Dataset Information

A Greedy Algorithm-Based Stem Cell LncRNA Signature Identifies a Novel Subgroup of Lung Adenocarcinoma Patients With Poor Prognosis.

ABSTRACT: Cancer stem cells play an essential role in therapy response and aggressiveness of various cancers, including lung adenocarcinoma (LUAD). Interestingly it also shares many features of embryonic stem cells (ESCs). Recently, long non-coding RNAs (lncRNAs) have emerged as a critical regulator of cell physiology. Here, we used expression data of ESCs, LUAD, and normal lung to identify 198 long non-coding hESC-associated lncRNAs (hESC-lncRNAs). Intriguingly, K-means clustering of hESC-associated lncRNAs identified a subgroup of LUAD patients [undifferentiated LUAD (uLUAD)] with high stem cell-like characteristic, decreased differentiation genes expression, and poor survival. We also observed that the uLUAD patients had overexpression of proteins associated with cell proliferation. Interestingly, uLUAD patients were highly enriched with the stemness-related gene sets, and had higher mutation load. A notable result observed was high infiltration of T cells and a higher level of neopeptides in uLUAD patients, making these patients an optimal candidate for immunotherapy. Further, feature selection using greedy algorithm identified 17-hESC-lncRNAs signature, which showed significant consistency with 198 hESC-lncRNAs-based classification, and identified a group of patients with high stem cell-like characteristic in the 10 most common cancer types and CCLE cell lines. These results suggest the conventional role of hESC-lncRNAs in stem cell biology. In summary, we identified a novel subgroup of LUAD patients (uLUAD) using a set of hESC-lncRNAs. The uLUAD patients had high stem cell-like characteristic and reduced survival rate and may be referred for immunotherapy. Furthermore, our analysis also showed the importance of lncRNAs in cancer and cancer stem cells.

SUBMITTER: Khadirnaikar S

PROVIDER: S-EPMC7431877 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Similar Datasets

Project description:BackgroundColon adenocarcinoma (COAD) is the most common colon cancer exhibiting high mortality. Due to their association with cancer progression, long noncoding RNAs (lncRNAs) are now being used as prognostic biomarkers. In the present study, we used relevant clinical information and expression profiles of lncRNAs originating from The Cancer Genome Atlas database, aiming to construct a prognostic lncRNA signature to estimate the prognosis of patients.MethodsThe samples were randomly spilt into training and validation cohorts. In the training cohort, prognosis-related lncRNAs were selected from differentially expressed lncRNAs using the univariate Cox analysis. Furthermore, the least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis were employed for identifying prognostic lncRNAs. The prognostic signature was constructed by these lncRNAs.ResultsThe prognostic model was able to calculate each COAD patient's risk score and split the patients into groups of low and high risks. Compared to the low-risk group, the high-risk group had significant poor prognosis. Next, the prognostic signature was validated in the validation, as well as all cohorts. The receiver operating characteristic (ROC) curve and c-index were determined in all cohorts. Moreover, these prognostic lncRNA signatures were combined with clinicopathological risk factors to construct a nomogram for predicting the prognosis of COAD in the clinic. Finally, seven lncRNAs (CTC-273B12.10, AC009404.2, AC073283.7, RP11-167H9.4, AC007879.7, RP4-816N1.7, and RP11-400N13.2) were identified and validated by different cohorts. The Kyoto Encyclopedia of Genes and Genomes analysis of the mRNAs co-expressed with the seven prognostic lncRNAs suggested four significantly upregulated pathways, which were AGE-RAGE, focal adhesion, ECM-receptor interaction, and PI3K/Akt signaling pathways.ConclusionThus, our study verified that the seven lncRNAs mentioned can be used as biomarkers to predict the prognosis of COAD patients and design personalized treatments.

Project description:BackgroundPancreatic ductal adenocarcinoma (PDAC) is characterized by an important heterogeneity, reflected by different clinical outcomes and chemoresistance. During carcinogenesis, tumor cells display aberrant glycosylated structures, synthetized by deregulated glycosyltransferases, supporting the tumor progression. In this study, we aimed to determine whether PDAC could be stratified through their glycosyltransferase expression profiles better than the current binary classification (basal-like and classical) in order to improve detection of patients with poor prognosis.MethodsBioinformatic analysis of 169 glycosyltransferase RNA sequencing data were performed for 74 patient-derived xenografts (PDX) of resected and unresectable tumors. The Australian cohort of International Cancer Genome Consortium and the microarray dataset from Puleo patient's cohort were used as independent validation datasets.FindingsNew PDAC stratification based on glycosyltransferase expression profile allowed to distinguish different groups of patients with distinct clinical outcome (p-value = 0.007). A combination of 19 glycosyltransferases differentially expressed in PDX defined a glyco-signature, whose prognostic value was validated on datasets including resected whole tumor tissues. The glyco-signature was able to discriminate three clusters of PDAC patients on the validation cohorts, two clusters displaying a short overall survival compared to one cluster having a better prognosis. Both poor prognostic clusters having different glyco-profiles in Puleo patient's cohort were correlated with stroma activated or desmoplastic subtypes corresponding to distinct microenvironment features (p-value < 0.0001). Besides, differential expression and enrichment analyses revealed deregulated functional pathways specific to different clusters.InterpretationThis study identifies a glyco-signature relevant for a prognostic use, potentially applicable to resected and unresectable PDAC. Furthermore, it provides new potential therapeutic targets.FundingThis work was supported by INCa (Grants number 2018-078 and 2018-079), Fondation ARC (Grant number ARCPJA32020070002326), Cancéropôle PACA, DGOS (labelization SIRIC, Grant number 6038), Amidex Foundation and Ligue Nationale Contre le Cancer and by institutional fundings from INSERM and the Aix-Marseille Université.

Dataset Information

A Greedy Algorithm-Based Stem Cell LncRNA Signature Identifies a Novel Subgroup of Lung Adenocarcinoma Patients With Poor Prognosis.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets