Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma

Development and validation of an interferon signature predicting prognosis and treatment response for glioblastoma

Background: Interferon treatment, as an important approach of anti-tumor immunotherapy, has been implemented in multiple clinical trials of glioma. However, only a small number of gliomas benefit from it. Therefore, it is necessary to investigate the clinical role of interferons and to establish robust biomarkers to facilitate its application. Materials and methods: This study reviewed 1,241 glioblastoma (GBM) and 1,068 lower grade glioma (LGG) patients from six glioma cohorts. The transcription matrix and clinical information were analyzed using R software, GraphPad Prism 7 and Medcalc, etc. Immunohistochemical (IHC) staining were performed for validation in protein level. Results: Interferon signaling was significantly enhanced in GBM. An interferon signature was developed based on five interferon genes with prognostic significance, which could reflect various interferon statuses. Survival analysis showed the signature could serve as an unfavorable prognostic factor independently. We also established a nomogram model integrating the risk signature into traditional prognostic factors, which increased the validity of survival prediction. Moreover, high-risk group conferred resistance to chemotherapy and high IFNB1 expression levels. Functional analysis showed that the high-risk group was associated with overloaded immune response. Microenvironment analysis and IHC staining found that high-risk group occupied a disorganized microenvironment which was characterized by an enrichment of M0 macrophages and neutrophils, but less infiltration of activated nature killing (NK) cells and M1 type macrophages. Conclusion: This interferon signature was an independent indicator for unfavorable prognosis and showed great potential for screening out patients who will benefit from chemotherapy and interferon treatment.

Large-scale gene expression analysis reveals robust gene signatures for prognosis prediction in lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the leading cause of cancer-related death worldwide. High mortality in LUAD motivates us to stratify the patients into high- and low-risk groups, which is beneficial for the clinicians to design a personalized therapeutic regimen. To robustly predict the risk, we identified a set of robust prognostic gene signatures and critical pathways based on ten gene expression datasets by the meta-analysis-based Cox regression model, 25 of which were selected as predictors of multivariable Cox regression model by MMPC algorithm. Gene set enrichment analysis (GSEA) identified the Aurora-A pathway, the Aurora-B pathway, and the FOXM1 transcription factor network as prognostic pathways in LUAD. Moreover, the three prognostic pathways were also the biological processes of G2-M transition, suggesting that hyperactive G2-M transition in cell cycle was an indicator of poor prognosis in LUAD. The validation in the independent datasets suggested that overall survival differences were observed not only in all LUAD patients, but also in those with a specific TNM stage, gender, and age group. The comprehensive analysis demonstrated that prognostic signatures and the prognostic model by the large-scale gene expression analysis were more robust than models built by single data based gene signatures in LUAD overall survival prediction.

Gene signature associated with neuro-endocrine activity predicting prognosis of pancreatic carcinoma

Background

Genomic analysis is the promising tool to clear understanding of the tumorigenesis and guide molecular classification for pancreatic cancer. Our purpose was to develop a critical predictive model for prognosis in pancreatic carcinoma, based on the genomic data.

Methods

The online The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets were queried as training and validation cohorts for comprehensive bioinformatic analysis. We applied Lasso and multivariate Cox regression to shrink genes and construct predictive model.

Results

A four genes model (DNAH10: HR = 0.71, 95% CI = 0.57–0.88, HSBP1L1: HR = 1.51, 95% CI = 1.18–1.92, KIAA0513: HR = 0.69, 95% CI = 0.50–0.96, and MRPL3: HR = 3.73, 95% CI = 2.03–6.86), was proposed and validated. The C‐index was 0.73 (95% CI: 0.7–0.77). Patients in high‐risk and low‐risk group, stratified by model, suffered significantly different overall survival time (15.1 vs. 49.3 months, p < 0.0001 in TCGA; 423 vs. 618 days, p = 0.038 in ICGC). Taken clinical parameters into consideration, the risk‐score was independent marker in clinical subpopulation. To explore the molecular mechanisms, 579 differential expression genes (DEG) in two groups were identified by edgeR. Functional enrichment of DEG indicated neuro‐endocrine activity was the potential mechanism for the discrepant prognosis.

Conclusion

A specific four genes signature with the ability to predicted survival of pancreatic carcinoma was generated, which may indicate the connection between neuro‐endocrine activity and patients’ prognosis.

Dynamic Recruitment of Single RNAs to Processing Bodies Depends on RNA Functionality

Cellular RNAs often colocalize with cytoplasmic, membrane-less ribonucleoprotein (RNP) granules enriched for RNA-processing enzymes, termed processing bodies (PBs). Here we track the dynamic localization of individual miRNAs, mRNAs, and long non-coding RNAs (lncRNAs) to PBs using intracellular single-molecule fluorescence microscopy. We find that unused miRNAs stably bind to PBs, whereas functional miRNAs, repressed mRNAs, and lncRNAs both transiently and stably localize within either the core or periphery of PBs, albeit to different extents. Consequently, translation potential and 3' versus 5' placement of miRNA target sites significantly affect the PB localization dynamics of mRNAs. Using computational modeling and supporting experimental approaches, we show that partitioning in the PB phase attenuates mRNA silencing, suggesting that physiological mRNA turnover occurs predominantly outside of PBs. Instead, our data support a PB role in sequestering unused miRNAs for surveillance and provide a framework for investigating the dynamic assembly of RNP granules by phase separation at single-molecule resolution.

A gene signature associated with prognosis and immune processes in head and neck squamous cell carcinoma

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) has a poor prognosis that has not significantly improved in the past several decades. A prognostic-related signature was needed.

METHODS

The Cancer Genome Atlas and GSE41613 databases were downloaded as a training and validation set, respectively. We identified 12 genes that demonstrated progression and prognostic value, and then, a gene signature was constructed.

RESULTS

This classification could reflect distinct characteristics, phenotypically and molecularly, among HNSCC tumors. It could stratify patients with significantly different survival rates (median survival: 2083 days vs 927 days; P = 3.85E-08) in the training cohort and validation cohort (P = 0.007) and was significantly involved in immune/inflammatory response and tumor progression processes.

CONCLUSIONS

This bioinformatics-based signature suggested the presence of two distinct populations of patients with HNSCC with distinguishable phenotypic characteristics and clinical outcomes and might provide insight for new types of immune therapy.

A Novel Cluster-Based Computational Method to Identify miRNA Regulatory Modules

The identification of miRNA regulatory modules can help decipher miRNAs combinatorial regulation effects on the pathogenesis underlying complex diseases, especially in cancer. By integrating miRNA/mRNA expression profiles and sequence-based predicted target site information, we develop a novel cluster-based computational method named CoModule for identifying miRNA regulatory modules (MRMs). The ultimate goal of CoModule is to detect the MRMs, in which the miRNAs in each module are expected to present cooperative mechanisms in regulating their targets mRNAs. Here, the co-expression of miRNAs are believed to present cooperative regulatory relationship, therefore, the critical step of CoModule is first to partition the miRNAs with similar expression into a cluster by employing rough set clustering. After gaining credible miRNA clusters, the targets of regulator are naturally added into corresponding clusters to produce the final miRNA regulatory modules. We apply this present method to ovarian cancer datasets and make a comparison with the other two existing prominent approaches. The results indicate that the modules identified by CoModule perform better than the other two methods ranging from the topological aspects to the biological function. Survival analysis detects a number of prognostic modules with statistical significance, which can help reveal the potential diagnostic for ovarian cancer.

Development of somatic mutation signatures for risk stratification and prognosis in lung and colorectal adenocarcinomas

Background

Prognostic signatures are vital to precision medicine. However, development of somatic mutation prognostic signatures for cancers remains a challenge. In this study we developed a novel method for discovering somatic mutation based prognostic signatures.

Results

Somatic mutation and clinical data for lung adenocarcinoma (LUAD) and colorectal adenocarcinoma (COAD) from The Cancer Genome Atlas (TCGA) were randomly divided into training (n = 328 for LUAD and 286 for COAD) and validation (n = 167 for LUAD and 141 for COAD) datasets. A novel method of using the log2 ratio of the tumor mutation frequency to the paired normal mutation frequency is computed for each patient and missense mutation. The missense mutation ratios were mean aggregated into gene-level somatic mutation profiles. The somatic mutations were assessed using univariate Cox analysis on the LUAD and COAD training sets separately. Stepwise multivariate Cox analysis resulted in a final gene prognostic signature for LUAD and COAD. Performance was compared to gene prognostic signatures generated using the same pipeline but with different somatic mutation profile representations based on tumor mutation frequency, binary calls, and gene-gene network normalization. Signature high-risk LUAD and COAD cases had worse overall survival compared to the signature low-risk cases in the validation set (log-rank test p-value = 0.0101 for LUAD and 0.0314 for COAD) using mutation tumor frequency ratio (MFR) profiles, while all other methods, including gene-gene network normalization, have statistically insignificant stratification (log-rank test p-value ≥0.05). Most of the genes in the final gene signatures using MFR profiles are cancer-related based on network and literature analysis.

Conclusions

We demonstrated the robustness of MFR profiles and its potential to be a powerful prognostic tool in cancer. The results are robust according to validation testing and the selected genes are biologically relevant.

BiModule: biclique modularity strategy for identifying transcription factor and microRNA co-regulatory modules

Systematic identification of gene regulatory modules can provide invaluable knowledge towards understanding aberrant transcriptional/post-transcriptional collaborative regulatory (co-regulatory) effects in cancer. Transcription factor (TF) and microRNA (miRNA) are known as two classes of prominent regulators that play crucial roles in gene regulation. Existing studies on gene regulatory modules identification mainly focused on the miRNA-mediated regulatory network, and few considered these two regulators in a co-occurring network. In this current study, we developed a computational method called BiModule for systematically identifying TF-miRNA co-regulatory modules. BiModule operates in two main stages: it first constructs a cancerspecific regulator-mRNA network and then identifies modules based on maximal bicliques by employing biclique modularity strategy, which is a novel flexible method for bipartite graph mining. We applied our model to a cervical cancer dataset. The results showed that the TF-miRNA co-regulatory modules identified by BiModule exhibit denser connections and stronger expression correlations than another existing related method. Moreover, the BiModule-modules exhibit high biological functional enrichment. In addition, based on Kaplan-Meier survival analysis, we found a number of modules with significant prognostic associations.

Development of a miRNA-seq based prognostic signature in lung adenocarcinoma

Background

We utilized miRNAs expression and clinical data to develop a prognostic signature for patients with lung adenocarcinoma, with respect to their overall survival, to identify high-risk subjects based on their miRNA genomic profile.

Methods

MiRNA expressions based on miRNA sequencing and clinical data of lung adenocarcinoma patients (n = 479) from the Cancer Genome Atlas were randomly partitioned into non-overlapping Model (n = 320) and Test (n = 159) sets, respectively, for model estimation and validation.

Results

Among the ten miRNAs identified using the univariate Cox analysis, six from miR-8, miR-181, miR-326, miR-375, miR-99a, and miR-10, families showed improvement of the overall survival chance, while two miRNAs from miR-582 and miR-584 families showed a worsening of survival chances. The final prognostic signature was developed with five miRNAs—miR-375, miR-582-3p, miR-326, miR-181c-5p, and miR-99a-5p—utilizing a stepwise variable selection procedure. Using the KEGG pathway analysis, we found potential evidence supporting their significance in multiple cancer pathways, including non-small cell lung cancer. We defined two risk groups with a score calculated using the Cox regression coefficients. The five-year survival rates for the low-risk group was approximately 48.76% (95% CI = (36.15, 63.93)); however, it was as low as 7.50% (95% CI = (2.34, 24.01)) for the high-risk group. Furthermore, we demonstrated the effect of the genomic profile using the miRNA signature, quantifying survival rates for hypothetical subjects in different pathological stages of cancer.

Conclusions

The proposed prognostic signature can be used as a reliable tool for identifying high-risk subjects regarding survival based on their miRNA genomic profile.

Electronic supplementary material

The online version of this article (10.1186/s12885-018-5206-8) contains supplementary material, which is available to authorized users.

A positive feedback loop between ZNF205-AS1 and EGR4 promotes non-small cell lung cancer growth

Accumulating evidences revealed that long noncoding RNAs (lncRNAs) are frequently implicated in non‐small cell lung cancer (NSCLC). Herein, we reported the identification of a novel NSCLC‐associated functional lncRNA ZNF205 antisense RNA 1 (ZNF205‐AS1). ZNF205‐AS1 was increased in NSCLC tissues and cell lines, and associated with poor prognosis of NSCLC patients. Bioinformatics prediction, combined with experimental verification revealed that early growth response 4 (EGR4) directly bound to ZNF205‐AS1 promoter, increased the promoter activity of ZNF205‐AS1, and activated ZNF205‐AS1 transcription. Intriguingly, ZNF205‐AS1 transcript directly interacted with EGR4 mRNA, increased EGR4 mRNA stability, and up‐regulated EGR4 expression via RNA‐RNA interaction. Thus, ZNF205‐AS1 and EGR4 formed a positive feedback loop. Through regulating EGR4, ZNF205‐AS1 activated its own promoter activity. EGR4 was also increased in NSCLC and the expression of ZNF205‐AS1 was significantly positively correlated with EGR4 in NSCLC tissues. Gain‐of‐function and loss‐of‐function assays demonstrated that both ZNF205‐AS1 and EGR4 promoted NSCLC cell growth in vitro and NSCLC tumour growth in vivo. Concurrently depleting ZNF205‐AS1 and EGR4 more significantly repressed NSCLC tumour growth in vivo. Collectively, our study demonstrated that the positive feedback loop between ZNF205‐AS1 and EGR4 promotes NSCLC growth, and implied that targeting this feedback loop may be promising therapeutic strategy for NSCLC.

Identification of 4-lncRNA prognostic signature in head and neck squamous cell carcinoma

Deregulated long noncoding RNAs (lncRNA) have been critically implicated in tumorigenesis and serve as novel diagnostic and prognostic biomarkers. Here we sought to develop a prognostic lncRNA signature in patients with head and neck squamous cell carcinoma (HNSCC). Original RNA-seq data of 499 HNSCC samples were retrieved from The Cancer Genome Atlas database, which was randomly divided into training and testing set. Univariate Cox regression survival analysis, robust likelihood-based survival model and random sampling iterations were applied to identify prognostic lncRNA candidates in the training cohort. A prognostic risk score was developed based on the Cox coefficient of four individual lncRNA imputed as follows: (0.14546 × expression level of RP11-366H4.1) + (0.27106 × expression level of LINC01123) + (0.54316 × expression level of RP11-110I1.14) + (-0.48794 × expression level of CTD-2506J14.1). Kaplan-Meier analysis revealed that patients with high-risk score had significantly reduced overall survival as compared with those with low-risk score when patients in training, testing, and validation cohorts were stratified into high- or low-risk subgroups. Multivariate survival analysis further revealed that this 4-lncRNA signature was a novel and important prognostic factor independent of multiple clinicopathological parameters. Importantly, ROC analyses indicated that predictive accuracy and sensitivity of this 4-lncRNA signature outperformed those previously well-established prognostic factors. Noticeably, prognostic score based on quantification of these 4-lncRNA via qRT-PCR in another independent HNSCC cohort robustly stratified patients into subgroups with high or low survival. Taken together, we developed a robust 4-lncRNA prognostic signature for HNSCC that might provide a novel powerful prognostic biomarker for precision oncology.

Genetic variants in nuclear DNA along with environmental factors modify mitochondrial DNA copy number: a population-based exome-wide association study

Background

Mitochondrial DNA (mtDNA) copy number has been found associated with multiple diseases, including cancers, diabetes and so on. Both environmental and genetic factors could affect the copy number of mtDNA. However, limited study was available about the relationship between genetic variants and mtDNA copy number. What’s more, most of previous studies considered only environmental or genetic factors. Therefore, it’s necessary to explore the genetic effects on mtDNA copy number with the consideration of PM_2.5 exposure and smoking.

Results

A multi-center population-based study was performed with 301 subjects from Zhuhai, Wuhan and Tianjin. Personal 24-h PM_2.5 exposure levels, smoking and mtDNA copy number were evaluated. The Illumina Human Exome BeadChip, which contained 241,305 single nucleotide variants, was used for genotyping. The association analysis was conducted in each city and meta-analysis was adopted to combine the overall effect among three cities. Seven SNPs showed significant association with mtDNA copy number with P value less than 1.00E-04 after meta-analysis. The following joint analysis of our identified SNPs showed a significant allele-dosage association between the number of variants and mtDNA copy number (P = 5.02 × 10^− 17). Further, 11 genes were identified associated with mtDNA copy number using gene-based analysis with a P value less than 0.01.

Conclusion

This study was the first attempt to evaluate the genetic effects on mtDNA copy number with the consideration of personal PM_2.5 exposure level. Our findings could provide more evidences that genetic variants played important roles in modulating the copy number of mtDNA.

Electronic supplementary material

The online version of this article (10.1186/s12864-018-5142-7) contains supplementary material, which is available to authorized users.

Molecular characterization of papillary thyroid carcinoma: a potential three-lncRNA prognostic signature

Purpose

Papillary thyroid carcinoma (PTC), the most frequent type of malignant thyroid tumor, lacks novel and reliable biomarkers of patients’ prognosis. In the current study, we mined The Cancer Genome Atlas (TCGA) to develop lncRNA signature of PTC.

Patients and methods

The intersection of PTC lncRNAs was obtained from the TCGA database using integrative computational method. By the univariate and multivariate Cox analysis, key lncRNAs were identified to construct the prognostic model. Then, all patients were divided into the high-risk group and low-risk group to perform the Kaplan–Meier (K–M) survival curves and time-dependent receiver operating characteristic (ROC) curve, estimating the prognostic power of the prognostic model. Functional enrichment analysis was also performed. Finally, we verified the results of the TCGA analysis by the Gene Expression Omnibus (GEO) databases and quantitative real-time PCR (qRT-PCR).

Results

After the comprehensive analysis, a three-lncRNA signature (PRSS3P2, KRTAP5-AS1 and PWAR5) was obtained. Interestingly, patients with low-risk scores tended to gain obviously longer survival time, and the area under the time-dependent ROC curve was 0.739. Furthermore, gene ontology (GO) and pathway analysis revealed the tumorigenic and prognostic function of the three lncRNAs. We also found three potential transcription factors to help understand the mechanisms of the PTC-specific lncRNAs. Finally, the GEO databases and qRT-PCR validation were consistent with our TCGA bioinformatics results.

Conclusion

We built a three-lncRNA signature by mining the TCGA database, which could effectively predict the prognosis of PTC.

A B7-CD28 family based signature demonstrates significantly different prognoses and tumor immune landscapes in lung adenocarcinoma

B7 family ligands and CD28 family receptors have complicated interaction for modulating immune functions. They play a central role in response to immunotherapy and outcome of patients with lung adenocarcinoma (LUAD). Thus, we analyzed B7-CD28 family gene expression profiles in LUAD and generated a signature to predict prognosis and immune host status. B7-CD28 family gene expression profiles and clinical data of LUAD from The Cancer Genome Atlas (TCGA) were analyzed. In the training cohort, prognostic association was assessed and then a prognostic signature was built with stepwise multivariable Cox analysis. The signature was validated by Kaplan-Meier and multivariable Cox analysis in several published gene expression datasets and a Fudan University cohort. Expression of immune cell populations and other immunotherapy predictors was further investigated. In TCGA LUAD cohort, eight B7-CD28 family genes had prognostic association with p values <0.05. Stepwise regression generated a gene signature including two genes, CD28 and CD276. Signature high-risk cases had worse overall survival (OS) and disease-free survival (DFS) in three published gene expression datasets and a Fudan University validation cohort. The B7-CD28 family based signature also significantly stratified OS and DFS in important clinical subsets, including stage I-II and EGFR mutant subsets. Signature high- and low-risk tumor had significantly different expressions of PD-L1 and tumor infiltrating leukocytes. The B7-CD28 family based signature demonstrates significantly different prognoses and tumor immune landscapes in LUAD. Whether it could serve as potential biomarkers for immunotherapy needs further investigation.

Decoding the LncRNA transcriptome of esophageal cancer: identification of clinically relevant LncRNAs

AIM

LncRNAs may act as promising biomarkers in esophageal cancer (EC). Here, we illustrate the LncRNA profile and their clinical relevance in EC.

PATIENTS & METHODS

In this study, we utilized the Cancer Genome Atlas RNA-sequencing and clinical data from 186 patients and 13 normal samples. Various statistical and gene set enrichment analysis (GSEA) were performed to identify the biomarkers.

RESULTS

In a differential expression analysis, we identified a total of 127 LncRNAs with more differentially expressed in EC compared with normal and showed their function using guilt-by-association analysis. We generated a LncRNAs prognostic signature for EC. Using Cox regression analysis, we showed the prognostic ability of LncRNAs' prognostic signature in training and test-cohort (p-value < 0.01).

CONCLUSION

In summary, we explored the LncRNA expression profile and their clinical utility in EC patients.

Molecular characterization of lung adenocarcinoma: A potential four-long noncoding RNA prognostic signature

BACKGROUND

Lung adenocarcinoma (LUAD), mainly originated in lung glandular cells, is the most frequent pathological type of lung cancer and the 5-year survival rate of LUAD patients is still very low. Therefore, we aim to identify a long noncoding RNA (lncRNA)-related signature as the sensitive and novel prognostic biomarkers.

METHODS

The associations between survival outcome and the intersection of lncRNAs were obtained from The Cancer Genome Atlas (TCGA) database. By the univariate and multivariate Cox analyses, key lncRNAs were identified to construct the prognostic model. The model was estimated by survival analysis and receiver operating characteristic curve, and verified by the Kaplan-Meier (K-M) plotter and quantitative reverse-transcription polymerase chain reaction (qRT-PCR). Functional enrichment analysis was also performed.

RESULTS

A four-lncRNA signature (CEBPA-AS1, GVINP1, MIR31HG, and RAET1K) was developed after Cox analysis. The power of the four-lncRNA prognostic signature was effective in the TCGA database. The results from by the K-M plotter and qRT-PCR validation were consistent with our TCGA bioinformatics results. Furthermore, Gene Ontology and pathway analysis revealed the tumorigenic and prognostic function of the four lncRNAs.

CONCLUSIONS

By mining the TCGA data, we built a four-lncRNA signature, which could effectively predict prognosis of LUAD. In the future, an independent cohort is needed to validate our findings.

IMPACT

The four-lncRNA signature could become potential prognostic indicator of LUAD in the future.

A network-based signature to predict the survival of non-smoking lung adenocarcinoma

Background

A substantial increase in the number of non-smoking lung adenocarcinoma (LAC) patients has been drawing extensive attention in the past decade. However, effective biomarkers, which could guide the precise treatment, are still limited for identifying high-risk patients. Here, we provide a network-based signature to predict the survival of non-smoking LAC.

Materials and methods

Gene expression profiles were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus. Significant gene co-expression networks and hub genes were identified by Weighted Gene Co-expression Network Analysis. Potential mechanisms and pathways of co-expression networks were analyzed by Gene Ontology. The predictive signature was constructed by penalized Cox regression analysis and tested in two independent datasets.

Results

Two distinct co-expression modules were significantly correlated with the non-smoking status across 4 Gene Expression Omnibus datasets. Gene Ontology revealed that nuclear division and cell cycle pathways were main mechanisms of the blue module and that genes in the turquoise module were involved in lymphocyte activation and cell adhesion pathways. Seventeen genes were selected from hub genes at an optimal lambda value and built the prognostic signature. The prognostic signature distinguished the survival of non-smoking LAC (training: hazard ratio [HR]=3.696, 95% CI: 2.025–6.748, P<0.001; testing: HR=2.9, 95% CI: 1.322–6.789, P=0.006; HR=2.78, 95% CI: 1.658–6.654, P=0.022) and had moderate predictive abilities in the training and validation datasets.

Conclusion

The prognostic signature is a promising predictor of non-smoking LAC patients, which might benefit clinical practice and precision therapeutic management.

Drug Resistance in Non-Small Cell Lung Cancer: A Potential for NOTCH Targeting?

Drug resistance is a major cause for therapeutic failure in non-small cell lung cancer (NSCLC) leading to tumor recurrence and disease progression. Cell intrinsic mechanisms of resistance include changes in the expression of drug transporters, activation of pro-survival, and anti-apoptotic pathways, as well as non-intrinsic influences of the tumor microenvironment. It has become evident that tumors are composed of a heterogeneous population of cells with different genetic, epigenetic, and phenotypic characteristics that result in diverse responses to therapy, and underlies the emergence of resistant clones. This tumor heterogeneity is driven by subpopulations of tumor cells termed cancer stem cells (CSCs) that have tumor-initiating capabilities, are highly self-renewing, and retain the ability for multi-lineage differentiation. CSCs have been identified in NSCLC and have been associated with chemo- and radiotherapy resistance. Stem cell pathways are frequently deregulated in cancer and are implicated in recurrence after treatment. Here, we focus on the NOTCH signaling pathway, which has a role in stem cell maintenance in non-squamous non-small lung cancer, and we critically assess the potential for targeting the NOTCH pathway to overcome resistance to chemotherapeutic and targeted agents using both preclinical and clinical evidence.

Clinical utility of the S3-score for molecular prediction of outcome in non-metastatic and metastatic clear cell renal cell carcinoma

BACKGROUND

Stratification of cancer patients to identify those with worse prognosis is increasingly important. Through in silico analyses, we recently developed a gene expression-based prognostic score (S3-score) for clear cell renal cell carcinoma (ccRCC), using the cell type-specific expression of 97 genes within the human nephron. Herein, we verified the score using whole-transcriptome data of independent cohorts and extend its application for patients with metastatic disease receiving tyrosine kinase inhibitor treatment. Finally, we sought to improve the signature for clinical application using qRT-PCR.

METHODS

A 97 gene-based S3-score (S3₉₇) was evaluated in a set of 52 primary non-metastatic and metastatic ccRCC patients as well as in 53 primary metastatic tumors of sunitinib-treated patients. Gene expression data of The Cancer Genome Atlas (n = 463) was used for platform transfer and development of a simplified qRT-PCR-based 15-gene S3-score (S3₁₅). This S3₁₅-score was validated in 108 metastatic and non-metastatic ccRCC patients and ccRCC-derived metastases including in part several regions from one metastasis. Univariate and multivariate Cox regression stratified by T, N, M, and G were performed with cancer-specific and progression-free survival as primary endpoints.

RESULTS

The S3₉₇-score was significantly associated with cancer-specific survival (CSS) in 52 ccRCC patients (HR 2.9, 95% Cl 1.0-8.0, P_Log-rank = 3.3 × 10^-2) as well as progression-free survival in sunitinib-treated patients (2.1, 1.1-4.2, P_Log-rank = 2.2 × 10^-2). The qRT-PCR based S3₁₅-score performed similarly to the S3₉₇-score, and was significantly associated with CSS in our extended cohort of 108 patients (5.0, 2.1-11.7, P_Log-rank = 5.1 × 10^-5) including metastatic (9.3, 1.8-50.0, P_Log-rank = 2.3 × 10^-3) and non-metastatic patients (4.4, 1.2-16.3, P_Log-rank = 1.6 × 10^-2), even in multivariate Cox regression, including clinicopathological parameters (7.3, 2.5-21.5, P_Wald = 3.3 × 10^-4). Matched primary tumors and metastases revealed similar S3₁₅-scores, thus allowing prediction of outcome from metastatic tissue. The molecular-based qRT-PCR S3₁₅-score significantly improved prediction of CSS by the established clinicopathological-based SSIGN score (P = 1.6 × 10^-3).

CONCLUSION

The S3-score offers a new clinical avenue for ccRCC risk stratification in the non-metastatic, metastatic, and sunitinib-treated setting.

Identification of Critical Genes and Five Prognostic Biomarkers Associated with Colorectal Cancer

BACKGROUND Colorectal cancer (CRC) is a common malignant tumor with high incidence and mortality worldwide. The aim of this study was to evaluate the association between differentially expressed genes (DEGs), which may function as biomarkers for CRC prognosis and therapies, and the clinical outcome in patients with CRC. MATERIAL AND METHODS A total of 116 normal mucous tissue and 930 CRC tissue datasets were downloaded from the Gene Expression Omnibus database (GEO) and The Cancer Genome Atlas (TCGA). After screening DEGs based on limma package in R. Gene Ontology (GO) and KEGG enrichment analysis as well as the protein-protein interaction (PPI) networks were performed to predict the function of these DEGs. Meanwhile, Cox proportional hazards regression was used to build a prognostic model of these DEGs. Then, Kaplan-Meier risk analysis was used to test the model in TCGA datasets and validation datasets. RESULTS In the present study, 300 DEGs with 100 upregulated genes and 200 downregulated genes were identified. The PPI networks including 162 DEGs and 256 nodes were constructed and 2 modules with high degree were selected. Moreover, 5 genes (MMP1, ACSL6, SMPD1, PPARGC1A, and HEPACAM2) were identified using the Cox proportional hazards stepwise regression. Kaplan-Meier risk curve in the TCGA and validation cohorts showed that high-risk group had significantly poor overall survival than the low-risk group. CONCLUSIONS Our study provided insights into the mechanisms of CRC formation and found 5 prognostic genes, which could potentially inform further studies and clinical therapies.

Transcriptome profiling of the interconnection of pathways involved in malignant transformation and response to hypoxia

In tumor tissues, hypoxia is a commonly observed feature resulting from rapidly proliferating cancer cells outgrowing their surrounding vasculature network. Transformed cancer cells are known to exhibit phenotypic alterations, enabling continuous proliferation despite a limited oxygen supply. The four-step isogenic BJ cell model enables studies of defined steps of tumorigenesis: the normal, immortalized, transformed, and metastasizing stages. By transcriptome profiling under atmospheric and moderate hypoxic (3% O₂) conditions, we observed that despite being highly similar, the four cell lines of the BJ model responded strikingly different to hypoxia. Besides corroborating many of the known responses to hypoxia, we demonstrate that the transcriptome adaptation to moderate hypoxia resembles the process of malignant transformation. The transformed cells displayed a distinct capability of metabolic switching, reflected in reversed gene expression patterns for several genes involved in oxidative phosphorylation and glycolytic pathways. By profiling the stage-specific responses to hypoxia, we identified ASS1 as a potential prognostic marker in hypoxic tumors. This study demonstrates the usefulness of the BJ cell model for highlighting the interconnection of pathways involved in malignant transformation and hypoxic response.

A New Approach to Predict Progression-free Survival in Stage IV EGFR-mutant NSCLC Patients with EGFR-TKI Therapy

Purpose: We established a CT-derived approach to achieve accurate progression-free survival (PFS) prediction to EGFR tyrosine kinase inhibitors (TKI) therapy in multicenter, stage IV EGFR-mutated non-small cell lung cancer (NSCLC) patients.Experimental Design: A total of 1,032 CT-based phenotypic characteristics were extracted according to the intensity, shape, and texture of NSCLC pretherapy images. On the basis of these CT features extracted from 117 stage IV EGFR-mutant NSCLC patients, a CT-based phenotypic signature was proposed using a Cox regression model with LASSO penalty for the survival risk stratification of EGFR-TKI therapy. The signature was validated using two independent cohorts (101 and 96 patients, respectively). The benefit of EGFR-TKIs in stratified patients was then compared with another stage-IV EGFR-mutant NSCLC cohort only treated with standard chemotherapy (56 patients). Furthermore, an individualized prediction model incorporating the phenotypic signature and clinicopathologic risk characteristics was proposed for PFS prediction, and also validated by multicenter cohorts.Results: The signature consisted of 12 CT features demonstrated good accuracy for discriminating patients with rapid and slow progression to EGFR-TKI therapy in three cohorts (HR: 3.61, 3.77, and 3.67, respectively). Rapid progression patients received EGFR TKIs did not show significant difference with patients underwent chemotherapy for progression-free survival benefit (P = 0.682). Decision curve analysis revealed that the proposed model significantly improved the clinical benefit compared with the clinicopathologic-based characteristics model (P < 0.0001).Conclusions: The proposed CT-based predictive strategy can achieve individualized prediction of PFS probability to EGFR-TKI therapy in NSCLCs, which holds promise of improving the pretherapy personalized management of TKIs. Clin Cancer Res; 24(15); 3583-92. ©2018 AACR.

Long non-coding RNA XIST inhibited breast cancer cell growth, migration, and invasion via miR-155/CDX1 axis

Long non-coding RNA (lncRNA) is an important member of non-coding RNA family and emerging evidence has indicated that it plays a pivotal role in many physiological and pathological processes. The lncRNA X inactive specific transcript (XIST) is a potential tumour suppressor in some types of cancers. However, the expression and function of XIST in breast cancer remain largely unclear. The objective of this study was to evaluate the expression and biological role of XIST in breast cancer. The results showed that XIST was significantly down-regulated in breast cancer tissues and cell lines. Further functional analysis indicated that overexpression of XIST remarkably inhibited breast cancer cell growth, migration, and invasion. The results of luciferase reporter assays verified that miR-155 was a direct target of XIST in breast cancer. Moreover, caudal-type homeobox 1 (CDX1) was identified as a direct target of miR-155 and miR-155/CDX1 rescued the effects of XIST in breast cancer cells. Taken together, our results suggest that XIST is down-regulated in breast cancer and suppresses breast cancer cell growth, migration, and invasion via the miR-155/CDX1 axis.

Identification of lung adenocarcinoma specific dysregulated genes with diagnostic and prognostic value across 27 TCGA cancer types

As the most common histologic subtype of lung cancer, lung adenocarcinoma (LUAD) contributes to a majority of cancer-related deaths worldwide annually. In order to find specific biomarkers of LUAD that are able to distinguish LUAD from other types of cancer so as to improve the early diagnostic and prognostic power in LUAD, we analyzed 10098 tumor tissue samples across 27 TCGA cancer types and identified 112 specific expressed genes in LUAD. Meantime, 8240 LUAD dysregulated genes in tumor and normal samples were identified. Combining with the results of specific expressed genes and dysregulated genes in LUAD, we found there were 70 specific dysregulated genes in LUAD (LUAD-SDGs). Then ROC curve revealed six LUAD-SDGs that may be of strong diagnostic value to predict the existence of cancer (area under curve[AUC] > 95%). Kaplan-Meier survival analysis was performed to identify 6 LUAD-SDGs associated with patients' prognosis (P-values < 0.001). Multivariate Cox proportional hazards regression was employed to demonstrate that the six LUAD-SDGs were independent prognostic factors. Then, we used the six overall survival (OS)-related LUAD-SDGs constructing a six-gene signature. Multivariate Cox regression analysis suggested that the six-gene signature was an independent prognostic factor of other clinical variables (hazard ratio [HR] = 1.5098, 95%CI = 1.2996-1.7538, P < 0.0001). Based on our findings, we first presented the LUAD-SDGs for LUAD diagnosis and prognosis. Our results may provide efficient biomarkers to clinical diagnostic and prognostic evaluation in LUAD.

Dynamic Changes in the Splenic Transcriptome of Chickens during the Early Infection and Progress of Marek's Disease

Gallid alphaherpesvirus 2 (GaHV2) is an oncogenic avian herpesvirus inducing Marek’s disease (MD) and rapid-onset T-cell lymphomas. To reveal molecular events in MD pathogenesis and tumorigenesis, the dynamic splenic transcriptome of GaHV2-infected chickens during early infection and pathogenic phases has been determined utilizing RNA-seq. Based on the significant differentially expressed genes (DEGs), analysis of gene ontology, KEGG pathway and protein-protein interaction network has demonstrated that the molecular events happening during GaHV2 infection are highly relevant to the disease course. In the ‘Cornell Model’ description of MD, innate immune responses and inflammatory responses were established at early cytolytic phase but persisted until lymphoma formation. Humoral immunity in contrast began to play a role firstly in the intestinal system and started at late cytolytic phase. Neurological damage caused by GaHV2 is first seen in early cytolytic phase and is then sustained throughout the following phases over a long time period. During the proliferative phase many pathways associated with transcription and/or translation were significantly enriched, reflecting the cell transformation and lymphoma formation. Our work provides an overall view of host responses to GaHV2 infection and offers a meaningful basis for further studies of MD biology.

Identification of prognostic genes through expression differentiation during metastatic process in lung adenocarcinoma

Cancer is a highly complicated biological process due to large scale heterogeneity. Identification of differentially expressed genes between normal and cancer samples is widely utilized in the discovery of prognostic factors. In this study, based on RNA sequencing data of lung adenocarcinoma, we focused on the expression differentiation during confined (with neither lymph node invasion nor distant metastasis) primary tumors and lymphnode (with only lymph node invasion but not distant metastasis) primary tumors. The result indicated that differentially expressed genes during confined-lymphnode transition were more closely related to patient’s overall survival comparing with those identified from normal-cancer transition. With the aid of public curated biological network, we successfully retrieved the biggest connected module composed of 135 genes, of which the expression was significantly associated with patient’s overall survival, confirmed by 9 independent microarray datasets.

Prognostic and predictive biomarkers post curative intent therapy

Large-scale screening trials have demonstrated that early diagnosis of lung cancer results in a significant reduction in lung cancer mortality. Despite improvements in detecting more lung cancers at early stages, the 5-year survival rates of lung cancers diagnosed before widespread disease is only 30-50%. High rates of recurrence, despite early diagnosis, suggest the need to improve treatment strategies based on the likelihood of recurrence in patient subsets, as well as explore the role of predictive markers for therapy selection in the adjuvant setting. In the era of personalized medicine, there have been a wide array of molecular alterations and signatures studied for their potential prognostic and predictive utility, however most have failed to translate into clinical tools. This review will discuss progress made in clinical management of lung cancer, and recent progress in the development of patient selection tools for the refinement of early stage lung cancers.

Clinical Significance and Effect of lncRNA HOXA11-AS in NSCLC: A Study Based on Bioinformatics, In Vitro and in Vivo Verification

HOXA11 antisense RNA (HOXA11-AS) has been shown to be involved in tumorigenesis and development of different cancers. However, the role of HOXA11-AS in non-small cell lung cancer (NSCLC) remains unclear. In this study, we firstly explored and confirmed the expression of HOXA11-AS in NSCLC tissues and cells. Cytometry, CCK-8, cell scratch, migration, Matrigel invasion and flow cytometry assays were performed to determine the biological impact of HOXA11-AS in vitro. Furthermore, a chick embryo chorioallantoic membrane (CAM) model of NSCLC was constructed to explore the effect of HOXA11-AS on tumorigenicity and angiogenesis in vivo. Additionally, bioinformatics analyses were performed to investigate the prospective pathways of HOXA11-AS co-expressed genes. As results, HOXA11-AS was markedly highly expressed in NSCLC tissues and cells. Furthermore, the proliferation, migration, invasion, tumorigenic and angiogenic ability of NSCLC cells were all inhibited and apoptosis was induced after HOXA11-AS knock-down. HOXA11-AS RNAi also led to cell cycle arrest on G0/G1 or G2/M phase. In addition, the non-small cell lung cancer pathway might be involved in regulating the co-expressed genes of HOXA11-AS in NSCLC. These results indicate that HOXA11-AS plays pivotal roles in NSCLC and it can become a novel therapeutic direction for treating NSCLC.