An expression signature model to predict lung adenocarcinoma-specific survival

Identification of a prognosis-related gene signature and ceRNA regulatory networks in lung adenocarcinoma

The ceRNA network, consisting of both noncoding RNA and protein-coding RNA, governs the occurrence, progression, metastasis, and infiltration of lung adenocarcinoma. Signatures comprising multiple genes can effectively determine survival stratification and prognosis of patients with lung adenocarcinoma. To explore the mechanisms of lung adenocarcinoma progression and identify potential biological targets, we carried out systematic bioinformatics analyses of the genetic profiles of lung adenocarcinoma, such as weighted gene co-expression network analysis (WGCNA), differential expression (DE) assessment, univariate and multivariate Cox proportional hazard regression models, ceRNA modulatory networks generated using the ENCORI and miRcode databases, nomogram models, ROC curve assessment, and Kaplan-Meier survival curve analysis. The ceRNA network encompassed 37 nodes, comprising 12 mRNAs, 22 lncRNAs, and three miRNAs. Simultaneously, we performed integration analysis using the 12 genes from the ceRNA network. Our findings revealed that the signature established by these 12 genes serves as an adverse element in lung adenocarcinoma, contributing to unfavorable patient prognosis. To ensure the credibility of our results, we used in vitro experiments for further verification. In conclusion, our study delved into the potential mechanisms underlying lung adenocarcinoma via the ceRNA regulatory network, specifically focusing on the PIF1 and has-miR-125a-5p axis. Additionally, a signature comprising 12 genes was identified as a biomarker related to the prognosis of lung adenocarcinoma.

An epigenome-wide analysis of sex hormone levels and DNA methylation in male blood samples

Endogenous sex hormones and DNA methylation both play important roles in various diseases. However, their interplay is largely unknown. A deeper understanding of their interrelationships could provide new insights into the pathology of disease development. We, therefore, investigated associations between circulating sex hormones, sex hormone binding globulin (SHBG), and DNA methylation in blood, using samples from 77 men (65 with repeated samples), from the population-based Northern Sweden Health and Disease Study (NSHDS). DNA methylation was measured in buffy coat using the Infinium Methylation EPIC BeadChip (Illumina). Sex hormone (oestradiol, oestrone, testosterone, androstenedione, dehydroepiandrosterone, and progesterone) and SHBG concentrations were measured in plasma using a high-performance liquid chromatography tandem mass spectrometry (LC/MS-MS) method and an enzyme-linked immunoassay, respectively. Associations between sex hormones, SHBG, and DNA methylation were estimated using both linear regression and mixed-effects models. Additionally, we used the comb-p method to identify differentially methylated regions based on nearby P values. We identified one novel CpG site (cg14319657), at which DNA methylation was associated with dehydroepiandrosterone, surpassing a genome-wide significance level. In addition, more than 40 differentially methylated regions were associated with levels of sex hormones and SHBG and several of these mapped to genes involved in hormone-related diseases. Our findings support a relationship between circulating sex hormones and DNA methylation and suggest that further investigation is warranted, both for validation, further exploration and to gain a deeper understanding of the mechanisms and potential consequences for health and disease.

Identification and Validation of 7-lncRNA Signature of Epigenetic Disorders by Comprehensive Epigenetic Analysis

The survival rate of patients with lung adenocarcinoma (LUAD) is low. This study analyzed the correlation between the expression of long noncoding RNA (lncRNA) and epigenetic alterations along with the investigation of the prognostic value of these outcomes for LUAD. Differentially expressed lncRNAs were identified based on multiomic data and positively related genes using DESeq2 in R, differentially histone-modifying genes specific to LUAD based on histone modification data, gene enhancers from information collected from the FANTOM5 (Function Annotation Of The Mammalian Genome-5) (fantom.gsc.riken.jp/5) human enhancer database, gene promoters using the ChIPseeker and the human lincRNAs Transcripts database in R, and differentially methylated regions (DMRs) using Bumphunter in R. Overall survival was estimated by Kaplan-Meier, comparisons were performed among groups using log-rank tests to derive differences between sample subclasses, and epigenetic lncRNAs (epi-lncRNAs) potentially relevant to LUAD prognosis were identified. A total of seven dysregulated epi-lncRNAs in LUAD were identified by comparing histone modifications and alterations in histone methylation regions on lncRNA promoter and enhancer elements, including H3K4me2, H3K27me3, H3K4me1, H3K9me3, H4K20me1, H3K9ac, H3K79me2, H3K27ac, H3K4me3, and H3K36me3. Furthermore, 69 LUAD-specific dysregulated epi-lncRNAs were identified. Moreover, lncRNAs-based prognostic analysis of LUAD samples was performed and explored that seven of these lncRNAs, including A2M-AS1, AL161431.1, DDX11-AS1, FAM83A-AS1, MHENCR, MNX1-AS1, and NKILA (7-EpiLncRNA), showed the potential to serve as markers for LUAD prognosis. Additionally, patients having a high 7-EpiLncRNA score showed a generally more unfavorable prognosis compared with those which scored lower. Seven lncRNAs were identified as markers of prognosis in patients with LUAD. The outcomes of this research will help us understand epigenetically aberrant regulation of lncRNA expression in LUAD in a better way and have implications for research advances in the regulatory role of lncRNAs in LUAD.

Identification and Validation of a Proliferation-Associated Score Model Predicting Survival in Lung Adenocarcinomas

AIM

This study is aimed at building a risk model based on the genes that significantly altered the proliferation of lung adenocarcinoma cells and exploring the underlying mechanisms.

METHODS

The data of 60 lung adenocarcinoma cell lines in the Cancer Dependency Map (Depmap) were used to identify the genes whose knockout led to dramatical acceleration or deacceleration of cell proliferation. Then, univariate Cox regression was performed using the survival data of 497 patients with lung adenocarcinoma in The Cancer Genome Atlas (TCGA). The least absolute shrinkage and selection operator (LASSO) model was used to construct a risk prediction score model. Patients with lung adenocarcinoma from TCGA were classified into high- or low-risk groups based on the scores. The differences in clinicopathologic, genomic, and immune characteristics between the two groups were analyzed. The prognosis of the genes in the model was verified with immunohistochemical staining in 100 samples from the Department of Thoracic Surgery, Zhongshan Hospital, and the alteration in the proliferation rate was checked after these genes were knocked down in lung adenocarcinoma cells (A549 and H358).

RESULTS

A total of 55 genes were found to be significantly related to survival by combined methods, which were crucial to tumor progression in functional enrichment analysis. A six-gene-based risk prediction score, including the proteasome subunit beta type-6 (PSMB6), the heat shock protein family A member 9 (HSPA9), the deoxyuridine triphosphatase (DUT), the cyclin-dependent kinase 7 (CDK7), the polo-like kinases 1 (PLK1), and the folate receptor beta 2 (FOLR2), was built using the LASSO method. The high-risk group classified with the score model was characterized by poor overall survival (OS), immune infiltration, and relatively higher mutation load. A total of 9864 differentially expressed genes and 138 differentially expressed miRNAs were found between the two groups. Also, a nomogram comparing score model, age, and the stage was built to predict OS for patients with lung adenocarcinoma. Using immunohistochemistry, the expression levels of PSMB6, HSPA9, DUT, CDK7, and PLK1 were found to be higher in lung adenocarcinoma tissues of patients, while the expression of FOLR2 was low, which was consistent with survival prediction. The knockdown of PSMB6 and HSPA9 by siRNA significantly downregulated the proliferation of A549 and H358 cells.

CONCLUSION

The proposed score model may function as a promising risk prediction tool for patients with lung adenocarcinoma and provide insights into the molecular regulation mechanism of lung adenocarcinoma.

Identification of crucial long non-coding RNAs and mRNAs along with related regulatory networks through microarray analysis in esophageal carcinoma

Esophageal carcinoma (EC) is a tremendous threat to human health and life worldwide. Long non-coding RNAs (lncRNAs) have been identified as crucial players in carcinomas including EC. An in-depth understanding on regulatory networks of lncRNAs contributes to the better management of EC. In this text, 2052 lncRNAs and 3240 mRNAs were found to be differentially expressed in 5 EC tumor tissues versus adjacent normal tissues by microarray analysis. Moreover, 297 carcinoma-related genes were screened out according to pathway and disease annotation analyses. In addition, 410 potential lncRNA-mRNA cis-regulation pairs and 395 lncRNA-mRNA trans-regulation pairs were screened out. Among these genes, 14 trans-regulated and 19 cis-regulated genes were found to be related with carcinomas. Additionally, 42 possible lncRNA-mRNA trans-regulation pairs and 26 cis-regulation pairs were found to be related with carcinomas. Also, 4 differentially expressed transcription factors in EC and lncRNAs possibly regulated by these transcription factors were screened out. Moreover, plenty of common upregulated or downregulated lncRNAs and mRNAs in EC were identified by comparative analysis for our microarray outcomes and previous high-throughput data. Furthermore, we demonstrated that ENST00000437781.1 knockdown inhibited cell proliferation and facilitated cell apoptosis by downregulating SIX homeobox 4 (SIX4) and ENST00000524987.1 knockdown had no influence on anoctamin 1 calcium activated chloride channel (ANO1) expression in EC cells. In conclusion, we identified some crucial lncRNAs and genes along with potential regulatory networks of lncRNAs/genes, deepening our understanding on pathogenesis of EC.

Long non-coding RNA RP11-284F21.9 functions as a ceRNA regulating PPWD1 by competitively binding to miR-769-3p in cervical carcinoma

Cervical carcinoma is the most common gynecological cancer in women worldwide. Emerging evidence has shown that long non-coding RNAs (lncRNAs) participate in multiple biological processes of cervical carcinoma tumorigenesis. We aimed to investigate the function of a novel lncRNA RP11-284F21.9 in cervical carcinoma. We found that RP11-284F21.9 was down-regulated in cervical carcinoma tissues and cell lines. Overexpression of RP11-284F21.9 inhibits proliferation, invasion and migration of cervical carcinoma cells in vitro. Further, we identified that RP11-284F21.9 directly interacted with miR-769-3p and functioned as the miR-769-3p sponge. Mechanistically, we showed that miR-769-3p regulated peptidylprolyl isomerase domain and WD repeat-containing protein1 (PPWD1) expression by targeting PPWD1 3′-UTR. Furthermore, xenograft tumor model revealed that overexpression of RP11-284F21.9 inhibited tumor growth of cervical carcinoma in vivo. Taken together, our results demonstrate that RP11-284F21.9 functions as tumor suppressor and regulates PPWD1 expression through competitively binding to miR-769-3p in cervical carcinoma, suggesting that RP11-284F21.9/miR-769-3p/PPWD1 axis could serve as a promising prognostic biomarker and therapeutic target for cervical carcinoma.

KLHDC7B-DT aggravates pancreatic ductal adenocarcinoma development via inducing cross-talk between cancer cells and macrophages

Tumor microenvironment (TME) exerts key roles in pancreatic ductal adenocarcinoma (PDAC) development. However, the factors regulating the cross-talk between PDAC cells and TME are largely unknown. In the present study, we identified a long noncoding RNA (lncRNA) KLHDC7B divergent transcript (KLHDC7B-DT), which was up-regulated in PDAC and correlated with poor survival of PDAC patients. Functional assays demonstrated that KLHDC7B-DT enhanced PDAC cell proliferation, migration, and invasion. Mechanistically, KLHDC7B-DT was found to directly bind IL-6 promoter, induce open chromatin structure at IL-6 promoter region, activate IL-6 transcription, and up-regulate IL-6 expression and secretion. The expression of KLHDC7B-DT was positively correlated with IL-6 in PDAC tissues. Via inducing IL-6 secretion, KLHDC7B-DT activated STAT3 signaling in PDAC cells in an autocrine manner. Furthermore, KLHDC7B-DT also activated STAT3 signaling in macrophages in a paracrine manner, which induced macrophage M2 polarization. KLHDC7B-DT overexpressed PDAC cells-primed macrophages promoted PDAC cell proliferation, migration, and invasion. Blocking IL-6/STAT3 signaling reversed the effects of KLHDC7B-DT on macrophage M2 polarization and PDAC cell proliferation, migration, and invasion. In conclusion, KLHDC7B-DT enhanced malignant behaviors of PDAC cells via IL-6-induced macrophage M2 polarization and IL-6-activated STAT3 signaling in PDAC cells. The cross-talk between PDAC cells and macrophages induced by KLHDC7B-DT represents potential therapeutic target for PDAC.

Identification of key genes associated with lung adenocarcinoma by bioinformatics analysis

Lung adenocarcinoma (LUAD) is the most common histological type of lung cancer, comprising around 40% of all lung cancer. Until now, the pathogenesis of LUAD has not been fully elucidated. In the current study, we comprehensively analyzed the dysregulated genes in lung adenocarcinoma by mining public datasets. Two sets of gene expression datasets were obtained from the Gene Expression Omnibus (GEO) database. The dysregulated genes were identified by using the GEO2R online tool, and analyzed by R packages, Cytoscape software, STRING, and GPEIA online tools. A total of 275 common dysregulated genes were identified in two independent datasets, including 54 common up-regulated and 221 common down-regulated genes in LUAD. Gene Ontology (GO) enrichment analysis showed that these dysregulated genes were significantly enriched in 258 biological processes (BPs), 27 cellular components (CCs), and 21 molecular functions (MFs). Furthermore, protein-protein interaction (PPI) network analysis showed that PECAM1, ENG, KLF4, CDH5, and VWF were key genes. Survival analysis indicated that the low expression of ENG was associated with poor overall survival (OS) of LUAD patients. The low expression of PECAM1 was associated with poor OS and recurrence-free survival of LUAD patients. The cox regression model developed based on age, tumor stage, ENG, PECAM1 could effectively predict 5-year survival of LUAD patients. This study revealed some key genes, BPs, CCs, and MFs involved in LUAD, which would provide new insights into understanding the pathogenesis of LUAD. In addition, ENG and PECAM1 might serve as promising prognostic markers in LUAD.

Identification and Validation of Two Lung Adenocarcinoma-Development Characteristic Gene Sets for Diagnosing Lung Adenocarcinoma and Predicting Prognosis

Background: Lung adenocarcinoma (LUAD) is one of the main types of lung cancer. Because of its low early diagnosis rate, poor late prognosis, and high mortality, it is of great significance to find biomarkers for diagnosis and prognosis. Methods: Five hundred and twelve LUADs from The Cancer Genome Atlas were used for differential expression analysis and short time-series expression miner (STEM) analysis to identify the LUAD-development characteristic genes. Survival analysis was used to identify the LUAD-unfavorable genes and LUAD-favorable genes. Gene set variation analysis (GSVA) was used to score individual samples against the two gene sets. Receiver operating characteristic (ROC) curve analysis and univariate and multivariate Cox regression analysis were used to explore the diagnostic and prognostic ability of the two GSVA score systems. Two independent data sets from Gene Expression Omnibus (GEO) were used for verifying the results. Functional enrichment analysis was used to explore the potential biological functions of LUAD-unfavorable genes. Results: With the development of LUAD, 185 differentially expressed genes (DEGs) were gradually upregulated, of which 84 genes were associated with LUAD survival and named as LUAD-unfavorable gene set. While 237 DEGs were gradually downregulated, of which 39 genes were associated with LUAD survival and named as LUAD-favorable gene set. ROC curve analysis and univariate/multivariate Cox proportional hazards analyses indicated both of LUAD-unfavorable GSVA score and LUAD-favorable GSVA score were a biomarker of LUAD. Moreover, both of these two GSVA score systems were an independent factor for LUAD prognosis. The LUAD-unfavorable genes were significantly involved in p53 signaling pathway, Oocyte meiosis, and Cell cycle. Conclusion: We identified and validated two LUAD-development characteristic gene sets that not only have diagnostic value but also prognostic value. It may provide new insight for further research on LUAD.

Long non-coding RNA FAM83H-AS1 as an emerging marker for diagnosis, prognosis and therapeutic targeting of cancer

Incidence and mortality rates of cancer continue to increase greatly despite the improved diagnostic and therapeutic methods. Based on GLOBOCAN estimates, the numbers of new cancer cases reported in 2018 were ~18.1 million, while the numbers of cancer mortalities were ~9.6 million. It remains difficult to diagnose most cancer patients at early stages. Although cancer therapy market is rapidly evolving, the effectiveness of therapy is still inadequate. Therefore, exploring new biomarkers for diagnosis, prognosis and treatment is essential for cancer management. Long non-coding RNAs (lncRNAs) are unique regulatory molecules that control several cellular processes and are implicated in diverse human diseases including cancer. LncRNAs could serve as potential biomarkers for cancer patients to aid diagnosis and determine prognosis. In addition, numerous lncRNAs have proved their ability to predict response to cancer treatment. FAM83H antisense RNA 1 (FAM83H-AS1) is among those highly dysregulated lncRNAs in cancer. FAM83H-AS1 was demonstrated to participate in the progression of different malignancies and also shown to play a vital role in diagnosis, prognosis and treatment. Here, we analyse recent studies concerning the oncogenic role and molecular mechanisms of lncRNA FAM83H-AS1 in the following cancer types: bladder, breast, lung, hepatocellular, colorectal, gastric, pancreatic, ovarian, cervical cancer as well as glioma.

Development and validation of a robust immune-related prognostic signature in early-stage lung adenocarcinoma

Background

The incidence of stage I and stage II lung adenocarcinoma (LUAD) is likely to increase with the introduction of annual screening programs for high-risk individuals. We aimed to identify a reliable prognostic signature with immune-related genes that can predict prognosis and help making individualized management for patients with early-stage LUAD.

Methods

The public LUAD cohorts were obtained from the large-scale databases including 4 microarray data sets from the Gene Expression Omnibus (GEO) and 1 RNA-seq data set from The Cancer Genome Atlas (TCGA) LUAD cohort. Only early-stage patients with clinical information were included. Cox proportional hazards regression model was performed to identify the candidate prognostic genes in GSE30219, GSE31210 and GSE50081 (training set). The prognostic signature was developed using the overlapped prognostic genes based on a risk score method. Kaplan–Meier curve with log-rank test and time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic value and performance of this signature, respectively. Furthermore, the robustness of this prognostic signature was further validated in TCGA-LUAD and GSE72094 cohorts.

Results

A prognostic immune signature consisting of 21 immune-related genes was constructed using the training set. The prognostic signature significantly stratified patients into high- and low-risk groups in terms of overall survival (OS) in training data set, including GSE30219 (HR = 4.31, 95% CI 2.29–8.11; P = 6.16E−06), GSE31210 (HR = 11.91, 95% CI 4.15–34.19; P = 4.10E−06), GSE50081 (HR = 3.63, 95% CI 1.90–6.95; P = 9.95E−05), the combined data set (HR = 3.15, 95% CI 1.98–5.02; P = 1.26E−06) and the validation data set, including TCGA-LUAD (HR = 2.16, 95% CI 1.49–3.13; P = 4.54E−05) and GSE72094 (HR = 2.95, 95% CI 1.86–4.70; P = 4.79E−06). Multivariate cox regression analysis demonstrated that the 21-gene signature could serve as an independent prognostic factor for OS after adjusting for other clinical factors. ROC curves revealed that the immune signature achieved good performance in predicting OS for early-stage LUAD. Several biological processes, including regulation of immune effector process, were enriched in the immune signature. Moreover, the combination of the signature with tumor stage showed more precise classification for prognosis prediction and treatment design.

Conclusions

Our study proposed a robust immune-related prognostic signature for estimating overall survival in early-stage LUAD, which may be contributed to make more accurate survival risk stratification and individualized clinical management for patients with early-stage LUAD.

Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinoma

Background

Lung cancer is the leading cause of the largest number of deaths worldwide and lung adenocarcinoma is the most common form of lung cancer. In order to understand the molecular basis of lung adenocarcinoma, integrative analysis have been performed by using genomics, transcriptomics, epigenomics, proteomics and clinical data. Besides, molecular prognostic signatures have been generated for lung adenocarcinoma by using gene expression levels in tumor samples. However, we need signatures including different types of molecular data, even cohort or patient-based biomarkers which are the candidates of molecular targeting.

Results

We built an R pipeline to carry out an integrated meta-analysis of the genomic alterations including single-nucleotide variations and the copy number variations, transcriptomics variations through RNA-seq and clinical data of patients with lung adenocarcinoma in The Cancer Genome Atlas project. We integrated significant genes including single-nucleotide variations or the copy number variations, differentially expressed genes and those in active subnetworks to construct a prognosis signature. Cox proportional hazards model with Lasso penalty and LOOCV was used to identify best gene signature among different gene categories.

We determined a 12-gene signature (BCHE, CCNA1, CYP24A1, DEPTOR, MASP2, MGLL, MYO1A, PODXL2, RAPGEF3, SGK2, TNNI2, ZBTB16) for prognostic risk prediction based on overall survival time of the patients with lung adenocarcinoma. The patients in both training and test data were clustered into high-risk and low-risk groups by using risk scores of the patients calculated based on selected gene signature. The overall survival probability of these risk groups was highly significantly different for both training and test datasets.

Conclusions

This 12-gene signature could predict the prognostic risk of the patients with lung adenocarcinoma in TCGA and they are potential predictors for the survival-based risk clustering of the patients with lung adenocarcinoma. These genes can be used to cluster patients based on molecular nature and the best candidates of drugs for the patient clusters can be proposed. These genes also have a high potential for targeted cancer therapy of patients with lung adenocarcinoma.

Development of a novel prognostic score combining clinicopathologic variables, gene expression, and mutation profiles for lung adenocarcinoma

BACKGROUND

Integrating phenotypic and genotypic information to improve prognostic prediction is under active investigation for lung adenocarcinoma (LUAD). In this study, we developed a new prognostic model for event-free survival (EFS) and recurrence-free survival (RFS) based on the combination of clinicopathologic variables, gene expression, and mutation data.

METHODS

We enrolled a total of 408 patients from the Cancer Genome Atlas Lung Adenocarcinoma (TCGA-LUAD) project for the study. We pre-selected gene expression or mutation features and constructed 14 different input feature sets for predictive model development. We assessed model performance with multiple evaluation metrics including the distribution of C-index on testing dataset, risk score significance, and time-dependent AUC under competing risks scenario. We stratified patients into higher- and lower-risk subgroups by the final risk score and further investigated underlying immune phenotyping variations associated with the differential risk.

RESULTS

The model integrating all three types of data achieved the best prediction performance. The resultant risk score provided a higher-resolution risk stratification than other models within pathologically defined subgroups. The score could account for extra EFS-related variations that were not captured by clinicopathologic scores. Being validated for RFS prediction under a competing risks modeling framework, the score achieved a significantly higher time-dependent AUC as compared to that of the conventional clinicopathologic variables-based model (0.772 vs. 0.646, p value < 0.001). The higher-risk patients were characterized with transcriptional aberrations of multiple immune-related genes, and a significant depletion of mast cells and natural killer cells.

CONCLUSIONS

We developed a novel prognostic risk score with improved prediction accuracy, using clinicopathologic variables, gene expression and mutation profiles as input, for LUAD. Such score was a significant predictor of both EFS and RFS.

TRIAL REGISTRATION

This study was based on public open data from TCGA and hence the study objects were retrospectively registered.

Long non‑coding RNA RP11‑400N13.3 promotes the progression of colorectal cancer by regulating the miR‑4722‑3p/P2RY8 axis

Accumulating evidence has shown that long non-coding RNAs (lncRNAs) play significant roles in the development and progression of many types of cancer including colorectal cancer. RP11-400N13.3 is a novel lncRNA discovered recently and its biological function and underlying mechanism in colorectal cancer remain elusive. This study aimed to reveal the relationship between RP11-400N13.3 and colorectal cancer. Our results demonstrated that the expression of RP11-400N13.3 was significantly upregulated in both colorectal cancer tissues and cell lines as compared to normal adjacent tissues and normal colonic epithelial cells by RT-qPCR, respectively. Upregulation of RP11-400N13.3 was found to be correlated with a poor overall survival rate. Functional studies revealed that RP11-400N13.3 facilitated the proliferation, migration, invasion and tumor growth of colorectal cancer cells while inhibiting the apoptosis of cancer cells in vitro and in vivo. We also observed that RP11-400N13.3 serves as a sponge for miR-4722-3p, and that P2Y receptor family member 8 (P2RY8) was predicted to be a target of miR-4722-3p by bioinformatics analysis. Western blot assay indicated that the expression of P2RY8 was negatively or positively regulated by miR-4722-3p or RP11-400N13.3. In addition, rescue experiments revealed that RP11-400N13.3 promoted proliferation, migration and invasion by directly regulating the expression of miR-4722-3p and P2RY8. In conclusion, our results revealed that RP11-400N13.3 promoted colorectal cancer progression via modulating the miR-4722-3p/P2RY8 axis, thus suggesting RP11-400N13.3 as a potential therapeutic target for the treatment of colorectal cancer.

A Four-Methylated lncRNAs-Based Prognostic Signature for Hepatocellular Carcinoma

Currently, an increasing number of studies suggest that long non-coding RNAs (lncRNAs) and methylation-regulated lncRNAs play a critical role in the pathogenesis of various cancers including hepatocellular carcinoma (HCC). Therefore, methylated differentially expressed lncRNAs (MDELs) may be critical biomarkers of HCC. In this study, 63 MDELs were identified by screening The Cancer Genome Atlas (TCGA) HCC lncRNAs expression data set and lncRNAs methylation data set. Based on univariate and multivariate survival analysis, four MDELs (AC025016.1, LINC01164, LINC01183 and LINC01269) were selected to construct the survival prognosis prediction model. Through the PI formula, the study indicates that our new prediction model performed well and is superior to the traditional staging method. At the same time, compared with the previous prediction models reported in the literature, the results of time-dependent receiver operating characteristic (ROC) curve analysis show that our 4-MDELs model predicted overall survival (OS) stability and provided better prognosis. In addition, we also applied the prognostic model to Cancer Cell Line Encyclopedia (CCLE) cell lines and classified different hepatoma cell lines through the model to evaluate the sensitivity of different hepatoma cell lines to different drugs. In conclusion, we have established a new risk scoring system to predict the prognosis, which may have a very important guiding significance for the individualized treatment of HCC patients.

Serum LncRNA-ATB and FAM83H-AS1 as diagnostic/prognostic non-invasive biomarkers for breast cancer

AIMS

Circulating long non-coding RNAs (lncRNAs) have proven to be useful non-invasive tools for diagnosis of various cancers. FAM83H antisense RNA 1 (FAM83H-AS1) and lncRNA activated by TGF β (lncRNA-ATB) are two lncRNAs that have been shown to play an important role in different cancer types including breast cancer. The primary aim of our study was to investigate the potential role of serum FAM83H-AS1 and lncRNA-ATB as diagnostic/prognostic markers for breast cancer patients.

MAIN METHODS

Serum expression levels of FAM83H-AS1 and lncRNA-ATB were analyzed in 90 breast cancer patients and 30 age- and sex-matched healthy controls using RT-qPCR.

KEY FINDINGS

We found that FAM83H-AS1 and lncRNA-ATB were significantly overexpressed in sera of breast cancer patients compared to controls (p = 0.000 for both). Analysis of receiver operating characteristic curve demonstrated that lncRNA-ATB had a higher area under curve (AUC) value than the conventional tumor marker cancer antigen 15-3 (CA15-3) (AUC: 0.844, p = 0.000 versus 0.738, p = 0.002) for early diagnosis of breast cancer in patients with stage I-II. On the other hand, FAM83H-AS1 showed a significant correlation with tumor-node metastasis (TNM) stages, large tumor size and lymph node metastasis, suggesting a prognostic rather than diagnostic value.

SIGNIFICANCE

This is the first study to demonstrate that serum lncRNA-ATB could be used as a non-invasive diagnostic marker for early stages of breast cancer. Furthermore, serum FAM83H-AS1 has a potential ability for monitoring of progression and staging of breast cancer.

Development and Validation of a 12-Gene Immune Relevant Prognostic Signature for Lung Adenocarcinoma Through Machine Learning Strategies

Background: Although immunotherapy with checkpoint inhibitors is changing the face of lung adenocarcinoma (LUAD) treatments, only limited patients could benefit from it. Therefore, we aimed to develop an immune-relevant-gene-based signature to predict LUAD patients' prognosis and to characterize their tumor microenvironment thus guiding therapeutic strategy.

Methods and Materials: Gene expression data of LUAD patients from Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA) were systematically analyzed. We performed Cox regression and random survival forest algorithm to identify immune-relevant genes with potential prognostic value. A risk score formula was then established by integrating these selected genes and patients were classified into high- and low-risk score group. Differentially expressed genes, infiltration level of immune cells, and several immune-associated molecules were further compared across the two groups.

Results: Nine hundred and fifty-four LUAD patients were enrolled in this study. After implementing the 2-steps machine learning screening methods, 12 immune-relevant genes were finally selected into the risk-score formula and the patients in high-risk group had significantly worse overall survival (HR = 10.6, 95%CI = 3.21–34.95, P < 0.001). We also found the distinct immune infiltration patterns in the two groups that several immune cells like cytotoxic cells and immune checkpoint molecules were significantly enriched and upregulated in patients from the high-risk group. These findings were further validated in two independent LUAD cohorts.

Conclusion: Our risk score formula could serve as a powerful and accurate tool for predicting survival of LUAD patients and may facilitate clinicians to choose the optimal therapeutic regimen more precisely.

Characterization of lncRNA-Associated ceRNA Network to Reveal Potential Prognostic Biomarkers in Lung Adenocarcinoma

Lung adenocarcinoma (LUAD) is one of the most fatal malignant tumors harmful to human health. The complexity and behavior characteristics of long-non-coding RNA (lncRNA)-associated competing endogenous RNA (ceRNA) network in LUAD patients are still unclear. The purpose of this study was to elucidate the regulatory networks of dysregulated RNAs, view, and identify potential prognosis signatures involved in LUAD. The expression profiles of mRNAs, lncRNAs, and miRNAs were obtained from the TCGA database. In total, 2078 DEmRNAs, 257 DElncRNAs, and 101 DEmiRNAs were sorted out. A PPI network including 45 DEmRNAs was constructed. Ten hub genes in the PPI network associated with cell cycle-related pathways were identified and they played key roles in regulating cell proliferation. A total of three DEmiRNAs, seven DElncRNAs, and six DEmRNAs were enrolled in the ceRNA network. Except for certain genes without any published study reports, all the genes in the ceRNA network played an essential role in controlling tumor cell proliferation and were associated with prognosis in LUAD. Finally, based on step regression and Cox regression survival analysis, we identified four candidate biomarkers, including miR490, miR1293, LINC01740, and IGF2BP1, and established a risk model based on the four genes. Our study provided a global view and systematic dissection of the lncRNA-associated ceRNA network, and the identified four genes might be novel important prognostic factors involved in LUAD pathogenesis.

Construction of ceRNA Coexpression Network and Screening of Molecular Targets in Colorectal Cancer

Objective

To screen some RNAs that correlated with colorectal cancer (CRC).

Methods

Differentially expressed miRNAs, lncRNAs, and mRNAs between cancer tissues and normal tissues in CRC were identified using data from the Gene Expression Omnibus (GEO) database. The Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway and protein-protein interactions (PPIs) were performed to do the functional enrichment analysis. And a lncRNA-miRNA-mRNA network was constructed which correlated with CRC. RNAs in this network were subjected to analyze the relationship with the patient prognosis.

Results

A total of 688, 241, and 103 differentially expressed genes (diff-mRNA), diff-lncRNA, and diff-miRNA were obtained between cancer tissues and normal tissues. A total of 315 edges were obtained in the ceRNA network. lncRNA RP11-108K3.2 and mRNA ONECUT2 correlated with prognosis.

Conclusion

The identified RNAs and constructed ceRNA network could provide great sources for the researches of therapy of the CRC. And the lncRNA RP11-108K3.2 and mRNA ONECUT2 may serve as a novel prognostic predictor of CRC.

Identification of a long non-coding RNA signature for predicting prognosis and biomarkers in lung adenocarcinoma

Long non-coding RNAs (lncRNAs) have a number of functions in various cellular processes and are potential prognostic factors for lung adenocarcinoma (LUAD). A gene risk model could provide novel evidence to improve the prediction of overall outcomes and provide more potential biomarkers. The present study aimed improve a previously published method of gene signature construction to make it more robust and accurate. The lncRNA expression profiles from 594 patients with LUAD were obtained from The Cancer Genome Atlas (TCGA) database and samples were divided into high- and low-risk groups based on median risk scores calculated using a prognosis-related risk score formula. Univariate Cox regression, least absolute shrinkage and selection operator algorithm and multivariate Cox regression were performed to construct a gene signature based on the differentially expressed lncRNAs in patients with LUAD. The robustness and accuracy of the present model was assessed using area under the calculated curves (AUC) and Kaplan-Meier (K-M) survival analysis of the high- and low-risk cohorts. Potential biomarkers associated with survival status were then identified using K-M survival analysis and potential biomarker functions were predicted using enrichment analysis of co-expressed mRNAs. The gene signature constructed contained 44 lncRNAs. The AUCs for 3- and 5-year survival with the model were 0.836 and 0.818, respectively, of a time-dependent receiver operator characteristic curve. Moreover, lncRNAs AC124804.1 and MIR34AHG were identified using K-M survival analysis and the potential function of these two lncRNAs was predicted using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes functional enrichment. The present lncRNA model provides novel insight which may improve prediction of prognosis for patients with LUAD and identify potentially novel biomarkers for the diagnosis.

IRGS: an immune-related gene classifier for lung adenocarcinoma prognosis

Background

Tumour cells interfere with normal immune functions by affecting the expression of some immune-related genes, which play roles in the prognosis of cancer patients. In recent years, immunotherapy for tumours has been widely studied, but a practical prognostic model based on immune-related genes in lung adenocarcinoma comparable to existing model has not been established and reported.

Methods

We first obtained publicly accessible lung adenocarcinoma RNA expression data from The Cancer Genome Atlas (TCGA) for differential gene expression analysis and then filtered immune-related genes based on the ImmPort database. By using the lasso algorithm and multivariate Cox Proportional-Hazards (CoxPH) regression analysis, we identified candidate genes for model development and validation. The robustness of the model was further examined by comparing the model with three established gene models.

Results

Gene expression data from a total of 524 lung adenocarcinoma patients from TCGA were used for model development. We identified four biomarkers (MAP3K8, CCL20, VEGFC, and ANGPTL4) that could predict overall survival in lung adenocarcinoma (HR = 1.98, 95% CI 1.48 to 2.64, P = 4.19e−06) and this model could be used as a classifier for the evaluation of low-risk and high-risk groups. This model was validated with independent microarray data and was highly comparable with previously reported gene expression signatures for lung adenocarcinoma prognosis.

Conclusions

In this study, we identified a practical and robust four-gene prognostic model based on an immune gene dataset with cross-platform compatibility. This model has potential value in improving TNM staging for survival predictions in patients with lung adenocarcinoma.

Impact

The study provides a method of immune relevant gene prognosis model and the identification of immune gene classifier for the prediction of lung adenocarcinoma prognosis with RNA sequencing and microarray compatibility.

Comprehensive Analysis of Aberrantly Expressed Profiles of lncRNAs and miRNAs with Associated ceRNA Network in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma

Lung cancer (LC) continues to be the leading cause of cancer-related deaths worldwide and the prognosis remains poor worldwide. At present, the long non-coding RNAs (lncRNAs) was considered as a part of competing endogenous RNA (ceRNA) network act as natural microRNA (miRNA) sponges to regulate protein-coding gene expression. However, functional roles of lncRNA-mediated ceRNAs in LC are insufficiently understood. To classify the specific mechanism of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), we comprehensively compared the expression profiles of mRNAs, lncRNAs and miRNAs obtained from 509 LUAD, 473 LUSC tissues and 49 adjacent non-cancerous lung tissues, based on The Cancer Genome Atlas (TCGA). After screening for differently expressed (DE) mRNAs, DEmiRNAs, DElncRNAs and weighted gene co-expression network analysis (WGCNA) (|log2FC| > 2.0 and an adjusted p value <0.05), a total of 4478 DEmRNAs, 526 DElncRNAs and 75 DEmiRNAs in LUAD, while 6237 DEmRNAs, 843 DElncRNAs and 117 DEmiRNAs in LUSC were discovered. Interaction (PPI) network analysis was performed to identify 656 nodes and 2987 edges (minimum required interaction score > 0.9), as well as 8 different protein-protein interactions. Gene ontology (GO) analysis mainly associated with cell proliferation. KEGG pathway enrichment analyses most partly associated with metabolism pathway and cytokine-cytokine receptor interaction. Finally, the dysregulated lncRNA-miRNA-ceRNA network was constructed based on correlation analyses and a total of 62 dysregulated lncRNAs, 28 DEmRNAs and 18 DEmiRNAs were involved. The most significant lncRNAs included DElncRNAs, LINC00641 and AC004947.2, miRNAs included miR-6860, miR-1285-3p, miR-767-3p and miR-7974, mRNAs included MAP3K3, FGD3 and ATP1B2. Then we analyzed and described the potential characteristics of biological function and pathological roles of the LUAD and LUSC ceRNA co-regulatory network. Our findings revealed ceRNA network will be beneficial for promoting the understanding of lncRNA-mediated ceRNA regulatory mechanisms in the pathogenesis of LUAD and LUSC.

Comprehensive analysis of TPX2-related ceRNA network as prognostic biomarkers in lung adenocarcinoma

Background and aim: Competing endogenous RNA (ceRNA) is believed to play vital roles in tumorigenesis. The goal of this study was to screen prognostic biomarkers in lung adenocarcinoma (LUAD). Methods: Common differentially expressed genes (DEGs) were collected from Gene Expression Omnibus (GEO) databases and The Cancer Genome Atlas databases (TCGA) using GEO2R and "limma" package in R, respectively. Overlapping DEGs were conducted using enrichment of functions and protein-protein interaction (PPI) network to discover significant candidate genes. By using a comprehensive analysis, we constructed an mRNA mediated ceRNA network. Survival rates were used Kaplan-Meier analysis. Statistical analysis was used to further identify the prognosis of studied genes. Results: Integrated analysis of GSE32863 and TCGA databases, a total of 886 overlapping DEGs, including 279 up-regulated and 607 down-regulated genes were identified. Considering the highest term of candidate genes in PPI, we identified TPX2, which was enriched in cell division signaling pathway. Besides, 35 differentially expressed miRNAs (DEmiRNAs) were predicted to target TPX2 and only 7 DEmiRNAs were identified to be prognostic biomarkers in LUAD. Then, 30 differentially expressed lncRNAs (DElncRNAs) were predicted to bind these 7 DEmiRNAs. Finally, we found that 7 DElncRNAs were correlated with the overall survival (all p <0.05). Furthermore, we identified elevated TPX2 was strongly correlated with the worse survival rate among 458 samples. Univariate and multivariate cox analysis showed TPX2 may act as an independent factor for prognosis in LUAD (p <0.05). Then pathway enrichment results suggested that TPX2 may facilitate tumorigenesis by participating in several cancer-related signaling pathways in LUAD, especially in Notch signal pathway. Conclusions: TPX2-related lncRNAs and miRNAs are related to the survival of LUAD. 7 lncRNAs, 7 miRNAs and TPX2 may serve as prognostic biomarkers in LUAD.

7-lncRNA Assessment Model for Monitoring and Prognosis of Breast Cancer Patients: Based on Cox Regression and Co-expression Analysis

Background: Breast cancer is one of the deadliest malignant tumors worldwide. Due to its complex molecular and cellular heterogeneity, the efficacy of existing breast cancer risk prediction models is unsatisfactory. In this study, we developed a new lncRNA model to predict the prognosis of patients with BRCA.

Methods: BRCA-related differentially-expressed long non-coding RNA were screened from the Cancer Genome Atlas database. A novel lncRNA model was developed by univariate and multivariate analyses to predict the prognosis of patients with BRCA. The efficacy of the model was verified by TCGA-based breast cancer samples. Identified lncRNA-related mRNA based on the co-expression method.

Results: We constructed a 7-lncRNA breast cancer prediction model including LINC00377, LINC00536, LINC01224, LINC00668, LINC01234, LINC02037, and LINC01456. The breast cancer samples were divided into high-risk and low-risk groups based on the model, which verified the specificity and sensitivity of the model. The Area Under Curve (AUC) of the 3- and 5-year Receiver Operating Characteristic curve were 0.711 and 0.734, respectively, indicating that the model has good performance.

Conclusion: We constructed a 7-lncRNA model to predict the prognosis of patients with BRCA, and suggest that these lncRNAs may play a specific role in the carcinogenesis of BRCA.

A large cohort study identifying a novel prognosis prediction model for lung adenocarcinoma through machine learning strategies

BACKGROUND

Predicting lung adenocarcinoma (LUAD) risk is crucial in determining further treatment strategies. Molecular biomarkers may improve risk stratification for LUAD.

METHODS

We analyzed the gene expression profiles of LUAD patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). We initially used three distinct algorithms (sigFeature, random forest, and univariate Cox regression) to evaluate each gene's prognostic relevance. Survival related genes were then fitted into the least absolute shrinkage and selection operator (LASSO) model to build a risk prediction model for LUAD. After 100,000 times of calculation and model construction, a 16-gene-based prediction model capable of classifying LUAD patients into high-risk and low-risk groups was successfully built.

RESULTS

Using a combined strategy, we initially identified 2472 significant survival-related genes. Functional enrichment analysis demonstrated these genes' relevance to tumor initiation and progression. Using the LASSO method, we successfully built a reliable risk prediction model. The risk model was validated in two external sets and an independent set. The expression of these 16 genes was highly correlated with patients' risk. High-risk group patients witnessed poorer recurrence-free survival (RFS) and overall survival (OS) compared to low-risk group patients. Moreover, stratification analysis and decision curve analysis (DCA) confirmed the independence and potential translational value of this predictive tool. We also built a nomogram comprising risk model and stage to predict OS for LUAD patients.

CONCLUSIONS

Our risk model may serve as a practical and reliable prognosis predictive tool for LUAD and could provide novel insights into the understanding of the molecular mechanism of this disease.

Single-cell cloning of human T-cell lines reveals clonal variation in cell death responses to chemotherapeutics

Genetic modification of human leukemic cell lines using CRISPR-Cas9 has become a staple of gene-function studies. Single-cell cloning of modified cells is frequently used to facilitate studies of gene function. Inherent in this approach is an assumption that the genetic drift, amplified in some cell lines by mutations in DNA replication and repair machinery, as well as non-genetic factors will not introduce significant levels of experimental cellular heterogeneity in clones derived from parental populations. In this study, we characterize the variation in cell death of fifty clonal cell lines generated from human Jurkat and MOLT-4 T-cells edited by CRISPR-Cas9. We demonstrate a wide distribution of sensitivity to chemotherapeutics between non-edited clonal human leukemia T-cell lines, and also following CRISPR-Cas9 editing at the NLRP1 locus, or following transfection with non-targeting sgRNA controls. The cell death sensitivity profile of clonal cell lines was consistent across experiments and failed to revert to the non-clonal parental phenotype. Whole genome sequencing of two clonal cell lines edited by CRISPR-Cas9 revealed unique and shared genetic variants, which had minimal read support in the non-clonal parental population and were not suspected CRISPR-Cas9 off-target effects. These variants included genes related to cell death and drug metabolism. The variation in cell death phenotype of clonal populations of human T-cell lines may be a consequence of T-cell line genetic instability, and to a lesser extent clonal heterogeneity in the parental population or CRISPR-Cas9 off-target effects not predicted by current models. This work highlights the importance of genetic variation between clonal T-cell lines in the design, conduct, and analysis of experiments to investigate gene function after single-cell cloning.

LUADpp: an effective prediction model on prognosis of lung adenocarcinomas based on somatic mutational features

Background

Lung adenocarcinoma is the most common type of lung cancers. Whole-genome sequencing studies disclosed the genomic landscape of lung adenocarcinomas. however, it remains unclear if the genetic alternations could guide prognosis prediction. Effective genetic markers and their based prediction models are also at a lack for prognosis evaluation.

Methods

We obtained the somatic mutation data and clinical data for 371 lung adenocarcinoma cases from The Cancer Genome Atlas. The cases were classified into two prognostic groups (3-year survival), and a comparison was performed between the groups for the somatic mutation frequencies of genes, followed by development of computational models to discrete the different prognosis.

Results

Genes were found with higher mutation rates in good (≥ 3-year survival) than in poor (< 3-year survival) prognosis group of lung adenocarcinoma patients. Genes participating in cell-cell adhesion and motility were significantly enriched in the top gene list with mutation rate difference between the good and poor prognosis group. Support Vector Machine models with the gene somatic mutation features could well predict prognosis, and the performance improved as feature size increased. An 85-gene model reached an average cross-validated accuracy of 81% and an Area Under the Curve (AUC) of 0.896 for the Receiver Operating Characteristic (ROC) curves. The model also exhibited good inter-stage prognosis prediction performance, with an average AUC of 0.846 for the ROC curves.

Conclusion

The prognosis of lung adenocarcinomas is related with somatic gene mutations. The genetic markers could be used for prognosis prediction and furthermore provide guidance for personal medicine.

Electronic supplementary material

The online version of this article (10.1186/s12885-019-5433-7) contains supplementary material, which is available to authorized users.