Development and validation of a novel N6-methyladenosine (m6A)-related multi- long non-coding RNA (lncRNA) prognostic signature in pancreatic adenocarcinoma

Abstract

Accumulating evidence has unveiled the pivotal roles of N6-methyladenosine (m6A) in pancreatic adenocarcinoma (PAAD). However, there are not many researches to predict the prognosis of PAAD using m6A-related long non-coding RNAs (lncRNAs). Raw data from The Cancer Genome Atlas (TCGA), International Cancer Genome Consortium (ICGC), and the Genotype-Tissue Expression project (GTEx) were utilized to comprehensively analyze the expression and prognostic performances of 145 m6Arelated lncRNAs in PAAD and to develop and validate a novel m6A-related multi-lncRNA prognostic signature (m6A-LPS) for PAAD patients. In total, 57 differentially expressed m6A-related lncRNAs with prognostic values were identified. Based on LASSO-Cox regression analysis, m6A-LPS was constructed and verified by using five-lncRNA expression profiles for TCGA and ICGC cohorts. PAAD patients were then divided into high-and low-risKBIE_A_1933868k subgroups with different clinical outcomes according to the median risk score; this was further verified by time-dependent receiver operating characteristic curves. Risk scores were significantly associated with clinical parameters such as histological grade and cancer status among PAAD patients. A nomogram consisting of risk score, grade, and cancer status was generated to predict the survival probability of PAAD patients, as also demonstrated by calibration curves. Discrepancies in cellular processes, signaling pathways, and immune status between the high-and low-risk subgroups were investigated by functional and singlesample gene set enrichment analyses. In conclusion, the novel m6A-LPS for PAAD patients was developed and validated, which might provide new insight into clinical decision-making and precision medicine.

An Inflammatory Response Related Gene Signature Associated with Survival Outcome and Gemcitabine Response in Patients with Pancreatic Ductal Adenocarcinoma

“…Previously, several valuable PDAC prognostic signatures have been constructed by using bioinformatics analysis. For example, signatures based on glycolysis, m6A, ferroptosis, immune, and extracellular vesicle predict 1-year OS for PDAC with AUC at 0.805, 0.736, 0.81, 0.755, and 0.640, respectively, which were similar to our study ( Xu et al, 2020 ; Chen et al, 2021 ; Song et al, 2021 ; Xu et al, 2021 ; Yuan et al, 2021 ). However, the number of patients included in the abovementioned studies ranged from 183 to 441 and were relatively low.…”

Identification of the Real Hub Gene and Construction of a Novel Prognostic Signature for Pancreatic Adenocarcinoma Based on the Weighted Gene Co-expression Network Analysis and Least Absolute Shrinkage and Selection Operator Algorithms

“…Four normal pancreatic samples obtained from the TCGA database and 168 normal pancreatic samples obtained from the GTEx database were combined and defined as the control group. All of the data from TCGA and GTEx were converted into the log 2 (x + 1) form (Yuan et al, 2021a). The "sva" R package was employed to batch normalize the raw data from different databases.…”

Comprehensive machine-learning survival framework develop a consensus model in large scale multi-center cohorts for pancreatic cancer

“…However, given the relatively low incidence but extremely high mortality of PACA, coupled with the current lack of optimal biomarkers to guide treatment decisions, patients may be over- or under-treated resulting in heavy socioeconomic burden, serious toxic side effects, or rapid disease progression ( De Dosso et al, 2021 ). In response to this problem, numerous multigene panels have been developed to address the wide heterogeneity of PACA and achieve relatively good performance in certain cohorts ( Wang et al, 2022; Tan et al, 2020; Yuan et al, 2021 ). Considering that these prognostic models were based on the expression files of mRNAs, miRNAs, or lncRNAs in a specific pathway (e.g., immunity, metabolic reprogramming, m6A methylation), data utilization is insufficient ( Wang et al, 2022; Tan et al, 2020; Yuan et al, 2021 ).…”

“…The prognostic value of AIDPS was evaluated by Kaplan-Meier curve and multivariate Cox regression analysis. The calibration curve and receiver operator characteristic (ROC) curve were plotted to assess the predictive accuracy of AIDPS.Collection and calculation of PACA published signaturesWith the attention paid to the stratified management and precise treatment of PACA, many prognostic signatures have been constructed, including m6A-related lncRNA signature, metabolic reprogramming-related signature, and SMAD4-driven immune signature, etc (Wang et al, 2022;Tan et al, 2020;Yuan et al, 2021)…”

“…Therefore, in clinical settings, our AIDPS may become a reliable platform to further serve individualized decision-making in PACA.In the era of precision medicine, anatomy-based TNM staging is far from meeting the needs of clinicians for ideal biomarkers that could accurately evaluate the prognosis and predict the efficacy of PACA patients(Katz et al, 2008;. Recently, numerous prognostic signatures of PACA have been constructed, but most of them are based on a specific biological pathway such as immunity, metabolic reprogramming, and m6A methylation(Wang et al, 2022;Tan et al, 2020;Yuan et al, 2021). This ignored information about other biological processes that played a crucial role in the oncogenesis and progression of PACA.…”