Identification of Potential Biomarkers in Stomach Adenocarcinoma using
Machine Learning Approaches

Nazari, Elham; Pourali, Ghazaleh; Khazaei, Mojtaba; Avan, Amir; Asadnia, Alireza; Dashtiahangar, Mohammad; Mohit, Reza; Maftooh, Mina; Nassiri, Mohammadreza; Hassanian, Seyed Mahdi; Ghayour‐Mobarhan, Majid; Ferns, Gordon A.; Shahidsales, Soodabeh

doi:10.2174/1574893618666230227103427

Cited by 8 publications

(3 citation statements)

References 59 publications

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“…In addition, the importance of miRNA-mRNA networks in activating or inhibiting many cancer-related molecular signaling pathways has recently been observed 31 , 32 . Recently, with the development of bioinformatics as well as the aid of machine learning algorithms, different forms of next-generation sequencing are increasingly being used to detect biomarkers with a role in early detection, treatment, and prognosis of cancer 33 – 35 . Machine learning approaches have gained popularity due to their predictive power of diagnostic and prognostic biomarkers.…”

Section: Discussionmentioning

confidence: 99%

Machine learning algorithms reveal potential miRNAs biomarkers in gastric cancer

Azari

Nazari

Mohit

et al. 2023

Sci Rep

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Gastric cancer is the high mortality rate cancers globally, and the current survival rate is 30% even with the use of combination therapies. Recently, mounting evidence indicates the potential role of miRNAs in the diagnosis and assessing the prognosis of cancers. In the state-of-art research in cancer, machine-learning (ML) has gained increasing attention to find clinically useful biomarkers. The present study aimed to identify potential diagnostic and prognostic miRNAs in GC with the application of ML. Using the TCGA database and ML algorithms such as Support Vector Machine (SVM), Random Forest, k-NN, etc., a panel of 29 was obtained. Among the ML algorithms, SVM was chosen (AUC:88.5%, Accuracy:93% in GC). To find common molecular mechanisms of the miRNAs, their common gene targets were predicted using online databases such as miRWalk, miRDB, and Targetscan. Functional and enrichment analyzes were performed using Gene Ontology (GO) and Kyoto Database of Genes and Genomes (KEGG), as well as identification of protein–protein interactions (PPI) using the STRING database. Pathway analysis of the target genes revealed the involvement of several cancer-related pathways including miRNA mediated inhibition of translation, regulation of gene expression by genetic imprinting, and the Wnt signaling pathway. Survival and ROC curve analysis showed that the expression levels of hsa-miR-21, hsa-miR-133a, hsa-miR-146b, and hsa-miR-29c were associated with higher mortality and potentially earlier detection of GC patients. A panel of dysregulated miRNAs that may serve as reliable biomarkers for gastric cancer were identified using machine learning, which represents a powerful tool in biomarker identification.

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Section: Discussionmentioning

confidence: 99%

Machine learning algorithms reveal potential miRNAs biomarkers in gastric cancer

Azari

Nazari

Mohit

et al. 2023

Sci Rep

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“…Our previous studies identified prognostic and diagnostic biomarkers in colorectal cancer and gastric cancer using RNA-seq analysis and machine learning [17][18][19]. In contrast to our previous study, the current study was designed based on an integrated two omics and deep learning approach to identify prognostic and diagnostic biomarkers in colorectal cancer (CRC) patients at different disease stages (early and metastatic).…”

Section: Discussionmentioning

confidence: 99%

The Prognostic Value of ASPHD1 and ZBTB12 in Colorectal Cancer: A Machine Learning-Based Integrated Bioinformatics Approach

Asadnia,

Nazari,

Goshayeshi

et al. 2023

Cancers

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Introduction: Colorectal cancer (CRC) is a common cancer associated with poor outcomes, underscoring a need for the identification of novel prognostic and therapeutic targets to improve outcomes. This study aimed to identify genetic variants and differentially expressed genes (DEGs) using genome-wide DNA and RNA sequencing followed by validation in a large cohort of patients with CRC. Methods: Whole genome and gene expression profiling were used to identify DEGs and genetic alterations in 146 patients with CRC. Gene Ontology, Reactom, GSEA, and Human Disease Ontology were employed to study the biological process and pathways involved in CRC. Survival analysis on dysregulated genes in patients with CRC was conducted using Cox regression and Kaplan–Meier analysis. The STRING database was used to construct a protein–protein interaction (PPI) network. Moreover, candidate genes were subjected to ML-based analysis and the Receiver operating characteristic (ROC) curve. Subsequently, the expression of the identified genes was evaluated by Real-time PCR (RT-PCR) in another cohort of 64 patients with CRC. Gene variants affecting the regulation of candidate gene expressions were further validated followed by Whole Exome Sequencing (WES) in 15 patients with CRC. Results: A total of 3576 DEGs in the early stages of CRC and 2985 DEGs in the advanced stages of CRC were identified. ASPHD1 and ZBTB12 genes were identified as potential prognostic markers. Moreover, the combination of ASPHD and ZBTB12 genes was sensitive, and the two were considered specific markers, with an area under the curve (AUC) of 0.934, 1.00, and 0.986, respectively. The expression levels of these two genes were higher in patients with CRC. Moreover, our data identified two novel genetic variants—the rs925939730 variant in ASPHD1 and the rs1428982750 variant in ZBTB1—as being potentially involved in the regulation of gene expression. Conclusions: Our findings provide a proof of concept for the prognostic values of two novel genes—ASPHD1 and ZBTB12—and their associated variants (rs925939730 and rs1428982750) in CRC, supporting further functional analyses to evaluate the value of emerging biomarkers in colorectal cancer.

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“…TCGA (Cancer Genome Atlas), an integrated collection of clinical information and gene sequencing data, allows systematic analysis of the molecular mechanisms underlying clinical features associated with cancers, for example. It contributed to improved diagnostic methods and ultimately improved the survival prognosis of cancer patients by assessing the pathological stage, histological type, tumor grade, diagnosis, and prognosis of the disease (19)(20)(21)(22). This study used the TCGA database for gene expression proo ng and machine learning to identify differential expression genes (DEGs) of PRCC tumors.…”

Section: Introductionmentioning

confidence: 99%

Identification of BCL11A, NTN5, and OGN as diagnosis biomarker of papillary renal cell carcinomas by bioinformatic analysis

Haghshenas¹,

Fathi,

Ahmadzadeh

et al. 2024

Preprint

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The prevalence of papillary renal cell carcinomas is estimated to be between 10% and 15. At this time, there is no effective therapeutic approach available for patients with advanced PRCCs. The molecular biomarkers associated with PRCC diagnoses have been rarely studied compared to renal clear cell carcinomas, therefore it is imperative that novel molecular biomarkers be identified to aid in the early identification of this disease. Bioinformatics and artificial intelligence technologies have become increasingly important in the search for diagnostic biomarkers for early cancer detection. In this study, three genes, BCL11A, NTN5, and OGN, were identified as diagnostic biomarkers using the TCGA database and deep learning techniques. To identify differentially expressed genes (DEGs), RNA expression profiles of PRCC patients were analyzed using a machine learning approach. A number of molecular pathways and co-expressions of DEGs have been analyzed, and a correlation between DEGs and clinical data has been determined. Diagnostic markers were then determined via machine learning analysis. The 10 genes selected with the highest Variable Importance value (more than 0.9) were further investigated and six of them were upregulated (BCL11A, NTN5, SEL1L3, SKA3, TAPBP, SEMA6A) and four were downregulated (OGN, ADCY4, SMOC2, CCL23). A combined ROC curve analysis revealed that the BCL11A-NTN5-OGN genes, which have specificity and sensitivity values of 0.968 and 0.901 respectively, can be used as a diagnostic biomarker for PRCC. In general, the genes introduced in this study may be able to be used as diagnostic biomarkers for the early diagnosis of PRCC and thus provide the possibility of early treatment and preventing the progression of the disease.

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Identification of Potential Biomarkers in Stomach Adenocarcinoma using Machine Learning Approaches

Cited by 8 publications

References 59 publications

Machine learning algorithms reveal potential miRNAs biomarkers in gastric cancer

Machine learning algorithms reveal potential miRNAs biomarkers in gastric cancer

The Prognostic Value of ASPHD1 and ZBTB12 in Colorectal Cancer: A Machine Learning-Based Integrated Bioinformatics Approach

Identification of BCL11A, NTN5, and OGN as diagnosis biomarker of papillary renal cell carcinomas by bioinformatic analysis

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