Wenjie Zhou^#, Jiawei Ding^#, Danfeng Xu^*

Oncology Research, Vol.33, No.11, pp. 3493-3522, 2025, DOI:10.32604/or.2025.066783 - 22 October 2025

Abstract Objectives: Despite the fact that prostate cancer is one of the most common tumors in men, this study intends to evaluate the predictive significance of immune and metabolic genes in prostate cancer using multi-omics data and experimental validation. Methods: The research developed and validated a prognostic model utilizing The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases, integrating immune and metabolic gene sets. Additionally, the prognostic gene Adenylate Kinase 5 (AK5) was analyzed in prostate cancer tissue microarrays from Ruijin Hospital. The functional role of the AK5 gene was validated through knockdown and… More >

Jun Li¹, Kangmin Yu¹, Zhiyong Chen¹, Dan Xing², Binshan Zha¹, Wentao Xie¹, Huan Ouyang¹, Changjun Yu^3,*

Oncology Research, Vol.33, No.11, pp. 3469-3492, 2025, DOI:10.32604/or.2025.066193 - 22 October 2025

Abstract Objectives: Colorectal cancer (CRC) remains a major contributor to global cancer mortality, ranking second worldwide for cancer-related deaths in 2022, and is characterized by marked heterogeneity in prognosis and therapeutic response. We sought to construct a machine-learning prognostic model based on a complement-related risk signature (CRRS) and to situate this signature within the CRC immune microenvironment. Methods: Transcriptomic profiles with matched clinical annotations from TCGA and GEO CRC cohorts were analyzed. Prognostic CRRS genes were screened using Cox proportional hazards modeling alongside machine-learning procedures. A random survival forest (RSF) predictor was trained and externally validated.… More >

Gaofeng Pan^1,2,3, Jiali Li^1,2, Weijie Sun⁴, Jiayu He^1,2, Maoying Fu³, Yufeng Gao^1,2,*

Oncology Research, Vol.33, No.8, pp. 1991-2011, 2025, DOI:10.32604/or.2025.063993 - 18 July 2025

Abstract Aims: The aim of this study is to develop a prognostic model for hepatocellular carcinoma (HCC) using stemness-related genes (SRGs), while also pinpointing and validating pivotal genes associated with this process. Methods: Utilizing the TCGA and ICGC database, a prognostic stemness-related scores (SRS) for HCC through a combination of WGCNA and machine learning. Bioinformatics analysis evaluated tumor immune infiltration characteristics and drug sensitivity in different SRS subgroups, identifying the key gene TOMM40L. qRT-PCR and IHC were employed to detect the expression level of TOMM40 L. Kaplan-Meier survival analysis assessed the prognostic value of TOMM40L in… More >

Zhun Yu^1,2, Jie Wang^1,2, Guoping Xu^1,2,*

Oncology Research, Vol.33, No.8, pp. 2013-2035, 2025, DOI:10.32604/or.2025.061757 - 18 July 2025

Abstract Objectives: Triple-negative breast cancer (TNBC) presents a major treatment challenge due to its aggressive behavior. The dysfunction of the Golgi apparatus (GA) contributes to the development of various cancers. This study aimed to utilize GA-related genes (GARGs) to forecast the prognosis and immune profile of TNBC. Methods: The data were downloaded from The Cancer Genome Atlas (TCGA) database, including 175 TNBC and 99 healthy samples. The differentially expressed GARGs (DEGARGs) were analyzed using the TCGA biolinks package. The patients with TNBC were classified into two clusters utilizing the ConsensusClusterPlus package according to prognosis-related DEGARGs, followed by… More >

YOUYANG HU^1,#, YISHU LUO^1,#, TIANYUE XIE¹, YUEHUA CHEN^1,2, JUN ZHAO¹, WEICHAO JI³, ZHIWEI YAN³, SITONG QIU³, KEXIN GAO³, HAIXIA ZHU⁴, LIMIN MA^1,*, QIYOU YIN^1,*

Oncology Research, Vol.33, No.7, pp. 1695-1708, 2025, DOI:10.32604/or.2025.060021 - 26 June 2025

Abstract Objective: Neuroblastoma (NB) is frequently associated with high-risk pediatric cases that demonstrate limited response to cisplatin, contributing to a poor prognosis. Recent studies have explored the role of tumor cell senescence in increasing sensitivity to this chemotherapy agent. This study aims to identify genes related to cell senescence in children diagnosed with NB, evaluate their influence on cisplatin sensitivity, and investigate potential strategies to enhance the efficacy of chemotherapy. Methods: Gene expression profiles and clinical data were obtained for 498 NB patients from the GEO database (GSE49710). The study focused on identifying genes that were… More >

LINGJIE XU¹, YIQIN XIA¹, QIN QIN¹, GUIQUN WANG¹, KAI TAO², WEI WEI^1,*

Oncology Research, Vol.33, No.7, pp. 1649-1666, 2025, DOI:10.32604/or.2025.056176 - 26 June 2025

Abstract Background: The centrosome, a crucial cellular structure involved in the mitotic process of eukaryotic cells, plays a significant role in tumor progression by regulating the growth and differentiation of neoplastic cells. This makes the centrosome a promising target for therapeutic strategies in cancer treatment. Methods: Utilizing data from the TCGA database, we identified centrosome-related genes and constructed a prognostic model for 518 lung adenocarcinoma patients. Prognosis-associated genes were initially screened using univariate Cox regression, with overfitting minimized by applying LASSO regression to remove collinearity. Finally, a set of 12 genes was selected through multivariable Cox… More >

Yinghui Ye^1,#, Yulou Luo^2,#, Yutian Sun³, Yujie Zhang¹, Jiaxin Lin⁴, Ziling Yang⁵, Anping Xu^6,*, Bei Xue^1,*

Oncology Research, Vol.33, No.6, pp. 1383-1404, 2025, DOI:10.32604/or.2025.062584 - 29 May 2025

Abstract Objectives: The tumorigenic progression of Lung adenocarcinoma (LUAD), the predominant NSCLC subtype, is predominantly driven by co-occurring mutations in KRAS proto-oncogene (KRAS)/Tumor protein p53 (TP53). However, their impact on tumor microenvironment (TME) heterogeneity, particularly neutrophil dynamics, remains poorly understood. This present study aims to elucidate how KRAS/TP53 mutations reprogram the TME and develop a neutrophil-centric prognostic signature for LUAD. Methods: Leveraging single-cell RNA sequencing data and transcriptome data, neutrophil subpopulations were identified using Seurat and CellChat R packages, with trajectory analysis via Monocle2 R package. High-dimensional weighted gene co-expression network analysis (hdWGCNA), univariate Cox regression,… More >

KE TIAN¹, ZHIPENG LI², XIANGYU ZHAI^2,3, HUAXIN ZHOU², HUI YAO^1,*

Oncology Research, Vol.33, No.3, pp. 617-630, 2025, DOI:10.32604/or.2024.049772 - 28 February 2025

Abstract Background: Primary liver cancer poses a significant global health burden, with projections indicating a surpassing of one million cases by 2025. Cuproptosis, a copper-dependent mechanism of cell death, plays a crucial role in the pathogenesis, progression, and prognosis of various cancers, including hepatocellular carcinoma (HCC). Purpose: This study aimed to develop a prognostic model for HCC based on cuproptosis-related genes, utilizing clinical data and gene expression profiles from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Materials and Methods: Clinical features and gene expression data of HCC patients were collected from publicly available More >

ATIEH POURBAGHERI-SIGAROODI¹, MAJID MOMENY², NIMA REZAEI^3,4,5, FATEMEH FALLAH^1,*, DAVOOD BASHASH^6,*

BIOCELL, Vol.48, No.12, pp. 1781-1804, 2024, DOI:10.32604/biocell.2024.055848 - 30 December 2024

Abstract Background: Despite progress in therapeutic strategies, treatment failure in hepatocellular carcinoma (HCC) remains a major challenge, resulting in low survival rates. The presence of bacteria and the host’s immune response to bacteria can influence the pathogenesis and progression of HCC. We developed a risk model based on bacterial response-related genes (BRGs) using gene sets from molecular signature databases to identify new markers for predicting HCC outcomes and categorizing patients into different risk groups. Methods: The data from the Cancer Genome Atlas (TCGA) portal was retrieved, and differentially expressed BRGs were identified. Uni- and multivariate Cox… More >

ZHEN WANG¹, JUN FU¹, SAISAI ZHU¹, HAODONG TANG², KUI SHI¹, JIHUA YANG³, MENG WANG³, MENGGE WU¹, DUNFENG QI^1,*

Oncology Research, Vol.32, No.12, pp. 1851-1866, 2024, DOI:10.32604/or.2024.055286 - 13 November 2024

Abstract Background: Pancreatic ductal adenocarcinoma (PDAC) has a rich and complex tumor immune microenvironment (TIME). M2 macrophages are among the most extensively infiltrated immune cells in the TIME and are necessary for the growth and migration of cancers. However, the mechanisms and targets mediating M2 macrophage infiltration in pancreatic cancer remain elusive. Methods: The M2 macrophage infiltration score of patients was assessed using the xCell algorithm. Using weighted gene co-expression network analysis (WGCNA), module genes associated with M2 macrophages were identified, and a predictive model was designed. The variations in immunological cell patterns, cancer mutations, and… More > Graphic Abstract