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Highly multiplexed imaging reveals prognostic immune and stromal spatial biomarkers in breast cancer.
JCI Insight
January 2025
Department of Biomedical Engineering, Oregon Health and Science University, Portland, United States of America.
Spatial profiling of tissues promises to elucidate tumor-microenvironment interactions and generate prognostic and predictive biomarkers. We analyzed single-cell, spatial data from three multiplex imaging technologies: cyclic immunofluorescence (CycIF) data we generated from 102 breast cancer patients with clinical follow-up, and publicly available imaging mass cytometry and multiplex ion-beam imaging datasets. Similar single-cell phenotyping results across imaging platforms enabled combined analysis of epithelial phenotypes to delineate prognostic subtypes among estrogen-receptor positive (ER+) patients.
View Article and Find Full Text PDFNon small cell lung cancer with SMARCA4 deficiency harboring rare EGFR mutations exhibited significant tumor response when treated with afatinib: a case report.
Front Med
January 2025
Department of Medical Oncology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou, 310016, China.
SMARCA4-deficient non small cell lung cancer (SMARCA4-dNSCLC) has recently garnered increasing attention due to its high malignancy and poor prognosis. The literature suggests that in non small cell lung cancer (NSCLC), the loss of SMARCA4 frequently co-occurs with mutations in KRAS, KEAP1, and STK11 rather than in EGFR, ALK, and ROS1. Herein, we present the first documented case of SMARCA4-dNSCLC accompanied with rare mutations of EGFR exon 20 S768I and exon 18 G719X.
View Article and Find Full Text PDFThe multifaceted roles of aldolase A in cancer: glycolysis, cytoskeleton, translation and beyond.
Hum Cell
January 2025
Institute of Translational Medicine, Medical College, Yangzhou University, No. 136 Jiangyangzhonglu, Yangzhou, 225009, Jiangsu, China.
Cancer, a complicated disease characterized by aberrant cellular metabolism, has emerged as a formidable global health challenge. Since the discovery of abnormal aldolase A (ALDOA) expression in liver cancer for the first time, its overexpression has been identified in numerous cancers, including colorectal cancer (CRC), breast cancer (BC), cervical adenocarcinoma (CAC), non-small cell lung cancer (NSCLC), gastric cancer (GC), hepatocellular carcinoma (HCC), pancreatic cancer adenocarcinoma (PDAC), and clear cell renal cell carcinoma (ccRCC). Moreover, ALDOA overexpression promotes cancer cell proliferation, invasion, migration, and drug resistance, and is closely related to poor prognosis of patients with cancer.
View Article and Find Full Text PDFTargeting CHEK1: Ginsenosides-Rh2 and Cu2O@G-Rh2 nanoparticles in thyroid cancer.
Cell Biol Toxicol
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Department of Radiology, Shengjing Hospital of China Medical University, No. 36 Sanhao Street, Heping District, Shenyang, 110004, Liaoning Province, China.
Thyroid cancer (THCA) is an increasingly common malignant tumor of the endocrine system, with its incidence rising steadily in recent years. For patients who experience recurrence or metastasis, treatment options are relatively limited, and the prognosis is poor. Therefore, exploring new therapeutic strategies has become particularly urgent.
View Article and Find Full Text PDFMachine learning validation of a simple prediction model for the correlation between advanced age and clinical outcomes in patients with aneurysmal subarachnoid hemorrhage.
Neurosurg Rev
January 2025
Kobayashi Hospital, 510 Imaichi, Izumo City, Shimane, 693-0001, Japan.
Adverse effects of advanced age and poor initial neurological status on outcomes of patients with aneurysmal subarachnoid hemorrhage (SAH) have been documented. While a predictive model of the non-linear correlation between advanced age and clinical outcome has been reported, no previous model has been validated. Therefore, we created a prediction model of the non-linear correlation between advanced age and clinical outcome by machine learning and validated it using a separate cohort.
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