DAIXIN ZHAO¹, QINGYU WANG², JIANBO WANG^1,*

BIOCELL, Vol.48, No.2, pp. 271-281, 2024, DOI:10.32604/biocell.2023.029094

Abstract Background: Esophageal cancer (ESCA) is a common digestive tract tumor. As a result, optimization of the early diagnosis of ESCA and identifying the contributing prognostic genes is urgently required. Herein, the prognosis of mitochondrial autophagy-related genes was analyzed in different subtypes of ESCA, and prognostic models were constructed to identify the immune cell infiltration with significant differences between subtypes. Methods: The Cancer Genome Atlas database was searched to download 185 ESCA samples, covering gene expression level data and clinical follow-up data, and 179 samples from the Gene Expression Omnibus database for subsequent validation analysis. The consensus Cluster Plus analysis method… More >

QIOU GU¹, CHUILIN LAI¹, XIAO GUAN¹, JING ZHU², TIAN ZHAN¹, JIANPING ZHANG^1,*

BIOCELL, Vol.48, No.2, pp. 253-269, 2024, DOI:10.32604/biocell.2023.028336

Abstract Objectives: Colorectal cancer (CRC) is a serious threat to human health worldwide. Oxaliplatin is a platinum analog and is widely used to treat CRC. However, resistance to oxaliplatin restricts its effectiveness and application while its target recognition and mechanism of action also remain unclear. Therefore, we aimed to develop an oxaliplatin-resistant prognostic model to clarify these aspects. Methods: We first obtained oxaliplatin-resistant and parental cell lines, and identified oxaliplatin-resistant genes using RNA sequencing (RNA-seq) and differential gene analysis. We then acquired relevant data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Cox regression and Least Absolute… More > Graphic Abstract

HENG ZHANG^1,#, WENJING CHENG^2,#, HAIBO ZHAO², WEIDONG CHEN², QIUJIE ZHANG^2,*, QING-QING YU^2,*

Oncology Research, Vol.32, No.2, pp. 297-308, 2024, DOI:10.32604/or.2023.043138

Abstract Background: Colorectal cancer (CRC) belongs to the class of significantly malignant tumors found in humans. Recently, dysregulated fatty acid metabolism (FAM) has been a topic of attention due to its modulation in cancer, specifically CRC. However, the regulatory FAM pathways in CRC require comprehensive elucidation. Methods: The clinical and gene expression data of 175 fatty acid metabolic genes (FAMGs) linked with colon adenocarcinoma (COAD) and normal cornerstone genes were gathered through The Cancer Genome Atlas (TCGA)-COAD corroborating with the Molecular Signature Database v7.2 (MSigDB). Initially, crucial prognostic genes were selected by uni- and multi-variate Cox proportional regression analyses; then, depending… More >

BOWEN PENG¹, YUN GE¹, GANG YIN^2,3,*

BIOCELL, Vol.47, No.7, pp. 1519-1535, 2023, DOI:10.32604/biocell.2023.027026

Abstract Background: Tanshinone IIA, one of the main ingredients of Danshen, is used to treat hepatocellular carcinoma (HCC). However, potential targets of the molecule in the therapy of HCC are unknown. Methods: In this study, we collected the tanshinone IIA targets from public databases for investigation. We screened differentially expressed genes (DEGs) across HCC and normal tissues using mRNA expression profiles from The Cancer Genome Atlas (TCGA). Univariate Cox regression analysis and least absolute shrinkage and selection operator (LASSO) Cox regression models were used to identify and construct the prognostic gene signature. Results: Finally, we discovered common genes across tanshinone IIA… More >

Lei Hu^1,2,#, Meng Chen^2,3,#, Haiming Dai^2,3,4, Hongzhi Wang^2,3,4,*, Wulin Yang^2,3,4,*

Oncologie, Vol.24, No.4, pp. 803-822, 2022, DOI:10.32604/oncologie.2022.026419

Abstract Background and Aim: Hundreds of consistently altered metabolic genes have been identified in breast cancer (BC), but their prognostic value remains to be explored. Therefore, we aimed to build a prediction model based on metabolism-related genes (MRGs) to guide BC prognosis. Methods: Current work focuses on constructing a novel MRGs signature to predict the prognosis of BC patients using MRGs derived from the Virtual Metabolic Human (VMH) database, and expression profiles and clinicopathological data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Results: The 3-MRGs-signature constructed by SERPINA1, QPRT and PXDNL was found to be an… More >

GUODONG XIAO^1,#, FENG CHENG^1,#, JING YUAN¹, WEIPING LU¹, PEILI WANG², HUIJIE FAN^1,*

Oncology Research, Vol.30, No.1, pp. 35-51, 2022, DOI:10.32604/or.2022.026616

Abstract Distant metastasis is a major cause of increased mortality in breast cancer patients, but the mechanisms underlying breast cancer metastasis remain poorly understood. In this study, we aimed to identify a metastasis-related gene (MRG) signature for predicting progression in breast cancer. By screening using three regression analysis methods, a 9-gene signature (NOTCH1, PTP4A3, MMP13, MACC1, EZR, NEDD9, PIK3CA, F2RL1 and CCR7) was constructed based on an MRG set in the BRCA cohort from TCGA. This signature exhibited strong robustness, and its generalizability was verified in the Metabric and GEO cohorts. Of the nine MRGs, EZR is an oncogenic gene with… More >

HUILI ZHU¹, LINA XIAO¹, XIA YIN¹, SHIBING XIANG¹, CHUNHUI WANG^2,*

BIOCELL, Vol.46, No.10, pp. 2241-2256, 2022, DOI:10.32604/biocell.2022.020389

Abstract Pancreatic ductal adenocarcinoma (PDAC) is highly heterogeneous, making its prognosis prediction difficult. The arachidonic acid (AA) cascade is involved in carcinogenesis. Therefore, the metabolic enzymes of the AA cascade consist of lipoxygenases (LOXs), phospholipase A2s (PLA2s), and cyclooxygenases (COXs) along with their metabolic products, including leukotrienes. Nevertheless, the prognostic potential of AA metabolism-associated PDAC has not been explored. Herein, the mRNA expression patterns and the matching clinical information of individuals with PDAC were abstracted from online data resources. We employed the LASSO Cox regression model to develop a multigene clinical signature in the TCGA queue. The GEO queue and the… More >

JUNXIA LIU¹, KE PANG², FEI HE^2,*

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

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 strong predictive performance. And we… More >

RUI XU¹, QIBIAO WU¹, YUHAN GONG², YONGZHE WU¹, QINGJIA CHI^1,*, DA SUN^3,*

BIOCELL, Vol.46, No.5, pp. 1261-1288, 2022, DOI:10.32604/biocell.2022.018427

Abstract There is currently no effective solution to the problem of poor prognosis and recurrence of HCC. The technology of immunotherapy and prognosis of genetic material has made continuous progress in recent years. In the study, a 5-gene signature was established for the prognosis of HCC through biological information, and the immune infiltration of HCC patients was studied. After studied HCC patients’ immune infiltration, the paper screened the differential target genes of miR-126-3p in HCC downloaded from TCGA database, and uses WGCNA method to select the modular genes highly relevant to M2 macrophage. Then we use LASSO and COX regression analysis… More >

LIPING WANG^1,2, ZHOU LUO¹, MINMIN SUN³, QIUYUE YUAN⁴, YINGGANG ZOU⁵, DEYUAN FU^1,*

BIOCELL, Vol.46, No.3, pp. 595-606, 2022, DOI:10.32604/biocell.2022.017337

Abstract This work aimed to improve current prognostic signatures based on clinical stages in identifying high-risk patients of triple-negative breast cancer (TNBC), to allow patients with a high-risk score for specific treatment decisions. In this study, 396 TNBC samples from TCGA and GEO databases were included in genome-wide transcriptome analysis. The relationship between normalized gene expression values and survival data of patients was determined by Cox proportional hazards models in each dataset. The overlapped genes among all datasets were considered as a potential prognostic signature. The risk score was constructed based on individual genes and validated with three separate data sets… More >