OSgbm: An Online Consensus Survival Analysis Web Server for Glioblastoma

Dong, H.; Wang, Qiang; Li, Ning; Lv, Jiajia; Ge, Lite; Yang, Mengsi; Zhang, Guosen; An, Yang; Wang, Fengling; Xie, Longxiang; Li, Yongqiang; Zhu, Wan; Zhang, Haiyu; Zhang, Minghang; Guo, Xiangqian

doi:10.3389/fgene.2019.01378

Cited by 23 publications

(17 citation statements)

References 71 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To rank and shortlist kinase candidates using information from the connectivity network, we employed a ranking algorithm similar to Kinase Addiction Ranker 20 . All 49 protein kinases targeted by the 31 hypoxic-selective inhibitors were scored individually according to their interaction with HIF-1α and network topology in addition to the predicted GBM patients' survival based on their expression status retrieved from PRECOG 21 , TCGA 22 , and CGGA 23 to strengthen the robustness and clinical relevance of the analysis ( Figure 1 D ). The sum of scores from each category was used to rank the protein kinases ( Figure 1 E ).…”

Section: Resultsmentioning

confidence: 99%

“…For the ranking analysis, we used an unweighted scoring algorithm similar to Kinase Addiction Ranker 20 . Each protein kinase was scored and ranked based on five criteria: direct interaction with HIF-1α (based on IPA network), network topology (number of first-order neighbors from IPA network), and the correlation of their gene expression with GBM patients' overall survival retrieved from Prediction of Clinical Outcomes from Genomic Profiles (PRECOG) 21 , The Cancer Genome Atlas Program (TCGA) 22 , and Chinese Glioma Genome Atlas (CGGA) 23 . The first two criteria were established based on the assumptions that HIF-1α is a master regulator of hypoxic response 24 and that protein kinases with more interacting neighbors are the signaling hubs in the kinase-kinase interactome during hypoxic stress response.…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Kinomic profile in patient-derived glioma cells during hypoxia reveals c-MET-PI3K dependency for adaptation

Cheng¹,

Marvalim²,

Zhu³

et al. 2021

Theranostics

View full text Add to dashboard Cite

Hypoxic microenvironment is a hallmark of solid tumors, especially glioblastoma. The strong reliance of glioma-propagating cells (GPCs) on hypoxia-induced survival advantages is potentially exploitable for drug development. Methods: To identify key signaling pathways for hypoxia adaptation by patient-derived GPCs, we performed a kinase inhibitor profiling by screening 188 small molecule inhibitors against 130 different kinases in normoxia and hypoxia. Potential kinase candidates were prioritized for in vitro and in vivo investigations using a ranking algorithm that integrated information from the kinome connectivity network and estimated patients' survival based on expression status. Results: Hypoxic drug screen highlighted extensive modifications of kinomic landscape and a crucial functionality of c-MET-PI3K. c-MET inhibitors diminished phosphorylation of c-MET and PI3K in GPCs subjected to hypoxia, suggesting its role in the hypoxic adaptation of GPCs. Mechanistically, the inhibition of c-MET and PI3K impaired antioxidant defense, leading to oxidative catastrophe and apoptosis. Repurposed c-MET inhibitors PF04217903 and tivantinib exhibited hypoxic-dependent drug synergism with temozolomide, resulting in reduced tumor load and growth of GPC xenografts. Detailed analysis of bulk and single-cell glioblastoma transcriptomes associates the cellular subpopulation over-expressing c-MET with inflamed, hypoxic, metastatic, and stem-like phenotypes. Conclusions: Thus, our “bench to bedside (the use of patient-derived GPCs and xenografts for basic research) and back (validation with independent glioblastoma transcriptome databases)” analysis unravels the novel therapeutic indications of c-MET and PI3K/Akt inhibitors for the treatment of glioblastoma, and potentially other cancers, in the hypoxic tumor microenvironment.

show abstract

Section: Resultsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Kinomic profile in patient-derived glioma cells during hypoxia reveals c-MET-PI3K dependency for adaptation

Cheng¹,

Marvalim²,

Zhu³

et al. 2021

Theranostics

View full text Add to dashboard Cite

show abstract

“…Further, with the help of GWAS, we can ascertain the disease-associated genetic loci, and it has been observed that genes linked with these loci are potential therapeutic targets. For instance, Li et al [ 48 ] used the GWAS catalog, gene expression, epigenomics, and methylation data to determine target genes associated with juvenile idiopathic arthritis loci through ML analysis . In addition, specific genes whose mutations can lead to different threatening diseases are also promising therapeutic targets.…”

Section: Revolutionizing Drug Discovery Process: Role Of Big Data Andmentioning

confidence: 99%

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

et al. 2021

View full text Add to dashboard Cite

Graphic abstract Drug designing and development is an important area of research for pharmaceutical companies and chemical scientists. However, low efficacy, off-target delivery, time consumption, and high cost impose a hurdle and challenges that impact drug design and discovery. Further, complex and big data from genomics, proteomics, microarray data, and clinical trials also impose an obstacle in the drug discovery pipeline. Artificial intelligence and machine learning technology play a crucial role in drug discovery and development. In other words, artificial neural networks and deep learning algorithms have modernized the area. Machine learning and deep learning algorithms have been implemented in several drug discovery processes such as peptide synthesis, structure-based virtual screening, ligand-based virtual screening, toxicity prediction, drug monitoring and release, pharmacophore modeling, quantitative structure–activity relationship, drug repositioning, polypharmacology, and physiochemical activity. Evidence from the past strengthens the implementation of artificial intelligence and deep learning in this field. Moreover, novel data mining, curation, and management techniques provided critical support to recently developed modeling algorithms. In summary, artificial intelligence and deep learning advancements provide an excellent opportunity for rational drug design and discovery process, which will eventually impact mankind. The primary concern associated with drug design and development is time consumption and production cost. Further, inefficiency, inaccurate target delivery, and inappropriate dosage are other hurdles that inhibit the process of drug delivery and development. With advancements in technology, computer-aided drug design integrating artificial intelligence algorithms can eliminate the challenges and hurdles of traditional drug design and development. Artificial intelligence is referred to as superset comprising machine learning, whereas machine learning comprises supervised learning, unsupervised learning, and reinforcement learning. Further, deep learning, a subset of machine learning, has been extensively implemented in drug design and development. The artificial neural network, deep neural network, support vector machines, classification and regression, generative adversarial networks, symbolic learning, and meta-learning are examples of the algorithms applied to the drug design and discovery process. Artificial intelligence has been applied to different areas of drug design and development process, such as from peptide synthesis to molecule design, virtual screening to molecular docking, quantitative structure–activity relationship to drug repositioning, protein misfolding to protein–protein interactions, and molecular pathway identification to polypharmacology. Artificial intelligence principles have been applied to the classification of active and inactive, monitoring drug release, pre-clinical and clinical development, primary and secondary drug screeni...

show abstract

“…Besides, the prognostic value of TRIM44 in the specific type of cancer was explored by some online OS analysis webservers, including OSgbm for glioblastoma [16], OSpaad for pancreatic carcinoma [17], OSbrca for breast cancer [18], OSacc for adrenocortical carcinoma [19], OSuvm for uveal melanoma [20], OScc for cervical cancer [21], OSkirc for kidney renal clear cell carcinoma [22], OSescc for esophageal squamous cell carcinoma [23], OSblca for bladder cancer [24], OSlms for leiomyosarcoma [25].…”

Section: Public Data and Toolsmentioning

confidence: 99%

Expression of tripartite motif-containing 44 and its prognostic and clinicopathological value in human malignancies:a meta-analysis

et al. 2020

View full text Add to dashboard Cite

Background: Previous researches have reported that tripartite motif-containing 44 (TRIM44) is related to the prognosis of multiple human tumors. This study was designed to systematically assess the prognostic value of TRIM44 in human malignancies and summarize its possible tumor-related mechanisms. Methods: The available databases were searched for eligible studies that evaluated the clinicopathological and prognostic roles of TRIM44 in patients with malignancies. The hazard ratios (HR) and odds ratios (OR) were combined to assess the predictive role of TRIM44 using Stata/SE 14.1 software. Results: A total of 1740 patients from thirteen original studies were finally included in this study. The results of the combined analysis showed that over-expression of TRIM44 protein was significantly correlated with shorter overall survival (OS) (HR = 1.94, 95% CI: 1.60-2.35) and worse disease-free survival (DFS) (HR = 2.13, 95% CI: 1.24-3.65) in cancer patients. Additionally, the combined ORs indicated that elevated expression level of TRIM44 protein was significantly associated with lymph node metastasis (OR = 2.69, 95% CI: 1.71-4.24), distant metastasis (OR = 10.35, 95% CI: 1.01-106.24), poor tumor differentiation (OR = 1.78, 95% CI: 1.03-3.09), increased depth of tumor invasion (OR = 2.72, 95% CI: 1.73-4.30), advanced clinical stage (OR = 2.75, 95% CI: 2.04-3.71), and recurrence (OR = 2.30, 95% CI: 1.34-3.95). Furthermore, analysis results using Gene Expression Profiling Interactive Analysis (GEPIA) showed that the expression level of TRIM44 mRNA was higher in most tumor tissues than in the corresponding normal tissues, and the relationship between TRIM44 mRNA level and prognosis in various malignant tumors also explored in GEPIA and OS analysis webservers. Conclusions: TRIM44 may serve as a valuable prognostic biomarker and a potential therapeutic target for patients with malignancies.

show abstract

OSgbm: An Online Consensus Survival Analysis Web Server for Glioblastoma

Cited by 23 publications

References 71 publications

Kinomic profile in patient-derived glioma cells during hypoxia reveals c-MET-PI3K dependency for adaptation

Kinomic profile in patient-derived glioma cells during hypoxia reveals c-MET-PI3K dependency for adaptation

Artificial intelligence to deep learning: machine intelligence approach for drug discovery

Expression of tripartite motif-containing 44 and its prognostic and clinicopathological value in human malignancies:a meta-analysis

Contact Info

Product

Resources

About