for the routine clinical management of breast cancer patients.     

Development and validation of a 10‐gene prognostic signature for acute myeloid leukaemia

Acute myeloid leukaemia (AML) is the most common type of adult acute leukaemia and has a poor prognosis. Thus, optimal risk stratification is of greatest importance for reasonable choice of treatment and prognostic evaluation. For our study, a total of 1707 samples of AML patients from three public databases were divided into meta‐training, meta‐testing and validation sets. The meta‐training set was used to build risk prediction model, and the other four data sets were employed for validation. By log‐rank test and univariate COX regression analysis as well as LASSO‐COX, AML patients were divided into high‐risk and low‐risk groups based on AML risk score (AMLRS) which was constituted by 10 survival‐related genes. In meta‐training, meta‐testing and validation sets, the patient in the low‐risk group all had a significantly longer OS (overall survival) than those in the high‐risk group (P < .001), and the area under ROC curve (AUC) by time‐dependent ROC was 0.5854‐0.7905 for 1 year, 0.6652‐0.8066 for 3 years and 0.6622‐0.8034 for 5 years. Multivariate COX regression analysis indicated that AMLRS was an independent prognostic factor in four data sets. Nomogram combining the AMLRS and two clinical parameters performed well in predicting 1‐year, 3‐year and 5‐year OS. Finally, we created a web‐based prognostic model to predict the prognosis of AML patients ( https://tcgi.shinyapps.io/amlrs_nomogram/ ).     

Serum interleukin-17 as a diagnostic and prognostic marker for non-small cell lung cancer

Abstract

Objective: The aim of this study was to explore the clinical role of serum interleukin (IL)-17 in patients with non-small-cell lung cancer (NSCLC).

Materials and method: Serum specimens from 128 patients with NSCLC and 60 healthy controls were collected. The concentrations of IL-17 were measured using enzyme-linked immunosorbent assay.

Results: Serum IL-17 levels were higher in the NSCLC group in comparison with the control group (p?<?0.01). With a cut-off value of 16?pg/ml, IL-17 showed a good diagnostic performance for NSCLC. Multivariate survival analysis indicated that IL-17 was an independent prognostic factor in NSCLC.

Conclusion: Measurement of IL-17 might be a useful diagnostic and prognostic value for patients with NSCLC.     

A novel DNA damage and repair‐related gene signature to improve predictive capacity of overall survival for patients with gliomas

Gliomas, as the most lethal and malignant brain tumours in adults, remain a major challenge worldwide. DNA damage and repair‐related genes (DDRRGs) appear to play a significant role in gliomas, but the studies of DDRRGs are still insufficient. Herein, we systematically explored and analysed 1547 DDRRGs in 938 glioma samples from TCGA and CGGA datasets. Using least absolute shrinkage and selection operator (LASSO) Cox regression analysis, we identified a 16‐DDRRG signature, characterized by high‐risk and low‐risk patterns. This risk model harbours robust predictive capability for overall survival of glioma patients. We found the high‐risk score is strongly associated with well‐known malignant features of gliomas, such as the mesenchymal subtype, IDH‐wildtype, 1p/19q non‐codeletion and MGMT promoter unmethylated status. In addition, we found that the high‐risk score is also linked with multiple oncogenic pathways and therapeutic resistance. Significantly, we found the high‐risk group has higher enrichment of immunosuppressive cells (M2‐type macrophages, Tregs and MDSCs) and immune inhibition biomarkers (PD‐1, PD‐L1 and CTLA‐4). Lastly, we proved that SMC4, which has the highest positive regression coefficient in our risk model, is strongly linked with malignant progression and TMZ resistance of gliomas in a E2F1‐dependent manner.     

Serum CYFRA21-1 as a prognostic marker for patients with undifferentiated nasopharyngeal carcinoma

The purpose of this study was to evaluate the potential of serum CYFRA21-1 and carcinoembryonic antigen (CEA) as prognostic markers in patients with undifferentiated nasopharyngeal carcinoma. Sixty-one patients who received definitive radiotherapy/chemoradiotherapy were analysed retrospectively. We investigated the association of the follow-up results with pretreatment level, post-treatment level and change of serum CYFRA21-1 and CEA, respectively. Patients with low pretreatment CYFRA21-1 had a significantly better overall survival. There were no significant associations among the remaining serum markers, and the survival and recurrence rates on multivariate analysis. The present study shows that pretreatment CYFRA21-1 level is a potential factor for predicting long-term survival.