Individualized detection of TMPRSS2-ERG fusion status in prostate cancer: a rank-based qualitative transcriptome signature.

BACKGROUND: TMPRSS2-ERG (T2E) fusion is highly related to aggressive clinical features in prostate cancer (PC), which guides individual therapy. However, current fusion prediction tools lacked enough accuracy and biomarkers were unable to be applied to individuals across different platforms due to their quantitative nature. This study aims to identify a transcriptome signature to detect the T2E fusion status of PC at the individual level. METHODS: Based on 272 high-throughput mRNA expression profiles from the Sboner dataset, we developed a rank-based algorithm to identify a qualitative signature to detect T2E fusion in PC. The signature was validated in 1223 samples from three external datasets (Setlur, Clarissa, and TCGA). RESULTS: A signature, composed of five mRNAs coupled to ERG (five ERG-mRNA pairs, 5-ERG-mRPs), was developed to distinguish T2E fusion status in PC. 5-ERG-mRPs reached 84.56% accuracy in Sboner dataset, which was verified in Setlur dataset (n = 455, accuracy = 82.20%) and Clarissa dataset (n = 118, accuracy = 81.36%). Besides, for 495 samples from TCGA, two subtypes classified by 5-ERG-mRPs showed a higher level of significance in various T2E fusion features than subtypes obtained through current fusion prediction tools, such as STAR-Fusion. CONCLUSIONS: Overall, 5-ERG-mRPs can robustly detect T2E fusion in PC at the individual level, which can be used on any gene measurement platform without specific normalization procedures. Hence, 5-ERG-mRPs may serve as an auxiliary tool for PC patient management.

Synthetic viability induces resistance to immune checkpoint inhibitors in cancer cells.

BACKGROUND: Immune checkpoint inhibitors (ICI) have revolutionized the treatment for multiple cancers. However, most of patients encounter resistance. Synthetic viability (SV) between genes could induce resistance. In this study, we established SV signature to predict the efficacy of ICI treatment for melanoma. METHODS: We collected features and predicted SV gene pairs by random forest classifier. This work prioritized SV gene pairs based on CRISPR/Cas9 screens. SV gene pairs signature were constructed to predict the response to ICI for melanoma patients. RESULTS: This study predicted robust SV gene pairs based on 14 features. Filtered by CRISPR/Cas9 screens, we identified 1,861 SV gene pairs, which were also related with prognosis across multiple cancer types. Next, we constructed the six SV pairs signature to predict resistance to ICI for melanoma patients. This study applied the six SV pairs signature to divide melanoma patients into high-risk and low-risk. High-risk melanoma patients were associated with worse response after ICI treatment. Immune landscape analysis revealed that high-risk melanoma patients had lower natural killer cells and CD8+ T cells infiltration. CONCLUSIONS: In summary, the 14 features classifier accurately predicted robust SV gene pairs for cancer. The six SV pairs signature could predict resistance to ICI.

An absolute human stemness index associated with oncogenic dedifferentiation.

The progression of cancer is accompanied by the acquisition of stemness features. Many stemness evaluation methods based on transcriptional profiles have been presented to reveal the relationship between stemness and cancer. However, instead of absolute stemness index values-the values with certain range-these methods gave the values without range, which makes them unable to intuitively evaluate the stemness. Besides, these indices were based on the absolute expression values of genes, which were found to be seriously influenced by batch effects and the composition of samples in the dataset. Recently, we have showed that the signatures based on the relative expression orderings (REOs) of gene pairs within a sample were highly robust against these factors, which makes that the REO-based signatures have been stably applied in the evaluations of the continuous scores with certain range. Here, we provided an absolute REO-based stemness index to evaluate the stemness. We found that this stemness index had higher correlation with the culture time of the differentiated stem cells than the previous stemness index. When applied to the cancer and normal tissue samples, the stemness index showed its significant difference between cancers and normal tissues and its ability to reveal the intratumor heterogeneity at stemness level. Importantly, higher stemness index was associated with poorer prognosis and greater oncogenic dedifferentiation reflected by histological grade. All results showed the capability of the REO-based stemness index to assist the assignment of tumor grade and its potential therapeutic and diagnostic implications.

Reference genome and annotation updates lead to contradictory prognostic predictions in gene expression signatures: a case study of resected stage I lung adenocarcinoma.

RNA-sequencing enables accurate and low-cost transcriptome-wide detection. However, expression estimates vary as reference genomes and gene annotations are updated, confounding existing expression-based prognostic signatures. Herein, prognostic 9-gene pair signature (GPS) was applied to 197 patients with stage I lung adenocarcinoma derived from previous and latest data from The Cancer Genome Atlas (TCGA) processed with different reference genomes and annotations. For 9-GPS, 6.6% of patients exhibited discordant risk classifications between the two TCGA versions. Similar results were observed for other prognostic signatures, including IRGPI, 15-gene and ORACLE. We found that conflicting annotations for gene length and overlap were the major cause of their discordant risk classification. Therefore, we constructed a prognostic 40-GPS based on stable genes across GENCODE v20-v30 and validated it using public data of 471 stage I samples (log-rank P < 0.0010). Risk classification was still stable in RNA-sequencing data processed with the newest GENCODE v32 versus GENCODE v20-v30. Specifically, 40-GPS could predict survival for 30 stage I samples with formalin-fixed paraffin-embedded tissues (log-rank P = 0.0177). In conclusion, this method overcomes the vulnerability of existing prognostic signatures due to reference genome and annotation updates. 40-GPS may offer individualized clinical applications due to its prognostic accuracy and classification stability.

Upregulation of CXCL1 and LY9 contributes to BRCAness in ovarian cancer and mediates response to PARPi and immune checkpoint blockade.

BACKGROUND: Mutations in BRCA1 or BRCA2 (BRCA1/2) cause homologous recombination deficiency (HRD). Ovarian cancer (OvCa) patients harbouring HRD beyond BRCA1/2 mutation result in a state referred to as "BRCAness". OvCa with BRCAness could benefit from PARP inhibitors. This study aims to identify a signature to detect the BRCAness population at the transcriptome level. METHODS: We used a rank-based algorithm to develop a qualitative BRCAness signature for OvCa. Upregulation of CXCL1 with downregulation of SV2A and upregulation of LY9 with downregulation of CHRNB3 were constructed as the BRCAness signature (2 gene pairs, 2-GPS) for OvCa. RESULTS: OvCa samples that were classified as BRCAness by 2-GPS showed improved overall survival, progression-free survival and exhibited increased multi-omics alterations in homologous recombination genes and enhanced sensitivity to immune checkpoint blockade. BRCAness cells were sensitive to PARP inhibitors. By biological experiments, we validated SKOV3 cells and patients with HRD exhibited higher expression of CXCL1 than SV2A and higher expression of LY9 than CHRNB3 at mRNA level. Both SKOV3 and A2780 with HRD were sensitive to mitomycin C, cisplatin and olaparib. CONCLUSIONS: In conclusion, 2-GPS could robustly predict BRCAness OvCa at the individual level and extend the population who may benefit from PARP inhibitors.

A genetic map of the chromatin regulators to drug response in cancer cells.

BACKGROUND: Diverse drug vulnerabilities owing to the Chromatin regulators (CRs) genetic interaction across various cancers, but the identification of CRs genetic interaction remains challenging. METHODS: In order to provide a global view of the CRs genetic interaction in cancer cells, we developed a method to identify potential drug response-related CRs genetic interactions for specific cancer types by integrating the screen of CRISPR-Cas9 and pharmacogenomic response datasets. RESULTS: Totally, 625 drug response-related CRs synthetic lethality (CSL) interactions and 288 CRs synthetic viability (CSV) interactions were detected. Systematically network analysis presented CRs genetic interactions have biological function relationship. Furthermore, we validated CRs genetic interactions induce multiple omics deregulation in The Cancer Genome Atlas. We revealed the colon adenocarcinoma patients (COAD) with mutations of a CRs set (EP300, MSH6, NSD2 and TRRAP) mediate a better survival with low expression of MAP2 and could benefit from taxnes. While the COAD patients carrying at least one of the CSV interactions in Vorinostat CSV module confer a poor prognosis and may be resistant to Vorinostat treatment. CONCLUSIONS: The CRs genetic interaction map provides a rich resource to investigate cancer-associated CRs genetic interaction and proposes a powerful strategy of biomarker discovery to guide the rational use of agents in cancer therapy.

A rank-based algorithm of differential expression analysis for small cell line data with statistical control.

To detect differentially expressed genes (DEGs) in small-scale cell line experiments, usually with only two or three technical replicates for each state, the commonly used statistical methods such as significance analysis of microarrays (SAM), limma and RankProd (RP) lack statistical power, while the fold change method lacks any statistical control. In this study, we demonstrated that the within-sample relative expression orderings (REOs) of gene pairs were highly stable among technical replicates of a cell line but often widely disrupted after certain treatments such like gene knockdown, gene transfection and drug treatment. Based on this finding, we customized the RankComp algorithm, previously designed for individualized differential expression analysis through REO comparison, to identify DEGs with certain statistical control for small-scale cell line data. In both simulated and real data, the new algorithm, named CellComp, exhibited high precision with much higher sensitivity than the original RankComp, SAM, limma and RP methods. Therefore, CellComp provides an efficient tool for analyzing small-scale cell line data.

Link synthetic lethality to drug sensitivity of cancer cells.

Synthetic lethal (SL) interactions occur when alterations in two genes lead to cell death but alteration in only one of them is not lethal. SL interactions provide a new strategy for molecular-targeted cancer therapy. Currently, there are few drugs targeting SL interactions that entered into clinical trials. Therefore, it is necessary to investigate the link between SL interactions and drug sensitivity of cancer cells systematically for drug development purpose. We identified SL interactions by integrating the high-throughput data from The Cancer Genome Atlas, small hairpin RNA data and genetic interactions of yeast. By integrating SL interactions from other studies, we tested whether the SL pairs that consist of drug target genes and the genes with genomic alterations are related with drug sensitivity of cancer cells. We found that only 6.26%∼34.61% of SL interactions showed the expected significant drug sensitivity using the pooled cancer cell line data from different tissues, but the proportion increased significantly to approximately 90% using the cancer cell line data for each specific tissue. From an independent pharmacogenomics data of 41 breast cancer cell lines, we found three SL interactions (ABL1-IFI16, ABL1-SLC50A1 and ABL1-SYT11) showed significantly better prognosis for the patients with both genes being altered than the patients with only one gene being altered, which partially supports the SL effect between the gene pairs. Our study not only provides a new way for unraveling the complex mechanisms of drug sensitivity but also suggests numerous potentially important drug targets for cancer therapy.

Glucose Homeostasis Is Dysregulated in Ducks Infected with Duck Hepatitis B Virus.

INTRODUCTION: The association between hepatitis B virus (HBV) infection and the development of diabetes remains controversial. This study examined the effect of HBV infection on glucose homeostasis using a duck HBV (DHBV) model. METHODS: Plasma DHBV DNA was detected by quantitative polymerase chain reaction (PCR). Tissue infection of DHBV was determined by detecting DHBV covalently closed circular DNA (cccDNA) with a method of rolling circle amplification combined with cross-gap PCR, and verified by fluorescence in situ hybridization assay. An intravenous injection glucose tolerance test (GTT) was used to analyze the effect of DHBV infection on glucose tolerance. RESULTS: Of the finally included 97 domestic ducks, 53 (54.6%) were congenitally infected by DHBV. The positive rate of DHBV cccDNA in the liver, kidney, pancreas, and skeletal muscle of the infected ducks was 100, 75.5, 67.9, and 47.2%, respectively. The DHBV-infected ducks had higher blood glucose levels at 15 and 30 min post-load glucose (p < 0.01 and p < 0.001, respectively) in the GTT, much more individuals with greater glucose area under curve (p < 0.01), and a 57% impaired glucose tolerance (IGT) rate, as compared with noninfected controls. In addition, the subgroups of the infected ducks with DHBV cccDNA positive in skeletal muscle maintained the higher blood glucose level up to 2 h post-load glucose during the GTT and had a 76% IGT rate. CONCLUSION: These results suggest that DHBV intrahepatic and extrahepatic infection impairs glucose tolerance, and thus evidence the association of DHBV infection with the dysregulation of glucose metabolism.

Individualized analysis of differentially expressed miRNAs with application to the identification of miRNAs deregulated commonly in lung cancer tissues.

Identifying differentially expressed microRNAs (DE miRNAs) between cancer samples and normal controls is a common way to investigate carcinogenesis mechanisms. However, for a DE miRNA detected at the population-level, we do not know whether it is DE in a particular cancer sample. Here, based on the finding that the within-sample relative expression orderings of miRNA pairs are highly stable in a particular type of normal tissues but widely disrupted in the corresponding cancer tissues, we proposed a method, called RankMiRNA, to identify DE miRNAs in each cancer tissue compared with its own normal state. Evaluated with pair-matched miRNA expression profiles of cancer tissues and adjacent normal tissues for lung and liver cancers, RankMiRNA exhibited excellent performance. Finally, we exemplified an application of the individual-level differential expression analysis by finding miRNAs DE in at least 90% lung cancer tissues, defined as common DE miRNAs of lung cancer. After identifying DE miRNAs for each of 991 lung cancer samples from The Cancer Genome Atlas with RankMiRNA, we found that hsa-mir-210 was upregulated, while hsa-mir-490 and hsa-mir-486 were downregulated in > 90% of the 991 lung cancer samples. These common DE miRNAs were validated in independent pair-matched samples of cancer tissues and adjacent normal tissues measured with different platforms. In conclusion, RankMiRNA provides us a novel tool to find common and subtype-specific miRNAs for a type of cancer, allowing us to study cancer mechanisms in a novel way.

A landscape of synthetic viable interactions in cancer.

Synthetic viability, which is defined as the combination of gene alterations that can rescue the lethal effects of a single gene alteration, may represent a mechanism by which cancer cells resist targeted drugs. Approaches to detect synthetic viable (SV) interactions in cancer genome to investigate drug resistance are still scarce. Here, we present a computational method to detect synthetic viability-induced drug resistance (SVDR) by integrating the multidimensional data sets, including copy number alteration, whole-exome mutation, expression profile and clinical data. SVDR comprehensively characterized the landscape of SV interactions across 8580 tumors in 32 cancer types by integrating The Cancer Genome Atlas data, small hairpin RNA-based functional experimental data and yeast genetic interaction data. We revealed that the SV interactions are favorable to cells and can predict clinical prognosis for cancer patients, which were robustly observed in an independent data set. By integrating the cancer pharmacogenomics data sets from Cancer Cell Line Encyclopedia (CCLE) and Broad Cancer Therapeutics Response Portal, we have demonstrated that SVDR enables drug resistance prediction and exhibits high reliability between two databases. To our knowledge, SVDR is the first genome-scale data-driven approach for the identification of SV interactions related to drug resistance in cancer cells. This data-driven approach lays the foundation for identifying the genomic markers to predict drug resistance and successfully infers the potential drug combination for anti-cancer therapy.

Identification of a small mutation panel of coding sequences to predict the efficacy of immunotherapy for lung adenocarcinoma.

BACKGROUND: Immune checkpoint inhibitors are effective in some cases of lung adenocarcinoma (LUAD). Whole-exome sequencing has revealed that the tumour mutation burden (TMB) is associated with clinical benefits among patients from immune checkpoint inhibitors. Several commercial mutation panels have been developed for estimating the TMB regardless of the cancer type. However, different cancer types have different mutational landscapes; hence, this study aimed to develop a small cancer-type-specific mutation panel for high-accuracy estimation of the TMB of LUAD patients. METHODS: We developed a small cancer-type-specific mutation panel based on coding sequences (CDSs) rather than genes, for LUAD patients. Using somatic CDSs mutation data from 486 LUAD patients in The Cancer Genome Atlas (TCGA) database, we pre-selected a set of CDSs with mutation states significantly correlated with the TMB, from which we selected a CDS mutation panel with a panel-score most significantly correlated with the TMB, using a genetic algorithm. RESULTS: A mutation panel containing 106 CDSs of 100 genes with only 0.34 Mb was developed, whose length was much shorter than current commercial mutation panels of 0.80-0.92 Mb. The correlation of this panel with the TMB was validated in two independent LUAD datasets with progression-free survival data for patients treated with nivolumab plus ipilimumab and pembrolizumab immunotherapies, respectively. In both test datasets, survival analyses revealed that patients with a high TMB predicted via the 106-CDS mutation panel with a cut-point of 6.20 mutations per megabase, median panel score in the training dataset, had a significantly longer progression-free survival than those with a low predicted TMB (log-rank p = 0.0018, HR = 3.35, 95% CI 1.51-7.42; log-rank p = 0.0020, HR = 5.06, 95% CI 1.63-15.69). This small panel better predicted the efficacy of immunotherapy than current commercial mutation panels. CONCLUSIONS: The small-CDS mutation panel of only 0.34 Mb is superior to current commercial mutation panels and can better predict the efficacy of immunotherapy for LUAD patients, and its low cost and time-intensiveness make it more suitable for clinical applications.

A qualitative transcriptional signature for predicting microsatellite instability status of right-sided Colon Cancer.

BACKGROUND: Microsatellite instability (MSI) accounts for about 15% of colorectal cancer and is associated with prognosis. Today, MSI is usually detected by polymerase chain reaction amplification of specific microsatellite markers. However, the instability is identified by comparing the length of microsatellite repeats in tumor and normal samples. In this work, we developed a qualitative transcriptional signature to individually predict MSI status for right-sided colon cancer (RCC) based on tumor samples. RESULTS: Using RCC samples, based on the relative expression orderings (REOs) of gene pairs, we extracted a signature consisting of 10 gene pairs (10-GPS) to predict MSI status for RCC through a feature selection process. A sample is predicted as MSI when the gene expression orderings of at least 7 gene pairs vote for MSI; otherwise the microsatellite stability (MSS). The classification performance reached the largest F-score in the training dataset. This signature was verified in four independent datasets of RCCs with the F-scores of 1, 0.9630, 0.9412 and 0.8798, respectively. Additionally, the hierarchical clustering analyses and molecular features also supported the correctness of the reclassifications of the MSI status by 10-GPS. CONCLUSIONS: The qualitative transcriptional signature can be used to classify MSI status of RCC samples at the individualized level.

LncRNA PTAR promotes EMT and invasion-metastasis in serous ovarian cancer by competitively binding miR-101-3p to regulate ZEB1 expression.

BACKGROUND: Ovarian cancer (OvCa) is one of the most common malignant diseases of the female reproductive system in the world. The majority of OvCa is diagnosed with metastasis in the abdominal cavity. Epithelial-to-mesenchymal transition (EMT) plays a key role in tumor cell metastasis. However, it is still unclear whether long non-coding RNA (lncRNA) is implicated in EMT and influences cell invasion and metastasis in OvCa. RESULTS: In this study, using bioinformatcis analysis, we constructed a lncRNA-mediated competing endogenous RNA (ceRNA) network for mesenchymal OvCa and identified lncRNA AP000695.4, which we named pro-transition associated RNA (PTAR). PTAR was significantly up-regulated in the mesenchymal subtype samples compared with the epithelial subtype samples from the TCGA OvCa data sets. In addition, our study showed that PTAR expression was positively correlated with the expression level of ZEB1 in the mesenchymal OvCa samples. Meanwhile, we found that silencing miR-101 promoted cell migration, whereas the overexpression of miR-101 suppressed EMT and cell migration in OvCa cell lines through the regulation of ZEB1. Further analysis showed that enhanced expression of PTAR promoted EMT and metastasis through the regulation of miR-101, whereas silencing PTAR led to the attenuation of TGF-ß1-induced tumorigenicity in ovarian cancer cells. Mechanistically, we found that PTAR acted as a ceRNA of miR-101, as forced expression of PTAR reduced the expression and activity of miR-101. More importantly, the knockdown of PTAR reduced tumorigenicity and metastasis in vivo. CONCLUSIONS: Taken together, the results from our study highlight a role for the PTAR-miR-101-ZEB1 axis in OvCa, which offers novel strategies for the prevention of metastasis in OvCa.

Systematic analyses reveal long non-coding RNA (PTAF)-mediated promotion of EMT and invasion-metastasis in serous ovarian cancer.

BACKGROUND: A deeper mechanistic understanding of epithelial-to-mesenchymal transition (EMT) regulation is needed to improve current anti-metastasis strategies in ovarian cancer (OvCa). This study was designed to investigate the role of lncRNAs in EMT regulation during process of invasion-metastasis in serous OvCa to improve current anti-metastasis strategies for OvCa. METHODS: We systematically analyzes high-throughput gene expression profiles of both lncRNAs and protein-coding genes in OvCa samples with integrated epithelial (iE) subtype and integrated mesenchymal (iM) subtype labels. Mouse models, cytobiology, molecular biology assays and clinical samples were performed to elucidate the function and underlying mechanisms of lncRNA PTAF-mediated promotion of EMT and invasion-metastasis in serous OvCa. RESULTS: We constructed a lncRNA-mediated competing endogenous RNA (ceRNA) regulatory network that affects the expression of many EMT-related protein-coding genes in mesenchymal OvCa. Using a combination of in vitro and in vivo studies, we provided evidence that the lncRNA PTAF-miR-25-SNAI2 axis controlled EMT in OvCa. Our results revealed that up-regulated PTAF induced elevated SNAI2 expression by competitively binding to miR-25, which in turn promoted OvCa cell EMT and invasion. Moreover, we found that silencing of PTAF inhibited tumor progression and metastasis in an orthotopic mouse model of OvCa. We then observed a significant correlation between PTAF expression and EMT markers in OvCa patients. CONCLUSIONS: The lncRNA PTAF, a mediator of TGF-ß signaling, can predispose OvCa patients to metastases and may serve as a potential target for anti-metastatic therapies for mesenchymal OvCa patients.

Individualized identification of disease-associated pathways with disrupted coordination of gene expression.

Current pathway analysis approaches are primarily dedicated to capturing deregulated pathways at the population level and cannot provide patient-specific pathway deregulation information. In this article, the authors present a simple approach, called individPath, to detect pathways with significantly disrupted intra-pathway relative expression orderings for each disease sample compared with the stable, normal intra-pathway relative expression orderings pre-determined in previously accumulated normal samples. Through the analysis of multiple microarray data sets for lung and breast cancer, the authors demonstrate individPath's effectiveness for detecting cancer-associated pathways with disrupted relative expression orderings at the individual level and dissecting the heterogeneity of pathway deregulation among different patients. The portable use of this simple approach in clinical contexts is exemplified by the identification of prognostic intra-pathway gene pair signatures to predict overall survival of resected early-stage lung adenocarcinoma patients and signatures to predict relapse-free survival of estrogen receptor-positive breast cancer patients after tamoxifen treatment.

Critical limitations of prognostic signatures based on risk scores summarized from gene expression levels: a case study for resected stage I non-small-cell lung cancer.

Most of current gene expression signatures for cancer prognosis are based on risk scores, usually calculated as some summaries of expression levels of the signature genes, whose applications require presetting risk score thresholds and data normalization. In this study, we demonstrate the critical limitations of such type of signatures that the risk scores of samples will change greatly when they are normalized together with different samples, which would induce spurious risk classification and difficulty in clinical settings, and the risk scores of independent samples are incomparable if data normalization is not adopted. To overcome these limitations, we propose a rank-based method to extract a prognostic gene pair signature for overall survival of stage I non-small-cell lung cancer. The prognostic gene pair signature is verified in three integrated data sets detected by different laboratories with different microarray platforms. We conclude that, different from the type of signatures based on risk scores summarized from gene expression levels, the rank-based signatures could be robustly applied at the individualized level to independent clinical samples assessed in different laboratories.

Differential expression analysis at the individual level reveals a lncRNA prognostic signature for lung adenocarcinoma.

BACKGROUND: Deregulations of long non-coding RNAs (lncRNAs) have been implicated in cancer initiation and progression. Current methods can only capture differential expression of lncRNAs at the population level and ignore the heterogeneous expression of lncRNAs in individual patients. METHODS: We propose a method (LncRIndiv) to identify differentially expressed (DE) lncRNAs in individual cancer patients by exploiting the disrupted ordering of expression levels of lncRNAs in each disease sample in comparison with stable normal ordering. LncRIndiv was applied to lncRNA expression profiles of lung adenocarcinoma (LUAD). Based on the expression profile of LUAD individual-level DE lncRNAs, we used a forward selection procedure to identify prognostic signature for stage I-II LUAD patients without adjuvant therapy. RESULTS: In both simulated data and real pair-wise cancer and normal sample data, LncRIndiv method showed good performance. Based on the individual-level DE lncRNAs, we developed a robust prognostic signature consisting of two lncRNA (C1orf132 and TMPO-AS1) for stage I-II LUAD patients without adjuvant therapy (P = 3.06 × 10-6, log-rank test), which was confirmed in two independent datasets of GSE50081 (P = 1.82 × 10-2, log-rank test) and GSE31210 (P = 7.43 × 10-4, log-rank test) after adjusting other clinical factors such as smoking status and stages. Pathway analysis showed that TMPO-AS1 and C1orf132 could affect the prognosis of LUAD patients through regulating cell cycle and cell adhesion. CONCLUSIONS: LncRIndiv can successfully detect DE lncRNAs in individuals and be applied to identify prognostic signature for LUAD patients.

Individualized analysis reveals CpG sites with methylation aberrations in almost all lung adenocarcinoma tissues.

BACKGROUND: Due to the heterogeneity of cancer, identifying differentially methylated (DM) CpG sites between a set of cancer samples and a set of normal samples cannot tell us which patients have methylation aberrations in a particular DM CpG site. METHODS: We firstly showed that the relative methylation-level orderings (RMOs) of CpG sites within individual normal lung tissues are highly stable but widely disrupted in lung adenocarcinoma tissues. This finding provides the basis of using the RankComp algorithm, previously developed for differential gene expression analysis at the individual level, to identify DM CpG sites in each cancer tissue compared with its own normal state. Briefly, through comparing with the highly stable normal RMOs predetermined in a large collection of samples for normal lung tissues, the algorithm finds those CpG sites whose hyper- or hypo-methylations may lead to the disrupted RMOs of CpG site pairs within a disease sample based on Fisher's exact test. RESULTS: Evaluated in 59 lung adenocarcinoma tissues with paired adjacent normal tissues, RankComp reached an average precision of 94.26% for individual-level DM CpG sites. Then, after identifying DM CpG sites in each of the 539 lung adenocarcinoma samples from TCGA, we found five and 44 CpG sites hypermethylated and hypomethylated in above 90% of the disease samples, respectively. These findings were validated in 140 publicly available and eight additionally measured paired cancer-normal samples. Gene expression analysis revealed that four of the five genes, HOXA9, TAL1, ATP8A2, ENG and SPARCL1, each harboring one of the five frequently hypermethylated CpG sites within its promoters, were also frequently down-regulated in lung adenocarcinoma. CONCLUSIONS: The common DNA methylation aberrations in lung adenocarcinoma tissues may be important for lung adenocarcinoma diagnosis and therapy.