Combined analysis of RNA‐sequence and microarray data reveals effective metabolism‐based prognostic signature for neuroblastoma

Meng, Xinyao; Feng, Chenzhao; Fang, Erhu; Feng, Jiexiong; Zhao, Xiang

doi:10.1111/jcmm.15650

Cited by 7 publications

(4 citation statements)

References 50 publications

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“…Pearson correlation analysis was carried out to screen m6A-related lncRNAs, and 1573 m6A-related lncRNAs were identified with the criteria of |Pearson R| > 0.3 and p < 0.001 ( 11 , 12 ).…”

Section: Methodsmentioning

confidence: 99%

m6A-related lncRNAs are potential biomarkers for the prognosis of COAD patients

Shao

et al. 2022

Front. Oncol.

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BackgroundColon adenocarcinoma (COAD) is the most common subtype of colon cancer. However, the 5-year survival rate of COAD patients remains unsatisfactory. N6-methyladenosine (m6A) and long noncoding RNAs (lncRNAs) play essential roles in the occurrence and development of COAD. Herein, we are committed to establish and validate a prognostic m6A-related lncRNA signature.MethodsWe obtained m6A-related lncRNAs by coexpression. The m6A-related lncRNA risk signature (m6ALncSig) was developed via univariate, LASSO, and multivariate Cox regression analyses. Kaplan-Meier (KM) survival curves, gene set enrichment analysis (GSEA), and nomogram generation were conducted to assess m6ALncSig. In addition, the potential immunotherapeutic signatures were also discussed. Real-time PCR and CCK8 analysis were performed to evaluate the expression and functions of lncRNA UBA6-AS1, which was selected.ResultsThe risk signature comprising 14 m6A-related lncRNAs (m6ALncSig) was established, which possessed a superior predictive ability of prognosis. Meanwhile, m6ALncSig was linked to immune cell infiltration. The level of UBA6-AS1 expression was validated in 17 pairs of COAD samples. In cell function experiments, UBA6-AS1 knockdown attenuated cell proliferation capacity.ConclusionsCollectively, m6ALncSig could serve as an independent predictive factor for COAD and accurately estimate the outcome for COAD patients. Importantly, UBA6-AS1 was first identified as an oncogene in COAD.

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“…Pearson correlation analysis was carried out to screen m6A-related lncRNAs, and 1573 m6A-related lncRNAs were identified with the criteria of |Pearson R| > 0.3 and p < 0.001 ( 11 , 12 ).…”

Section: Methodsmentioning

confidence: 99%

m6A-related lncRNAs are potential biomarkers for the prognosis of COAD patients

Shao

et al. 2022

Front. Oncol.

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“…Recently, many studies have demonstrated that lncRNAs are not “transcriptional noise”, as originally thought, but that they play roles as oncogenes or tumour suppressor genes [ 7 ] and act as competing endogenous RNAs (ceRNAs) to regulate key target genes in tumour development [ 19 , 20 ]. Previous studies have identified a variety of lncRNAs involved in tumour metabolism, autophagy, and immune-related processes [ 21 – 24 ], while many researchers have developed various predictive models based on prognosis-related lncRNAs for application [ 25 – 27 ]. We are very interested in pathways related to tumour metabolism and would like to develop a predictive model for intrahepatic cholangiocarcinoma.…”

Section: Discussionmentioning

confidence: 99%

A metabolism-related 4-lncRNA prognostic signature and corresponding mechanisms in intrahepatic cholangiocarcinoma

Zou

Wang

et al. 2021

BMC Cancer

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Background Long non-coding RNA (lncRNA) plays a critical role in the malignant progression of intrahepatic cholangiocarcinoma (iCCA). This study aimed to establish a 4-lncRNA prognostic signature and explore corresponding potential mechanisms in patients with iCCA. Methods The original lncRNA-seq and clinical data were collected from the TCGA and GEO databases. Overlapping and differentially expressed lncRNAs (DE-lncRNAs) were further identified from transcriptome data. Univariate regression analysis was performed to screen survival-related DE-lncRNAs, which were further selected to develop an optimal signature to predict prognosis using multivariate regression analysis. The Kaplan-Meier survival curve visualized the discrimination of the signature on overall survival (OS). The area under the curve (AUC) and C-index were used to verify the predictive accuracy of the signature. Combined with clinical data, multivariate survival analysis was used to reveal the independent predictive capability of the signature. In addition, a prognostic nomogram was constructed. Finally, the common target genes of 4 lncRNAs were predicted by the co-expression method, and the corresponding functions were annotated by GO and KEGG enrichment analysis. Gene set enrichment analysis (GSEA) was also performed to explore the potential mechanism of the signature. Quantitative real-time PCR was used to evaluated the expression of 4 lncRNAs in an independent cohort. Results We identified and constructed a 4-lncRNA (AC138430.1, AGAP2-AS1, AP001783.1, and AP005233.2) prognostic signature using regression analysis, and it had the capability to independently predict prognosis. The AUCs were 0.952, 0.909, and 0.882 at 1, 2, and 3 years, respectively, and the C-index was 0.808, which showed good predictive capability. Subsequently, combined with clinical data, we constructed a nomogram with good clinical application. Finally, 252 target genes of all four lncRNAs were identified by the co-expression method, and functional enrichment analysis showed that the signature was strongly correlated with metabolism-related mechanisms in tumourigenesis. The same results were also validated via GSEA. Conclusion We demonstrated that a metabolism-related 4-lncRNA prognostic signature could be a novel biomarker and deeply explored the target genes and potential mechanism. This study will provide a promising therapeutic strategy for patients with intrahepatic cholangiocarcinoma.

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“…For example, both Peng et al and Sinkala et al conducted pan-cancer research to characterize tumor subtypes based on the expression patterns of metabolic pathway-related genes and showed distinct survival among the subtypes [ 13 , 14 ]. Moreover, metabolic gene set-based prognostic signatures have been proposed in head and neck carcinoma [ 15 , 16 ] and neuroblastoma [ 17 ]. However, studies focusing on subtype characterization and risk signatures based on metabolism-related genes in CRC remain limited.…”

Section: Introductionmentioning

confidence: 99%

Molecular subtype identification and prognosis stratification by a metabolism-related gene expression signature in colorectal cancer

et al. 2021

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Background Metabolic reprograming have been associated with cancer occurrence and progression within the tumor immune microenvironment. However, the prognostic potential of metabolism-related genes in colorectal cancer (CRC) has not been comprehensively studied. Here, we investigated metabolic transcript-related CRC subtypes and relevant immune landscapes, and developed a metabolic risk score (MRS) for survival prediction. Methods Metabolism-related genes were collected from the Molecular Signatures Database and metabolic subtypes were identified using an unsupervised clustering algorithm based on the expression profiles of survival-related metabolic genes in GSE39582. The ssGSEA and ESTIMATE methods were applied to estimate the immune infiltration among subtypes. The MRS model was developed using LASSO Cox regression in the GSE39582 dataset and independently validated in the TCGA CRC and GSE17537 datasets. Results We identified two metabolism-related subtypes (cluster-A and cluster-B) of CRC based on the expression profiles of 539 survival-related metabolic genes with distinct immune profiles and notably different prognoses. The cluster-B subtype had a shorter OS and RFS than the cluster-A subtype. Eighteen metabolism-related genes that were mostly involved in lipid metabolism pathways were used to build the MRS in GSE39582. Patients with higher MRS had worse prognosis than those with lower MRS (HR 3.45, P < 0.001). The prognostic role of MRS was validated in the TCGA CRC (HR 2.12, P = 0.00017) and GSE17537 datasets (HR 2.67, P = 0.039). Time-dependent receiver operating characteristic curve and stratified analyses revealed the robust predictive ability of the MRS in each dataset. Multivariate Cox regression analysis indicted that the MRS could predict OS independent of TNM stage and age. Conclusions Our study provides novel insight into metabolic heterogeneity and its relationship with immune landscape in CRC. The MRS was identified as a robust prognostic marker and may facilitate individualized therapy for CRC patients.

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Combined analysis of RNA‐sequence and microarray data reveals effective metabolism‐based prognostic signature for neuroblastoma

Cited by 7 publications

References 50 publications

m6A-related lncRNAs are potential biomarkers for the prognosis of COAD patients

m6A-related lncRNAs are potential biomarkers for the prognosis of COAD patients

A metabolism-related 4-lncRNA prognostic signature and corresponding mechanisms in intrahepatic cholangiocarcinoma

Molecular subtype identification and prognosis stratification by a metabolism-related gene expression signature in colorectal cancer

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