The online version of this article (doi:10.1186/s12943-017-0666-z) contains supplementary material, which is available to authorized users.
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.
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.
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.
LncRIndiv can successfully detect DE lncRNAs in individuals and be applied to identify prognostic signature for LUAD patients.
Additional file 1: Table S1. Information of probes, Ensembl ID/RefSeq ID and symbol for each lncRNA annotated from Affymetrix Human Genome U133 Plus 2.0 Array (HG-U133 Plus 2.0). (XLSX 151 kb)12943_2017_666_MOESM1_ESM.xlsx
Additional file 2: Table S2. The LUAD datasets used for application of LncRIndiv. Table S3. The paired normal-cancer LUAD sample data used for evaluating the performance of LncRIndiv. Table S4. The datasets of stage I and II LUAD patients without adjuvant therapy. (DOC 95 kb)12943_2017_666_MOESM2_ESM.doc
Additional file 3: Table S5. Detail information of differentially expressed lncRNAs identified based on microarray data. (XLSX 275 kb)12943_2017_666_MOESM3_ESM.xlsx
Additional file 4: Table S6. Detail information of differentially expressed lncRNAs identified based on RNA-Seq data. (XLSX 230 kb)12943_2017_666_MOESM4_ESM.xlsx
Additional file 5: Figure S1. The hetamap of differentially expressed (DE) lncRNAs for microarray data (A) and sequencing data (B), respectively. Figure S2. Venn diagram to show the overlapped differentially expressed lncRNAs identified by LncRIndiv and RankComp using microarray data (A) and sequencing data (B). Figure S3. Kaplan-Meier estimates the overall survival in the training dataset and two independent validation datasets based on the differential expression of (A) C1orf132 and (B) TMPO-AS1, respectively. Figure S4. Cell cycle pathway annotated with differentially expressed genes. Figure S5. Cell adhesion molecules pathway annotated with differentially expressed genes. Figure S6. Sub-network of cell adhesion molecules pathway regulated by C1orf132. Figure S7. Expression levels of lncRNAs in the pair-wise LUAD patients for (A) LINC00341 and (B) AC005083.1. (DOC 3470 kb)12943_2017_666_MOESM5_ESM.doc
Additional file 6: Table S7. The consistency score under top 3, 5 and 7 reversal pairs in pair-wise datasets. Table S8. Comparison of LncRIndiv and RankComp methods using simulation data. Table S9. Information of lncRNAs with significant consistence between differential expression status and copy number alteration. Table S10. Information of genes co-expressed with TMPO-AS1 and C1orf132 in cell cycle pathway in GSE50081. Table S11. Differentially expressed lncRNAs identified by LncRIndiv method supported by experimental evidence. Table S12. Information of competing endogenous RNA and miRNA with the lncRNA C1orf132. Table S13.. Sensitivity, specificity, and F-score in simulated data under different scenarios. (DOC 257 kb)12943_2017_666_MOESM6_ESM.doc
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