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Journal: Genes & Diseases
Article Title: Blockade of co-inhibitory receptor immune checkpoint protein TIM3/CD366 augments the anti-cancer activity of CAR-T therapy in solid tumors: An ovarian cancer example
doi: 10.1016/j.gendis.2025.101978
Figure Lengend Snippet: Specific IFN-γ and TNF-α release of T lymphocytes transduced with TIM-3-silenced HER2-specific chimeric antigen receptor (CAR) or HER2-specific CAR. (A, B) TIM-3-silenced CAR-T cells and control T cells were co-incubated with Galectin-9 + or Galectin-9 – SKOV3 tumor cells (E:T ratio 5:1 or 10:1). At 20 h after coculture, a specific enzyme-linked immunosorbent assay was used to analyze the supernatant for IFN-γ cytokine-release. Results were presented as mean ± standard deviation. (C, D) The detection of TNF-α in the same culture supernatant. Results were presented as mean ± standard deviation. ∗ P < 0.05 and ∗∗ P < 0.01.
Article Snippet: Human cervical cancer cell line HeLa, lentivirus packaging cell line HEK 293TD, and
Techniques: Transduction, Control, Incubation, Enzyme-linked Immunosorbent Assay, Standard Deviation
Journal: Genes & Diseases
Article Title: Blockade of co-inhibitory receptor immune checkpoint protein TIM3/CD366 augments the anti-cancer activity of CAR-T therapy in solid tumors: An ovarian cancer example
doi: 10.1016/j.gendis.2025.101978
Figure Lengend Snippet: TIM-3 silencing augmented the anti-tumor activity of chimeric antigen receptor-T (CAR-T) cells in vivo . 2 × 10 6 SKOV3 tumor cells expressing luciferase were intraperitoneally inoculated in a xenograft mouse model, and 7 days after inoculation, the 2 × 10 6 HER2-specific CAR-T kdTim-3 cells or CAR-T cells, or untreated T cells were intraperitoneally administered. (A, B) Tumor growth was monitored using an in vivo imaging system. (C) Survival curve of 80-day post-treatment. ∗ P < 0.05 and ∗∗ P < 0.01.
Article Snippet: Human cervical cancer cell line HeLa, lentivirus packaging cell line HEK 293TD, and
Techniques: Activity Assay, In Vivo, Expressing, Luciferase, In Vivo Imaging
Journal: Translational Oncology
Article Title: Integrating spatial and single-cell transcriptomics via machine learning to characterize efferocytosis in hepatocellular carcinoma prognosis and immunotherapy
doi: 10.1016/j.tranon.2026.102801
Figure Lengend Snippet: Identification and multi-level validation of TPI1 as a pivotal prognostic driver. (A) Lollipop chart showing the selection frequency of feature genes across 101 machine learning models, identifying TPI1 as a high-frequency core gene. (B) Univariate Cox regression analysis of candidate genes; TPI1 exhibited the most substantial Hazard Ratio (HR), characterizing it as a preeminent risk factor. (C) Expression profiling of TPI1 across malignant versus paracancerous tissues within the TCGA-LIHC discovery cohort (upper) and GSE14520 validation cohort (lower). (D) Kaplan-Meier overall survival curves comparing patients with high and low TPI1 expression in the TCGA-LIHC (top) and GSE14520 (bottom) cohorts. (E) Relative mRNA expression levels of TPI1 in the immortalized human hepatocyte line (MIHA) and HCC cell lines (Huh7, SMMC-7721) determined by RT-qPCR. (F) Representative Western blot images (left) and densitometric quantification (right) of TPI1 protein levels in MIHA, Huh7, and SMMC-7721 cells. GAPDH served as the internal loading control. Data are expressed as mean ± SD. ** P < 0.01, *** P < 0.001.
Article Snippet: The
Techniques: Biomarker Discovery, Selection, Expressing, Quantitative RT-PCR, Western Blot, Control