1-11 of 11
Keywords: survival analysis
Follow your search
Access your saved searches in your account

Would you like to receive an alert when new items match your search?
Close Modal
Sort by
Biosci Rep (2022) 42 (11): BSR20221108.
Published: 21 November 2022
... BY) . Functional enrichment PPI and Regulatory biomolecules Renal cell carcinoma Survival analysis System biology Transcriptional profile and Comorbidity Renal cell carcinoma (RCC) appears to be the most common type of kidney cancer, with over 90% of occurrences [ 1 ]. It is a sophisticated carcinoma...
Includes: Supplementary data
Biosci Rep (2021) 41 (5): BSR20204148.
Published: 27 May 2021
...-protein interactions network Survival analysis Head and neck cancer is one of the most prevalent malignancies worldwide, with an estimated 600,000 new cases every year [ 1 ]. The most common type of head and neck cancer is head and neck squamous cell carcinoma (HNSCC), which is characterized...
Biosci Rep (2021) 41 (4): BSR20210280.
Published: 16 April 2021
... with that in paracancerous tissues in CRC patients, HCT116 xenograft, and RKO xenograft. High NLRP3 level correlated with the advanced TNM classification of malignant tumors, the occurrence of distant metastasis, vascular invasion, and positive lymph nodes. Furthermore, Kaplan–Meier survival analysis revealed that a high...
Biosci Rep (2021) 41 (1): BSR20200869.
Published: 06 January 2021
... by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY) . bioinformatics analysis DEGs non-TNBC survival analysis TNBC Breast cancer (BC) is the most common malignant disease worldwide and remains a major...
Includes: Supplementary data
Biosci Rep (2021) 41 (1): BSR20201920.
Published: 05 January 2021
...-cancer survival analysis Protein kinases are a common way of regulating information transduction in organisms, which play a crucial role in the process of cell signal by transferring a phosphate from adenosine triphosphate (ATP) to the target proteins [ 1 , 2 ]. It never really came...
Includes: Supplementary data
Biosci Rep (2019) 39 (12): BSR20192590.
Published: 20 December 2019
... access article published by Portland Press Limited on behalf of the Biochemical Society and distributed under the Creative Commons Attribution License 4.0 (CC BY) . osteosarcoma prognosis red cell distribution width Survival analysis In the present study, the enrolled patients were...
Biosci Rep (2019) 39 (4): BSR20190083.
Published: 02 April 2019
... that the up-regulated genes: FN1, SDC4, NMU, LPAR5 and the down-regulated genes: BCL2 and CXCL12 were key genes. Survival analysis indicated that the high expression of FN1 and NMU genes significantly decreased disease-free survival of patients with thyroid carcinoma. In conclusion, the genes and pathways...
Includes: Supplementary data
Biosci Rep (2019) 39 (2): BSR20181845.
Published: 22 February 2019
...), and lysophosphatidic acid receptor 3 (LPAR3) were hub nodes. CDK1 interacting with PLK1 and FOS, and LPAR3 interacting with FOS and SAA1 were found in the PPI network. Amongst the 40 network modules, 4 modules were with scores not less than 10. Survival analysis showed that anterior gradient 2 ( AGR2 ) and RLN3 could...
Biosci Rep (2019) 39 (1): BSR20181293.
Published: 03 January 2019
... based on gene expression profiles from GSE32018, GSE56315, and The Cancer Genome Atlas (TCGA) DLBC. Overlapping DEGs were then evaluated for gene ontology (GO), pathways enrichment, DNA methylation, protein–protein interaction (PPI) network analysis as well as survival analysis. Seventy-four up...
Biosci Rep (2018) 38 (6): BSR20181441.
Published: 09 November 2018
... pathway, and valine, leucine and isoleucine degradation pathway. PPI network analysis showed that CDK1, CCNB1, CCNB2, MAD2L1, ACACB, IGF1, TOP2A , and EHHADH were crucial genes. Survival analysis suggested that the high expression of CDK1, CCNB1, CCNB2, MAD2L1 , and TOP2A significantly decreased...
Biosci Rep (2018) 38 (4): BSR20180580.
Published: 03 July 2018
... 1.821 (1.037–3.198) 0.037 NI, not included in multivariate survival analysis. Bold P- values indicate statistical significance. Tumor growth and metastasis require that glucose metabolism be reprogrammed to glycolysis. Because CXCL1 expression was correlated with larger tumor sizes...