Identification of critical genes and pathways associated with hepatocellular carcinoma and type 2 diabetes mellitus using integrated bioinformatics analysis

Maddah, Reza; Shariati, Parvin; Arabpour, Javad; Bazireh, Homa; Shadpirouz, Marzieh; Kafraj, Amir Shafiei

doi:10.1016/j.imu.2022.100956

Cited by 8 publications

(4 citation statements)

References 30 publications

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“…Moreover, ASPM overexpression was strongly correlated with bladder cancer and consiered as promising predictor 91 . Our findings also illustrated that ASPM was a novel key biomarker for HCC, which was supported by the existing studies 9,22,35,38,39,[41][42][43][45][46][47][48]58 .…”

Section: Discussionsupporting

confidence: 88%

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning

Hasan

Maniruzzaman

Shin

2023

Sci Rep

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Hepatocellular carcinoma (HCC) is the most common lethal malignancy of the liver worldwide. Thus, it is important to dig the key genes for uncovering the molecular mechanisms and to improve diagnostic and therapeutic options for HCC. This study aimed to encompass a set of statistical and machine learning computational approaches for identifying the key candidate genes for HCC. Three microarray datasets were used in this work, which were downloaded from the Gene Expression Omnibus Database. At first, normalization and differentially expressed genes (DEGs) identification were performed using limma for each dataset. Then, support vector machine (SVM) was implemented to determine the differentially expressed discriminative genes (DEDGs) from DEGs of each dataset and select overlapping DEDGs genes among identified three sets of DEDGs. Enrichment analysis was performed on common DEDGs using DAVID. A protein-protein interaction (PPI) network was constructed using STRING and the central hub genes were identified depending on the degree, maximum neighborhood component (MNC), maximal clique centrality (MCC), centralities of closeness, and betweenness criteria using CytoHubba. Simultaneously, significant modules were selected using MCODE scores and identified their associated genes from the PPI networks. Moreover, metadata were created by listing all hub genes from previous studies and identified significant meta-hub genes whose occurrence frequency was greater than 3 among previous studies. Finally, six key candidate genes (TOP2A, CDC20, ASPM, PRC1, NUSAP1, and UBE2C) were determined by intersecting shared genes among central hub genes, hub module genes, and significant meta-hub genes. Two independent test datasets (GSE76427 and TCGA-LIHC) were utilized to validate these key candidate genes using the area under the curve. Moreover, the prognostic potential of these six key candidate genes was also evaluated on the TCGA-LIHC cohort using survival analysis.

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Section: Discussionsupporting

confidence: 88%

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning

Hasan

Maniruzzaman

Shin

2023

Sci Rep

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“…Comparative studies on microbiome composition revealed alterations due to COVID-19, with a notable increase in Rothia spp. relative abundance [20,21,22].…”

Section: Rothia Mucilaginosa Initially Classified Asmentioning

confidence: 99%

Microbiological and Epidemiological Aspects of Endocarditis Caused by Rothia spp.: A Systematic Review

Norberg,

Manhães

et al. 2023

JAMMR

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Background and Aim: Rothia, a genus comprising pleomorphic Gram-positive bacteria found in the human oral, intestinal, and skin microbiota, is recognized as an opportunistic pathogen. Case reports of Rothia spp. endocarditis in the scientific literature are scarce, with limited knowledge of relevant data on Rothia endocarditis and clinical aspects of its treatment. The objective of this literature review is to compile clinical aspects of endocarditis caused by Rothia species, analyzing results and clinical practices to elucidate the most important risk factors, comorbidities, prognostic factors, and appropriate antibiotic treatment options. Methods: Employing the PRISMA model, a systematic review was conducted utilizing PubMed, SciELO, and Google Scholar databases, encompassing articles published from 1978 to November 2023. Pertinent aspects were systematically recorded and summarized for subsequent analysis. Results: Rothia dentocariosa (48.69%), Rothia mucilaginosa (22.37%), Rothia aeria (14.47%), and Rothia kristinae (14.47%) were recognized as the agents of endocarditis cases. Patients exhibited an average age of 48.5 years, with a notable male preponderance (71.6%). Clinical manifestations of Rothia spp. endocarditis presented similar features compared to other Gram-positive bacterial endocarditis cases. The mortality rate was notably lower than observed in other infectious endocarditis instances (11.84%). Predominant risk factors included preexisting cardiovascular diseases (50%), followed by odontological procedures, caries, and precarious oral hygiene (17.1%), immunocompromised status (14.47%), injectable illicit drug use (11.84%), and diabetes (9.21%). Embolic events were documented in 35.53% of patients, predominantly in Central Nervous System (28.95%). Mycotic aneurysms were identified in 6.58% of cases. Resistance to antibiotics was identified in only 13.16% of strains causing endocarditis, although certain strains displayed characteristics indicative of multidrug resistance. Conclusion: Despite its rarity, Rothia spp. endocarditis exhibits clinical parallels with endocarditis caused by other Gram-positive bacteria, but with a comparatively lower mortality rate. Challenges in identifying Rothia spp. species based on cultural and microscopic characteristics, associated to early resolution in antibiotic therapy, seems to contribute to the underreporting of endocarditis caused by these bacteria.

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“…By evaluating potential molecular biomarkers, diagnostic and therapeutic options can be improved in many ways. 18 Nanotechnology offers a promising solution by designing novel drug delivery systems (DDSs) that can entrap and release lncRNAs in a controlled and efficient manner. 19 Nanotechnology also provides opportunities to improve the efficacy and safety of lncRNA delivery by combining it with other therapeutic modalities such as chemotherapy or gene therapy, or by exploiting the unique properties of nanomaterials such as size, shape, surface charge, or functionality.…”

Section: Introductionmentioning

confidence: 99%

“…By evaluating potential molecular biomarkers, diagnostic and therapeutic options can be improved in many ways. 18…”

Section: Introductionmentioning

confidence: 99%

Nanotherapeutic approaches for delivery of long non-coding RNAs: an updated review with emphasis on cancer

Davodabadi,

Mirinejad,

Malik

et al. 2024

Nanoscale

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Identification of critical genes and pathways associated with hepatocellular carcinoma and type 2 diabetes mellitus using integrated bioinformatics analysis

Cited by 8 publications

References 30 publications

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning

Differentially expressed discriminative genes and significant meta-hub genes based key genes identification for hepatocellular carcinoma using statistical machine learning

Microbiological and Epidemiological Aspects of Endocarditis Caused by Rothia spp.: A Systematic Review

Nanotherapeutic approaches for delivery of long non-coding RNAs: an updated review with emphasis on cancer

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