A Novel Immune-Related Gene Prognostic Index (IRGPI) in Pancreatic Adenocarcinoma (PAAD) and Its Implications in the Tumor Microenvironment

Zhou, Shujing; Szöllősi, Attila Gábor; Huang, Xufeng; Chang-Chien, Yi-Che; Hajdú, András

doi:10.3390/cancers14225652

Cited by 5 publications

(4 citation statements)

References 69 publications

(76 reference statements)

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“…Different investigations reported up regulation of this lncRNA in several types of cancers. The lncRNA H19 regulates GLIOMA angiogenesis [ 45 , 46 ], while MAP3K14 is one of the well-recognized biomarkers in the prognosis of renal cancer, which is reminiscent of the pancreatic metastasis from renal cell carcinoma [ 47 ]. MEG3 has been recently found to be important for the prediction of LEUKEMIA risk [ 48 ].…”

Section: Resultsmentioning

confidence: 99%

Neighborhood based computational approaches for the prediction of lncRNA-disease associations

Bonomo,

Rombo

2024

BMC Bioinformatics

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Motivation Long non-coding RNAs (lncRNAs) are a class of molecules involved in important biological processes. Extensive efforts have been provided to get deeper understanding of disease mechanisms at the lncRNA level, guiding towards the detection of biomarkers for disease diagnosis, treatment, prognosis and prevention. Unfortunately, due to costs and time complexity, the number of possible disease-related lncRNAs verified by traditional biological experiments is very limited. Computational approaches for the prediction of disease-lncRNA associations allow to identify the most promising candidates to be verified in laboratory, reducing costs and time consuming. Results We propose novel approaches for the prediction of lncRNA-disease associations, all sharing the idea of exploring associations among lncRNAs, other intermediate molecules (e.g., miRNAs) and diseases, suitably represented by tripartite graphs. Indeed, while only a few lncRNA-disease associations are still known, plenty of interactions between lncRNAs and other molecules, as well as associations of the latters with diseases, are available. A first approach presented here, NGH, relies on neighborhood analysis performed on a tripartite graph, built upon lncRNAs, miRNAs and diseases. A second approach (CF) relies on collaborative filtering; a third approach (NGH-CF) is obtained boosting NGH by collaborative filtering. The proposed approaches have been validated on both synthetic and real data, and compared against other methods from the literature. It results that neighborhood analysis allows to outperform competitors, and when it is combined with collaborative filtering the prediction accuracy further improves, scoring a value of AUC equal to 0966. Availability Source code and sample datasets are available at: https://github.com/marybonomo/LDAsPredictionApproaches.git

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Section: Resultsmentioning

confidence: 99%

Neighborhood based computational approaches for the prediction of lncRNA-disease associations

Bonomo,

Rombo

2024

BMC Bioinformatics

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“…Tumor microenvironment (TME) appears to play an essential role in tumorigenesis and progression. Particularly, TME contains several types of tumor‐infiltrating immune cells that have important predictive value for the efficacy of immunotherapy [ 23 ]. To further explore potential mechanisms underlying this risk signature in LIHC prognosis, several immune-associated analyses such as EpiC, QuanTIseq, CIBERSORT − ABS, MCPcounter, CIBERSORT, xCell, and TIMER were performed.…”

Section: Discussionmentioning

confidence: 99%

“…Additionally, the TIME composed of tumor‐infiltrating immune cells is found in close association with various approaches for tumor treatment, including chemoradiotherapy, molecular-targeted therapies, and immunotherapy [ 21 ]. It is becoming increasingly popular in bioinformatics to identify markers involved in regulating TME and TIME, with immune-related prognostic genes being of particular interest [ 22 , 23 ]. In particular, prognostic signatures can also serve as efficacy indicators of anti-tumor drugs.…”

Section: Introductionmentioning

confidence: 99%

Identification and in vitro and in vivo validation of the key role of GSDME in pyroptosis-related genes signature in hepatocellular carcinoma

et al. 2023

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We used pyroptosis-related genes to establish a risk–score model for prognostic prediction of liver hepatocellular carcinoma (LIHC) patients. A total of 52 pyroptosis-associated genes were identified. Then, data for 374 LIHC patients and 50 normal individuals were acquired from the TCGA database. Through gene expression analyses, differentially expressed genes (DEGs) were determined. The 13 pyroptosis-related genes (PRGs) confirmed as potential prognostic factors through univariate Cox regression analysis were entered into Lasso and multivariate Cox regression to build a PRGs prognostic signature, containing four PRGs (BAK1, GSDME, NLRP6, and NOD2) determined as independent prognostic factors. mRNA levels were evaluated by qRT-PCR, while overall survival (OS) rates were assessed by the Kaplan–Meier method. Enrichment analyses were done to establish the mechanisms associated with differential survival status of LIHC patients from a tumor immunology perspective. Additionally, a risk score determined by the prognostic model could divide LIHC patients into low- and high-risk groups using median risk score as cut-off. A prognostic nomogram, derived from the prognostic model and integrating clinical characteristics of patients, was constructed. The prognostic function of the model was also validated using GEO, ICGC cohorts, and online databases Kaplan–Meier Plotter. Small interfering RNA-mediated knockdown of GSDME, as well as lentivirus-mediated GSDME knockdown, were performed to validate that knockdown of GSDME markedly suppressed growth of HCC cells both in vivo and in vitro. Collectively, our study demonstrated a PRGs prognostic signature that had great clinical value in prognosis assessment.

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“…Accordingly, low expression of SEMA6C translated into shorter metastasis-free survival of patients. Recently, Zhou and colleagues [57] employed a bioinformatics approach and harnessed the SEMA6C gene expression profile, among 11 other chosen genes, to build a prediction algorithm for the immunological microenvironment of pancreatic tumors. According to the authors' findings, the proposed method based on the immune-related gene prognostic index (IRGPI) can be employed in survival prediction (OS) in patients with pancreatic adenocarcinoma (PAAD) and molecular classification of PAAD.…”

Section: Semaphorin 6cmentioning

confidence: 99%

Semaphorin 6 Family—An Important Yet Overlooked Group of Signaling Proteins Involved in Cancerogenesis

Wagner,

Ochman,

Wagner

2023

Cancers

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According to recent evidence, some groups of semaphorins (SEMAs) have been associated with cancer progression. These proteins are able to modulate the cellular signaling of particular receptor tyrosine kinases (RTKs) via the stimulation of SEMA-specific coreceptors, namely plexins (plexin-A, -B, -C, -D) and neuropilins (Np1, Np2), which share common domains with RTKs, leading to the coactivation of the latter receptors. MET, ERBB2, VEGFR2, PFGFR, and EGFR, among others, represent acknowledged targets of semaphorins that are often associated with tumor progression or poor prognosis. In particular, higher expression of SEMA6 family proteins in cancer cells and stromal cells of the cancer niche is often associated with enhanced tumor angiogenesis, metastasis, and resistance to anticancer therapy. Notably, high SEMA6 expression in malignant tumor cells such as melanoma, pleural mesothelioma, gastric cancer, lung adenocarcinoma, and glioblastoma may serve as a prognostic biomarker of tumor progression. To date, very few studies have focused on the mechanisms of transmembrane SEMA6-driven tumor progression and its underlying interplay with RTKs within the tumor microenvironment. This review presents the growing evidence in the literature on the complex and shaping role of SEMA6 family proteins in cancer responsiveness to environmental stimuli.

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A Novel Immune-Related Gene Prognostic Index (IRGPI) in Pancreatic Adenocarcinoma (PAAD) and Its Implications in the Tumor Microenvironment

Cited by 5 publications

References 69 publications

Neighborhood based computational approaches for the prediction of lncRNA-disease associations

Neighborhood based computational approaches for the prediction of lncRNA-disease associations

Identification and in vitro and in vivo validation of the key role of GSDME in pyroptosis-related genes signature in hepatocellular carcinoma

Semaphorin 6 Family—An Important Yet Overlooked Group of Signaling Proteins Involved in Cancerogenesis

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