JUNXIA LIU¹, KE PANG², FEI HE^2,*

BIOCELL, Vol.46, No.7, pp. 1661-1673, 2022, DOI:10.32604/biocell.2022.018023 - 17 March 2022

Abstract Breast cancer is one of the most common cancers in the world and seriously threatens the health of women worldwide. Prognostic models based on immune-related genes help to improve the prognosis prediction and clinical treatment of breast cancer patients. In the study, we used weighted gene co-expression network analysis to construct a co-expression network to screen out highly prognostic immune-related genes. Subsequently, the prognostic immune-related gene signature was successfully constructed from highly immune-related genes through COX regression and LASSO COX analysis. Survival analysis and time receiver operating characteristic curves indicate that the prognostic signature has More >

LU YANG^1,#, YUN LIU^1,#, BOKE ZHANG², MENGSI YU³, FEN HUANG¹, YANG WEN¹, JIANGZHENG ZENG¹, YANDA LU¹, CHANGCHENG YANG¹

BIOCELL, Vol.46, No.5, pp. 1215-1243, 2022, DOI:10.32604/biocell.2022.018221 - 06 January 2022

Abstract The aim of this study was to reveal genes associated with breast cancer metastasis, to investigate their intrinsic relationship with immune cell infiltration in the tumor microenvironment, and to screen for prognostic biomarkers. Gene expression data of breast cancer patients and their metastases were downloaded from the GEO, TCGA database. R language package was used to screen for differentially expressed genes, enrichment analysis of genes, PPI network construction, and also to elucidate key genes for diagnostic and prognostic survival. Spearman’s r correlation was used to analyze the correlation between key genes and infiltrating immune cells.… More >

YONGHUI YU^1,#, SONGHUI XU^2,#, ERYONG ZHAO^3,#, YONGSHUN DONG¹, JINBIN CHEN¹, BOQI RAO¹, JIE ZENG⁴, LEI YANG¹, JIACHUN LU¹, FUMAN QIU^1,4,*

BIOCELL, Vol.46, No.4, pp. 999-1011, 2022, DOI:10.32604/biocell.2022.017004 - 15 December 2021

Abstract The outcomes of ovarian cancer are complicated and usually unfavorable due to their diagnoses at a late stage. Identifying the efficient prognostic biomarkers to improve the survival of ovarian cancer is urgently warranted. The survival-related pseudogenes retrieved from the Cancer Genome Atlas database were screened by univariate Cox regression analysis and further assessed by least absolute shrinkage and selection operator (LASSO) method. A risk score model based on the prognostic pseudogenes was also constructed. The pseudogene-mRNA regulatory networks were established using correlation analysis, and their potent roles in the ovarian cancer progression were uncovered by… More >

MENG FANG¹, JING GUO¹, HAIPING WANG¹, ZICHANG YANG², HAN ZHAO^1,*, QINGJIA CHI²

BIOCELL, Vol.46, No.2, pp. 401-415, 2022, DOI:10.32604/biocell.2022.016989 - 20 October 2021

Abstract Hepatocellular carcinoma (HCC) is a common immunogenic malignant tumor. Although the new strategies of immunotherapy and targeted therapy have made considerable progress in the treatment of HCC, the 5-year survival rate of patients is still very low. The identification of new prognostic signatures and the exploration of the immune microenvironment are crucial to the optimization and improvement of molecular therapy strategies. We studied the potential clinical benefits of the inflammation regulator miR-93-3p and mined its target genes. Weighted gene co-expression network analysis (WGCNA), univariate and multivariate COX regression and the LASSO COX algorithm are employed… More >