ZHIGUO WANG^1,2,#, KUNLIN LI^2,#, CONGHUA LU², MINGXIA FENG², CAIYU LIN², GUOFANG YIN¹, DAN LUO¹, WENYI LIU³, KAIYU JIN⁴, YUANYAO DOU², DI WU², JIE ZHENG², KEJUN ZHANG⁵, LI LI^2,*, XIANMING FAN^1,*

Oncology Research, Vol.32, No.10, pp. 1637-1648, 2024, DOI:10.32604/or.2024.052664 - 18 September 2024

Abstract Background: Metformin has pleiotropic effects beyond glucose reduction, including tumor inhibition and immune regulation. It enhanced the anti-tumor effects of programmed cell death protein 1 (PD-1) inhibitors in serine/threonine kinase 11 (STK11) mutant non-small cell lung cancer (NSCLC) through an axis inhibition protein 1 (AXIN1)-dependent manner. However, the alterations of tumor metabolism and metabolites upon metformin administration remain unclear. Methods: We performed untargeted metabolomics using liquid chromatography (LC)-mass spectrometry (MS)/MS system and conducted cell experiments to verify the results of bioinformatics analysis. Results: According to the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway database, most… More > Graphic Abstract

WEIGANG XIU^1,#, XINGYU LIU^1,2,#, KAIXIN HU^1,2, QIN ZHANG^3,*, HUASHAN SHI^4,*

Oncology Research, Vol.32, No.10, pp. 1613-1621, 2024, DOI:10.32604/or.2024.047933 - 18 September 2024

Abstract Elevated serum cholesterol metabolism is associated with a reduced risk of lung cancer. Disrupted cholesterol metabolism is evident in both lung cancer patients and tumor cells. Inhibiting tumor cell cholesterol uptake or biosynthesis pathways, through the modulation of receptors and enzymes such as liver X receptor and sterol-regulatory element binding protein 2, effectively restrains lung tumor growth. Similarly, promoting cholesterol excretion yields comparable effects. Cholesterol metabolites, including oxysterols and isoprenoids, play a crucial role in regulating cholesterol metabolism within tumor cells, consequently impacting cancer progression. In lung cancer patients, both the cholesterol levels in the… More >

Pouyan Razmjouei¹, Elaheh Moharamkhani², Mohamad Hasanvand³, Maryam Daneshfar⁴, Mohammad Shokouhifar^5,*

CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3855-3880, 2024, DOI:10.32604/cmc.2024.054460 - 12 September 2024

Abstract This study investigates the application of deep learning, ensemble learning, metaheuristic optimization, and image processing techniques for detecting lung and colon cancers, aiming to enhance treatment efficacy and improve survival rates. We introduce a metaheuristic-driven two-stage ensemble deep learning model for efficient lung/colon cancer classification. The diagnosis of lung and colon cancers is attempted using several unique indicators by different versions of deep Convolutional Neural Networks (CNNs) in feature extraction and model constructions, and utilizing the power of various Machine Learning (ML) algorithms for final classification. Specifically, we consider different scenarios consisting of two-class colon… More >

Oncology Research Editorial Office

Oncology Research, Vol.32, No.9, pp. 1527-1527, 2024, DOI:10.32604/or.2024.056120 - 23 August 2024

Abstract This article has no abstract. More >

TATIANA KURILO^*, REBECCA D. PENTZ

Oncology Research, Vol.32, No.9, pp. 1401-1406, 2024, DOI:10.32604/or.2024.050266 - 23 August 2024

Abstract Objectives: Rural patients have poor cancer outcomes and clinical trial (CT) enrollment compared to urban patients due to attitudinal, awareness, and healthcare access differential. Knowledge of cancer survival disparities and CT enrollment is important for designing interventions and innovative approaches to address the stated barriers. The study explores the potential disparities in cancer survival rates and clinical trial enrollments in rural and urban breast and lung cancer patients. Our hypotheses are that for both cancer types, urban cancer patients will have longer 5-year survival rates and higher enrollment rates in clinical trials than those in… More >

Oncology Research Editorial Office

Oncology Research, Vol.32, No.8, pp. 1377-1377, 2024, DOI:10.32604/or.2024.055033 - 17 July 2024

Abstract This article has no abstract. More >

XIAOBI HUANG^1,#, CHUNYUAN CHEN², YONGYANG CHEN^1,#, HONGLIAN ZHOU¹, YONGHUA CHEN¹, ZHONG HUANG¹, YULIU XIE¹, BAIYANG LIU¹, YUDONG GUO¹, ZHIXIONG YANG¹, GUANGHUA CHEN^3,*, WENMEI SU^1,4,*

Oncology Research, Vol.32, No.7, pp. 1185-1195, 2024, DOI:10.32604/or.2023.030771 - 20 June 2024

Abstract Background: Long non-coding RNAs are important regulators in cancer biology and function either as tumor suppressors or as oncogenes. Their dysregulation has been closely associated with tumorigenesis. LINC00265 is upregulated in lung adenocarcinoma and is a prognostic biomarker of this cancer. However, the mechanism underlying its function in cancer progression remains poorly understood. Methods: Here, the regulatory role of LINC00265 in lung adenocarcinoma was examined using lung cancer cell lines, clinical samples, and xenografts. Results: We found that high levels of LINC00265 expression were associated with shorter overall survival rate of patients, whereas knockdown of LINC00265 inhibited proliferation… More >

Iftikhar Naseer^1,2, Tehreem Masood^1,2, Sheeraz Akram^3,*, Zulfiqar Ali⁴, Awais Ahmad³, Shafiq Ur Rehman³, Arfan Jaffar^1,2

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4963-4977, 2024, DOI:10.32604/cmc.2024.050204 - 20 June 2024

Abstract Lung cancer is a leading cause of global mortality rates. Early detection of pulmonary tumors can significantly enhance the survival rate of patients. Recently, various Computer-Aided Diagnostic (CAD) methods have been developed to enhance the detection of pulmonary nodules with high accuracy. Nevertheless, the existing methodologies cannot obtain a high level of specificity and sensitivity. The present study introduces a novel model for Lung Cancer Segmentation and Classification (LCSC), which incorporates two improved architectures, namely the improved U-Net architecture and the improved AlexNet architecture. The LCSC model comprises two distinct stages. The first stage involves… More >

Mutasem K. Alsmadi^*

CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 5175-5200, 2024, DOI:10.32604/cmc.2024.044065 - 20 June 2024

Abstract Lung cancer is among the most frequent cancers in the world, with over one million deaths per year. Classification is required for lung cancer diagnosis and therapy to be effective, accurate, and reliable. Gene expression microarrays have made it possible to find genetic biomarkers for cancer diagnosis and prediction in a high-throughput manner. Machine Learning (ML) has been widely used to diagnose and classify lung cancer where the performance of ML methods is evaluated to identify the appropriate technique. Identifying and selecting the gene expression patterns can help in lung cancer diagnoses and classification. Normally,… More >

JIANBO ZHOU^1,2,3, FENG WAN^2,4, BIN XIAO⁵, XIN LI¹, CHENG PENG^2,*, FU PENG^1,3,*

Oncology Research, Vol.32, No.5, pp. 943-953, 2024, DOI:10.32604/or.2023.044775 - 23 April 2024

Abstract Breast and lung cancers are the leading causes of mortality and most frequently diagnosed cancers in women and men, respectively, worldwide. Although the antitumor activity of chalcones has been extensively studied, the molecular mechanisms of isoliquiritigenin analog 2', 4', 4-trihydroxychalcone (metochalcone; TEC) against carcinomas remain less well understood. In this study, we found that TEC inhibited cell proliferation of breast cancer BT549 cells and lung cancer A549 cells in a concentration-dependent manner. TEC induced cell cycle arrest in the S-phase, cell migration inhibition in vitro, and reduced tumor growth in vivo. Moreover, transcriptomic analysis revealed that TEC More > Graphic Abstract