Differential Interactome Proposes Subtype-Specific Biomarkers and Potential Therapeutics in Renal Cell Carcinomas

Caliskan, Aysegul; Gulfidan, Gizem; Sinha, Raghu; Arğa, Kazım Yalçın

doi:10.3390/jpm11020158

Cited by 9 publications

(5 citation statements)

References 31 publications

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“…We carried out SBVS using the docking method on FDA-Approved drugs of the ZINC15 database to recognize new SENP1 inhibitors. Molecular docking is the best method to quickly estimate the binding conformations of ligands that are energy-efficient to interact with a pharmacological receptor site and has obtained popularity as a tool to store time and costs in the pipeline of drug discovery and development ( Kaushik et al, 2020 ; Caliskan et al, 2021 ). ZINC15 joins biological activities of drugs, gene products, and natural products with commercial availability ( Irwin and Shoichet, 2005 ).…”

Section: Resultsmentioning

confidence: 99%

Evidence of Omics, Immune Infiltration, and Pharmacogenomic for SENP1 in the Pan-Cancer Cohort

2021

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Sentrin specific-protease 1 (SENP1) is a protein involved in deSUMOylation that is almost overexpressed in cancer. SENP1 has a determinative role in the activation of transcription programs in the innate immune responses and the development B of and C lymphocytes. We found, SENP1 possibly plays a critical role in immune infiltration and acts as an expression marker in PAAD, ESCA, and THYM. CD4+ T cells, CD8+ T cells, and macrophages were more key-related immune cells, indicating that SENP1 might be introduced as a potential target for cancer immunotherapy. We further showed that dysregulation of SENP1 is powerfully associated with decreased patient survival and clinical stage. Total SENP1 protein also increases in cancer. SENP1 is also controlled by transcription factors (TFs) CREB1, KDM5A, REST, and YY1 that regulates apoptosis, cell cycle, cell proliferation, invasion, tumorigenesis, and metastasis. These TFs were in a positive correlation with SENP1. MiR-138–5p, miR-129-1-3p, and miR-129-2-3p also inhibit tumorigenesis through targeting of SENP1. The SENP1 expression level positively correlated with the expression levels of UBN1, SP3, SAP130, NUP98, NUP153 in 32 tumor types. SENP1 and correlated and binding genes: SAP130, NUP98, and NUP153 activated cell cycle. Consistent with this finding, drug analysis was indicated SENP1 is sensitive to cell cycle, apoptosis, and RTK signaling regulators. In the end, SENP1 and its expression-correlated and functional binding genes were enriched in cell cycle, apoptosis, cellular response to DNA damage stimulus. We found that the cell cycle is the main way for tumorigenesis by SENP1. SENP1 attenuates the effect of inhibitory drugs on the cell cycle. We also introduced effective FDA-Approved drugs that can inhibit SENP1. Therefore in the treatments in which these drugs are used, SENP1 inhibition is a suitable approach. This study supplies a wide analysis of the SENP1 across The Cancer Genome Atlas (CGA) cancer types. These results suggest the potential roles of SENP1 as a biomarker for cancer. Since these drugs and the drugs that cause to resistance are applied to cancer treatment, then these two class drugs can use to inhibition of SENP1.

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Section: Resultsmentioning

confidence: 99%

Evidence of Omics, Immune Infiltration, and Pharmacogenomic for SENP1 in the Pan-Cancer Cohort

2021

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“…We applied our constructed differential interactome algorithm, which had proven to be a powerful tool in other studies [8,31,32], for the protein-protein interactome data on hallmarks of cancer. Our results revealed DIHCPs showing remarkable changes in the interaction patterns of patients during the transition from the "normal" phenotype to the "cancer" phenotype.…”

Section: Discussionmentioning

confidence: 99%

A Pan-Cancer Atlas of Differentially Interacting Hallmarks of Cancer Proteins

Kori

Ozdemir

Arğa

et al. 2022

JPM

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Cancer hallmark genes and proteins orchestrate and drive carcinogenesis to a large extent, therefore, it is important to study these features in different cancer types to understand the process of tumorigenesis and discover measurable indicators. We performed a pan-cancer analysis to map differentially interacting hallmarks of cancer proteins (DIHCP). The TCGA transcriptome data associated with 12 common cancers were analyzed and the differential interactome algorithm was applied to determine DIHCPs and DIHCP-centric modules (i.e., DIHCPs and their interacting partners) that exhibit significant changes in their interaction patterns between the tumor and control phenotypes. The diagnostic and prognostic capabilities of the identified modules were assessed to determine the ability of the modules to function as system biomarkers. In addition, the druggability of the prognostic and diagnostic DIHCPs was investigated. As a result, we found a total of 30 DIHCP-centric modules that showed high diagnostic or prognostic performance in any of the 12 cancer types. Furthermore, from the 16 DIHCP-centric modules examined, 29% of these were druggable. Our study presents candidate systems’ biomarkers that may be valuable for understanding the process of tumorigenesis and improving personalized treatment strategies for various cancers, with a focus on their ten hallmark characteristics.

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“…Many successful or unsuccessful algorithms are developed today for this purpose. The "differential interactome algorithm" used in this study has been shown to be successful in various cancers [18,[49][50][51], but in the present study, it was applied for the rst time to HNSC cancer considering HPV status. We discovered DIPs that exhibit remarkable changes in their interaction patterns during the transition from the "HPV16-positive HNSC cancer" phenotype to the "HPV-negative HNSC cancer" phenotype.…”

Section: Discussionmentioning

confidence: 99%

HPV16 Status Predicts Differential Interactome in Head and Neck Squamous Cell Carcinoma: Potential Prognostic Biomarkers and Therapeutics in Framework of 3P Medicine

Kori

Arğa

2022

Preprint

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Human papillomavirus (HPV) infection is one of the causative factors in the development of head and neck squamous cell carcinoma (HNSC). One in four HNSC cases is positive for HPV infection, and HPV16 is the most common infection in HNSC cancer. HPV-positive and HPV-negative HNSC patients differ significantly in their molecular profiles and clinical features. Although the diagnosis and treatment strategies of HNSC patients should differ depending on the HPV status of the patient, there are no specific treatment regimens for these patients today. Given the enormous variation in HNSC cancers depending on HPV status, the 3P framework is of great importance specifically for these patients. In this study we present systemic biomarkers and treatment options tailored to the patient's HPV status. Gene expression levels of HPV16-positive and -negative cancer patients were used as proxies, and the differential interactome algorithm was employed to identify the differential interacting proteins (DIPs) around which significant changes occur at protein-protein interactome level. By assessing the prognostic capabilities and druggabilities of DIPs and their interacting partners (DIP-centered modules), we were able to introduce eight DIP-centered modules as potential systems biomarkers specialized for either HPV16-positive or HPV16-negative phenotype. The prognostic power of three modules specific for HPV16-positive phenotype was also validated by an independent cohort study. Finally, raloxifene was repositioned as a drug candidate for the treatment of HPV16-positive HNSC patients. With this study, we open new perspectives for the development of effective prognostic and treatment strategies for HNSC patients depending on their HPV16 status.

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Differential Interactome Proposes Subtype-Specific Biomarkers and Potential Therapeutics in Renal Cell Carcinomas

Cited by 9 publications

References 31 publications

Evidence of Omics, Immune Infiltration, and Pharmacogenomic for SENP1 in the Pan-Cancer Cohort

Evidence of Omics, Immune Infiltration, and Pharmacogenomic for SENP1 in the Pan-Cancer Cohort

A Pan-Cancer Atlas of Differentially Interacting Hallmarks of Cancer Proteins

HPV16 Status Predicts Differential Interactome in Head and Neck Squamous Cell Carcinoma: Potential Prognostic Biomarkers and Therapeutics in Framework of 3P Medicine

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