A metabolism-relevant signature as a predictor for prognosis and therapeutic response in pancreatic cancer

Although several altered metabolic genes have been identified to be involved in the tumorigenesis and advance of pancreatic cancer (PC), their prognostic values remained unclear. The purpose of this study was to explore new targets and establish a metabolic signature to predict prognosis and chemotherapy response for optimal individualized treatment. The expression data of PC patients from two independent cohorts and metabolism-related genes from KEGG were utilized and analyzed for the establishment of the signature via lasso regression. Then, the differentially expressed candidate genes were further confirmed via online data mining platform and qRT-PCR of clinical specimens. Then, the analyses of gene set enrichment, mutation, and chemotherapeutic response were performed via R package. As results showed, 109 differentially expressed metabolic genes were screened out in PC. Then a metabolism-related five-gene signature comprising B3GNT3, BCAT1, KYNU, LDHA, and TYMS was constructed and showed excellent ability for predicting survival. A novel nomogram coordinating the metabolic signature and other independent prognostic parameters was developed and showed better predictive power in predicting survival. In addition, this metabolic signature was significantly involved in the activation of multiple oncological pathways and regulation of the tumor immune microenvironment. The patients with high risk scores had higher tumor mutation burdens and were prone to be more sensitive to chemotherapy. In summary, our work identified a new metabolic signature and established a superior prognostic nomogram which may supply more indications to explore novel strategies for diagnosis and treatment.     

Combining bioinformatics techniques to explore the molecular mechanisms involved in pancreatic cancer metastasis and prognosis

This article aims to explore the underlying molecular mechanisms and prognosis‐related genes in pancreatic cancer metastasis. Pancreatic cancer metastasis‐related gene chip data were downloaded from GENE EXPRESSION OMNIBUS(GEO)database. Differentially expressed genes were screened after R‐package pre‐treatment. Functional annotations and related signalling pathways were analysed using DAVID software. GEPIA (Gene Expression Profiling Interactive Analysis) was used to perform prognostic analysis, and differential genes associated with prognosis were screened and validated using data from GEO. We screened 40 healthy patients, 40 primary pancreatic cancer and 40 metastatic pancreatic cancer patients, collected serum, designed primers and used qPCR to test the expression of prognosis‐related genes in each group. 109 differentially expressed genes related with pancreatic cancer metastasis were screened, of which 49 were up‐regulated and 60 were down‐regulated. Functional annotation and pathway analysis revealed differentially expressed genes were mainly concentrated in protein activation cascade, extracellular matrix construction, decomposition, etc In the biological process, it is mainly involved in signalling pathways such as PPAR, PI3K‐Akt and ECM receptor interaction. Prognostic analysis showed the expression levels of four genes were significantly correlated with the overall survival time of patients with pancreatic cancer, namely SCG5, CRYBA2, CPE and CHGB. qPCR experiments showed the expression of these four genes was decreased in both the primary pancreatic cancer group and the metastatic pancreatic cancer group, and the latter was more significantly reduced. Pancreatic cancer metastasis is closely related to the activation of PPAR pathway, PI3K‐Akt pathway and ECM receptor interaction. SCG5, CRYBA2, CPE and CHGB genes are associated with the prognosis of pancreatic cancer, and their low expression suggests a poor prognosis.     

A robust immune-related lncRNA signature for the prognosis of human colorectal cancer

Background: Colorectal cancer (CRC) is one of the most prevalent malignant cancers worldwide. Immune-related long non-coding RNAs (IRlncRNAs) are proved to be essential in the development and progression of carcinoma. The purpose of the present study was to develop and validate a prognostic IRlncRNA signature for CRC patients.Methods: Gene expression profiles of CRC samples were downloaded from The Cancer Genome Atlas (TCGA) database. Immune-related genes were obtained from the ImmPort database and were used to identify IRlncRNA by correlation analysis. Through LASSO Cox regression analyses, a prognostic signature was constructed. Functional enrichment analysis was performed by gene set enrichment analysis (GSEA). TIMER2.0 web server and tumor immune dysfunction and exclusion (TIDE) algorithm were employed to analyze the association between our model and tumor-infiltrating immune cells and immunotherapy response. The expression levels of IRlncRNAs in cell lines were detected by quantitative real-time PCR (qPCR).Results: A 9-IRlncRNA signature was developed by a LASSO Cox proportional regression model. Based on the signature, CRC patients were divided into high- and low-risk groups with different prognoses. GSEA results indicated that patients in high-risk group were associated with cancer-related pathways. In addition, patients in low-risk group were found to have more infiltration of anti-tumor immune cells and might show a favorable response to immunotherapy. Finally, the result of qPCR revealed that most IRlncRNAs were differently expressed between normal and tumor cell lines.Conclusion: The constructed 9-IRlncRNA signature has potential to predict the prognosis of CRC patients and may be helpful to guide personalized immunotherapy.     

An eight-long noncoding RNA expression signature for colorectal cancer patients’ prognosis

Long noncoding RNAs (lncRNAs) have recently emerged as important biomarkers of cancer progression. Here, we proposed to develop a lncRNA-based signature with a prognostic value for colorectal cancer (CRC) overall survival (OS). Through mining microarray datasets, we analyzed the lncRNA expression profiles of 122 patients with CRC from Gene Expression Omnibus. Associations between lncRNA and CRC OS were firstly evaluated through univariate Cox regression analysis. A random survival forest method was applied for further screening of the lncRNA signature, which resulted in eight lncRNAs, including PEG3-AS1, LOC100505715, MINCR, DBH-AS1, LINC00664, FAM224A, LOC642852, and LINC00662. Combination of the eight lncRNAs weighted by their multivariate Cox regression coefficients formed a prognostic signature, through which, we could divide the 122 patients with CRC into two subgroups with significantly different OS. Good robustness of the lncRNA signature's prognostic value was verified through an independent data set consisting of 55 patients with CRC. In addition, gene set enrichment analysis indicated the potential association between high prognostic value and oxygen metabolism-related processes. This result should indicate that lncRNAs could be a useful signature for CRC prognosis.     

A DCS-related lncRNA signature predicts the prognosis and chemotherapeutic response of patients with gastric cancer

The combination of docetaxel, cisplatin, and S-1 (DCS) is a common chemotherapy regimen for patients with gastric cancer (GC). However, studies on long noncoding RNAs (lncRNAs) associated with the chemotherapeutic response to and prognosis after DCS remain lacking. The aim of the present study was to identify DCS mRNAs-lncRNAs associated with chemotherapy response and prognosis in GC patients. In the present study, we identified 548 lncRNAs associated with these 16 mRNAs in the TCGA and {"type":"entrez-geo","attrs":{"text":"GSE31811","term_id":"31811"}}GSE31811 datasets. Eleven lncRNAs were used to construct a prognostic signature by least absolute shrinkage and selection operator (LASSO) regression. A model including the 11 lncRNAs (LINC02532, {"type":"entrez-nucleotide","attrs":{"text":"AC007277.1","term_id":"4580503","term_text":"AC007277.1"}}AC007277.1, AC005324.4, {"type":"entrez-nucleotide","attrs":{"text":"AL512506.1","term_id":"12044702","term_text":"AL512506.1"}}AL512506.1, {"type":"entrez-nucleotide","attrs":{"text":"AC068790.7","term_id":"8571647","term_text":"AC068790.7"}}AC068790.7, {"type":"entrez-nucleotide","attrs":{"text":"AC022509.2","term_id":"6938434","term_text":"AC022509.2"}}AC022509.2, {"type":"entrez-nucleotide","attrs":{"text":"AC113139.1","term_id":"18875234","term_text":"AC113139.1"}}AC113139.1, LINC00106, {"type":"entrez-nucleotide","attrs":{"text":"AC005165.1","term_id":"3242748","term_text":"AC005165.1"}}AC005165.1, MIR100HG, and UBE2R2-AS1) associated with the prognosis of GC was constructed. The signature was validated in the TCGA database, model comparison, and qRT-PCR experiments. The results showed that the risk signature was a more effective prognostic factor for GC patients. Furthermore, the results showed that this model can well predicting chemotherapy drug response and immune infiltration of GC patients. In addition, our experimental results indicated that lower expression levels of LINC00106 and UBE2R2-AS1 predicted worse drug resistance in AGS/DDP cells. The experimental results agreed with the predictions. Furthermore, knockdown of LINC00106 or UBE2R2-AS1 can significantly enhanced the proliferation and migration of GC AGS cells in vitro. In conclusion, a novel DCS therapy-related lncRNA signature may become a new strategy to predict chemotherapy response and prognosis in GC patients. LINC00106 and UBE2R2-AS1 may exhibit a tumor suppressive function in GC.     

Multi-class cancer classification through gene expression profiles:microRNA versus mRNA

Both microRNA （miRNA） and mRNA expression profiles are important methods for cancer type classification. A comparative study of their classification performance will be helpful in choosing the means of classification. Here we evaluated the classification performance of miRNA and mRNA profiles using a new data mining approach based on a novel SVM （Support Vector Machines） based recursive fea- ture elimination （nRFE） algorithm. Computational experiments showed that information encoded in miRNAs is not sufficient to classify cancers; gut-derived samples cluster more accurately when using mRNA expression profiles compared with using miRNA profiles; and poorly differentiated tumors （PDT） could be classified by mRNA expression profiles at the accuracy of 100% versus 93.8% when using miRNA profiles. Furthermore, we showed that mRNA expression profiles have higher capacity in normal tissue classifications than miRNA. We concluded that classification performance using mRNA profiles is superior to that of miRNA profiles in multiple-class cancer classifications.     

Mucins in pancreatic cancer: A well‐established but promising family for diagnosis,prognosis and therapy

Mucins are a family of multifunctional glycoproteins that mostly line the surface of epithelial cells in the gastrointestinal tract and exert pivotal roles in gut lubrication and protection. Pancreatic cancer is a lethal disease with poor early diagnosis, limited therapeutic effects, and high numbers of cancer‐related deaths. In this review, we introduce the expression profiles of mucins in the normal pancreas, pancreatic precursor neoplasia and pancreatic cancer. Mucins in the pancreas contribute to biological processes such as the protection, lubrication and moisturization of epithelial tissues. They also participate in the carcinogenesis of pancreatic cancer and are used as diagnostic biomarkers and therapeutic targets. Herein, we discuss the important roles of mucins that lead to the lethality of pancreatic adenocarcinoma, particularly MUC1, MUC4, MUC5AC and MUC16 in disease progression, and present a comprehensive analysis of the clinical application of mucins and their promising roles in cancer treatment to gain a better understanding of the role of mucins in pancreatic cancer.     

Identification of a 4‐mRNA metastasis‐related prognostic signature for patients with breast cancer

Metastasis‐related mRNAs have showed great promise as prognostic biomarkers in various types of cancers. Therefore, we attempted to develop a metastasis‐associated gene signature to enhance prognostic prediction of breast cancer (BC) based on gene expression profiling. We firstly screened and identified 56 differentially expressed mRNAs by analysing BC tumour tissues with and without metastasis in the discovery cohort (GSE102484, n = 683). We then found 26 of these differentially expressed genes were associated with metastasis‐free survival (MFS) in the training set (GSE20685, n = 319). A metastasis‐associated gene signature built using a LASSO Cox regression model, which consisted of four mRNAs, can classify patients into high‐ and low‐risk groups in the training cohort. Patients with high‐risk scores in the training cohort had shorter MFS (hazard ratio [HR] 3.89, 95% CI 2.53‐5.98; P < 0.001), disease‐free survival (DFS) (HR 4.69, 2.93‐7.50; P < 0.001) and overall survival (HR 4.06, 2.56‐6.45; P < 0.001) than patients with low‐risk scores. The prognostic accuracy of mRNAs signature was validated in the two independent validation cohorts (GSE21653, n = 248; GSE31448, n = 246). We then developed a nomogram based on the mRNAs signature and clinical‐related risk factors (T stage and N stage) that predicted an individual's risk of disease, which can be assessed by calibration curves. Our study demonstrated that this 4‐mRNA signature might be a reliable and useful prognostic tool for DFS evaluation and will facilitate tailored therapy for BC patients at different risk of disease.     

Prognostic and predictive value of a mRNA signature in peripheral T-cell lymphomas: A mRNA expression analysis

Current international prognostic index is widely questioned on the risk stratification of peripheral T-cell lymphoma and does not accurately predict the outcome for patients. We postulated that multiple mRNAs could combine into a model to improve risk stratification and helping clinicians make treatment decisions. In this study, the gene expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Weighted gene co-expression network analysis (WGCNA) was used to screening genes in selected module which most closely related to PTCLs, and then built a mRNA signature using a LASSO Cox regression model and validated the prognostic accuracy of it. Finally, a nomogram was constructed and the performance was assessed. A total of 799 WGCNA-selected mRNAs in black module were identified, and a mRNA signature which based on DOCK2, GSTM1, H2AFY, KCNAB2, LAPTM5 and SYK for PTCLs was developed. Significantly statistical difference can be seen in overall survival of PTCLs between low-risk group and high-risk group (training set:hazard ratio [HR] 4.3, 95% CI 2.4-7.4, P < .0001; internal testing set:hazard ratio [HR] 2.4, 95% CI 1.2-4.8, P < .01; external testing set:hazard ratio [HR] 2.3, 95% CI 1.10-4.7, P = .02). Furthermore, multivariate regression demonstrated that the signature was an independently prognostic factor. Moreover, the nomogram which combined the mRNA signature and multiple clinical factors suggesting that predicted survival probability agreed well with the actual survival probability. The signature is a reliable prognostic tool for patients with PTCLs, and it has the potential for clinicians to implement personalized therapeutic regimen for patients with PTCLs.     

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.     

The diagnosis and prognosis values of WNT mRNA expression in colon adenocarcinoma

WNT family genes have participated in the progression and development of many cancers, however, the association between colon adenocarcinoma (COAD) and WNTs have been rarely reported. This study investigated the significance of WNT genes expression in COAD from the standpoint of diagnosis and prognosis. The RNA-sequencing dataset of COAD was downloaded from The Cancer Genome Atlas and University of California, Santa Cruz Xena browser. The biology functions of WNT genes were investigated by biological analysis. Biological analysis of WNT family genes indicated that WNT genes were noticeably enriched in the complex process of WNT signaling pathway. The Pearson correlation analysis suggested WNT1 and WNT9B had a strong correlation. And receiver operating characteristic curves suggested that most of the genes could serve as a significant diagnostic makers in COAD (P < .05), especially WNT2 and WNT7B had high diagnostic values that the area under curve were 0.997 (95% confidence interval [0.994-1.000]) and 0.961 (95%CI [0.939-0.983]), respectively. And our multivariate survival analysis suggested the downregulated of WNT10B (P < .05) showed a favor prognosis in COAD overall survival. And the risk score model predicted that the upregulated expression of WNT10B might increase the risk of death. The very study we had conducted suggested that WNT genes had a certain connection with the diagnosis and prognosis of COAD. The messenger RNA expression of WNT2 and WNT7B might become potentially diagnostic biomarkers, and WNT10B might serve as an independent prognosis indicator for COAD.     

A novel risk signature that combines 10 long noncoding RNAs to predict neuroblastoma prognosis

Neuroblastoma (NBL) is the most frequently encountered extracranial solid neoplasm and impacts significantly on the survival of patients, especially in cases of advanced tumor stage or relapse. A long noncoding RNA (lncRNA) signature to predict the survival of patients with NBL is proposed in this paper. Differentially expressed lncRNA (DElncRNA) was selected using the Limma plus Voom package in R based on the RNA-sequencing data downloaded from the Therapeutically Applicable Research To Generate Effective Treatments database and Genotype-Tissue Expression database. Univariate cox regression analysis, least absolute shrinkage and selection operator regression analysis, and multivariate cox regression analysis were conducted to identify candidate DElncRNAs for the risk signature. Consequently, 10 DElncRNAs were designated as candidate DElncRNAs for the risk signature. Time-dependent receiver operating characteristic curves and Kapan–Meier survival curves confirmed the efficacy of the risk signature in predicting the survival of patients with NBL (area under the curve = 0.941; p ≤ .001). One of the DElncRNA constituent subparts (LINC01010) was significantly associated with the survival outcome of patients with NBL in GSE62564 (p = .004). Thus, a risk signature comprising 10 DElncRNAs was identified as effective for individual risk stratification and the survival prediction outcomes of patients with NBL.     

A novel antisense lncRNA NT5E promotes progression by modulating the expression of SYNCRIP and predicts a poor prognosis in pancreatic cancer

A novel antisense lncRNA NT5E was identified in a previous microarray that was clearly up‐regulated in pancreatic cancer (PC) tissues. However, its biological function remains unclear. Thus, we aimed to explore its function and clinical significance in PC. The lncNT5E expression was determined in PC specimens and cell lines. In vitro and in vivo studies detected the impact of lncNT5E depletion on PC cell proliferation, migration and invasion. Western blotting investigated the epithelial‐mesenchymal transition (EMT) markers. The interaction between lncNT5E and the promoter region of SYNCRIP was detected by dual‐luciferase reporter assay. The role of lncNT5E in modulating SYNCRIP was investigated in vitro. Our results showed that lncNT5E was significantly up‐regulated in PC tissues and cell lines and associated with poor prognosis. LncNT5E depletion inhibited PC cell proliferation, migration, invasion and EMT in vitro and caused tumorigenesis arrest in vivo. Furthermore, SYNCRIP knockdown had effects similar to those of lncNT5E depletion. A significant positive relationship was observed between lncNT5E and SYNCRIP. Moreover, the dual‐luciferase reporter assays indicated that lncNT5E depletion significantly inhibited SYNCRIP promoter activity. Importantly, the malignant phenotypes of lncNT5E depletion were rescued by overexpressing SYNCRIP. In conclusion, lncNT5E predicts poor prognosis and promotes PC progression by modulating SYNCRIP expression.     

Development and validation of a novel epigenetic signature for predicting prognosis in colon cancer

Epigenetic factors play a critical role in carcinogenesis by imparting a distinct feature to the chromatin architecture. The present study aimed to develop a novel epigenetic signature for evaluating the relapse-free survival of colon cancer patients. Public microarray datasets were acquired from the Gene Expression Omnibus databases: GSE39582, GSE17538, GSE33113, and GSE37892 set. Patients from GSE39582 set were randomized 1:1 into training and internal validation series. Patients were divided into high-risk and low-risk groups in training series based on a set of 11 epigenetic factors (p < .001). The good reproducibility for the prognostic value of the epigenetic signature was confirmed in the internal validation series (p < .001), external validation series (a combination of GSE17538 set, GSE33113 set, and GSE37892 set; p = .018), and entire series (p < .001). Furthermore, a nomogram, which integrated the epigenetic signature, pathological stage, and postoperative chemotherapy, was developed based on the GSE39582 set. The time-dependent receiver operating characteristic curve at 1 year demonstrated that the comprehensive signature presented superior prognostic value than the pathological stage. In conclusion, an epigenetic signature, which could be utilized to divide colon cancer patients into two groups with significantly different risk of relapse, was established. This biomarker would aid in identifying patients who require an intensive follow-up and aggressive therapeutic intervention.     

Five genes as a novel signature for predicting the prognosis of patients with laryngeal cancer

In this study, we purpose to investigate a novel five-gene signature for predicting the prognosis of patients with laryngeal cancer. The laryngeal cancer datasets were obtained from The Cancer Genome Atlas (TCGA). Both univariate and multivariate Cox regression analysis was applied to screening for prognostic differential expressed genes (DEGs), and a novel gene signature was obtained. The performance of this Cox regression model was tested by receiver operating characteristic (ROC) curves and area under the curve (AUC). Further survival analysis for each of the five genes was carried out through the Kaplan-Meier curve and Log-rank test. Totally, 622 DEGs were screened from the TCGA datasets in this study. We construct a five-gene signature through Cox survival analysis. Patients were divided into low- and high-risk groups depending on the median risk score, and a significant difference of the 5-year overall survival was found between these two groups (P < .05). ROC curves verified that this five-gene signature had good performance to predict the prognosis of laryngeal cancer (AUC = 0.862, P < .05). In conclusion, the five-gene signature consist of EMP1, HOXB9, DPY19L2P1, MMP1, and KLHDC7B might be applied as an independent prognosis predictor of laryngeal cancer.     

A 4-microRNA signature for survival prognosis in pediatric and adolescent acute myeloid leukemia

Acute myeloid leukemia (AML) is a hematologic malignancy with significant molecular heterogeneity. MicroRNAs (miRNAs) play a critical role in AML diagnosis, pathogenesis, and prognosis of AML. Little has been done to identify a miRNA signature in pediatric and adolescent patients for predicting overall survival. This study aims to identify a panel of miRNA signature that could predict the prognosis of all younger AML patients with all subtypes of AML by analyzing data from The Cancer Genome Atlas (TCGA). A total of 229 patients under 23 years with miRNA data and corresponding clinical data from TCGA database were enrolled in this study. Through conducting multivariate analysis in the training test, it was identified that the high expression of hsa-miR-509 and hsa-miR-542 were independent poor prognostic factors, whereas that of hsa-miR-146a and hsa-miR-3667 had a trend to be favorable factors. A 4-miRNA signature was constructed by these miRNAs considering the weight of each. In testing group and all 229 patients’ cohort as well as 59 cytogenetically normal AML (CN-AML) patients’ cohort, higher risk score was associated with shorter overall survival (OS). All results were confidential by using powerful statistical analysis. Gene ontology and Kyoto Encyclopedia of Genes and Genomes analysis were carried out to further develop leukemia-relevant mechanisms supporting the model. The results indicate that the 4-miRNA-based signature is a reliable prognostic biomarker for pediatric and adolescent AML patients.     

An EMT‐related gene signature for the prognosis of human bladder cancer

The transition from non–muscle‐invasive bladder cancer (NMIBC) to muscle‐invasive bladder cancer (MIBC) is detrimental to bladder cancer (BLCA) patients. Here, we aimed to study the underlying mechanism of the subtype transition. Gene set variation analysis (GSVA) revealed the epithelial‐mesenchymal transition (EMT) signalling pathway with the most positive correlation in this transition. Then, we built a LASSO Cox regression model of an EMT‐related gene signature in BLCA. The patients with high risk scores had significantly worse overall survival (OS) and disease‐free survival (DFS) than those with low risk scores. The EMT‐related gene signature also performed favourably in the accuracy of prognosis and in the subtype survival analysis. Univariate and multivariate Cox regression analyses demonstrated that the EMT‐related gene signature, pathological N stage and age were independent prognostic factors for predicting survival in BLCA patients. Furthermore, the predictive nomogram model was able to effectively predict the outcome of BLCA patients by appropriately stratifying the risk score. In conclusion, we developed a novel EMT‐related gene signature that has tumour‐promoting effects, acts as a negative independent prognostic factor and might facilitate personalized counselling and treatment in BLCA.