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human hepatocyte line miha  (Procell Inc)

 
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    Structured Review

    Procell Inc human hepatocyte line miha
    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 <t>hepatocyte</t> line <t>(MIHA)</t> 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.
    Human Hepatocyte Line Miha, supplied by Procell Inc, used in various techniques. Bioz Stars score: 86/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/result/human hepatocyte line miha/product/Procell Inc
    Average 86 stars, based on 1 article reviews
    human hepatocyte line miha - by Bioz Stars, 2026-06
    86/100 stars

    Images

    1) Product Images from "Integrating spatial and single-cell transcriptomics via machine learning to characterize efferocytosis in hepatocellular carcinoma prognosis and immunotherapy"

    Article Title: Integrating spatial and single-cell transcriptomics via machine learning to characterize efferocytosis in hepatocellular carcinoma prognosis and immunotherapy

    Journal: Translational Oncology

    doi: 10.1016/j.tranon.2026.102801

    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.
    Figure Legend 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.

    Techniques Used: Biomarker Discovery, Selection, Expressing, Quantitative RT-PCR, Western Blot, Control



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    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 <t>hepatocyte</t> line <t>(MIHA)</t> 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.
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    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 <t>hepatocyte</t> line <t>(MIHA)</t> 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.
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    Image Search Results


    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.

    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 human HCC cell lines (Huh7 and SMMC-7721) and the immortalized human hepatocyte line MIHA were procured from Procell Life Science & Technology Co., Ltd. (Wuhan, China).

    Techniques: Biomarker Discovery, Selection, Expressing, Quantitative RT-PCR, Western Blot, Control