Academic/Professional Position (Other)
Postdoctoral Fellow
Presentation Type
Oral Presentation
Discipline Track
Biomedical Science
Cancer Progression and Metastatic
Abstract Type
Research/Clinical
Abstract
Background: Hepatocellular carcinoma (HCC) constitutes a substantial portion, accounting for 85% to 90% of liver cancers worldwide. Notably, within the Hispanic population, liver cancer mortality rates are notably higher, particularly evident in regions like the South Texas Rio Grande Valley (RGV), where nearly 90% of the populace is Latino/Hispanic. This region grapples with poverty affecting nearly 30% of its residents, coupled with elevated rates of obesity, diabetes, and low-income households, thereby fostering a prevalent environment of stress. Stress can profoundly impact cancer outcomes by compromising immune functionality and triggering inflammatory responses, potentially impairing surveillance against oncogenic triggers. The activation of the Hypothalamic-Pituitary-Adrenal (HPA) axis during stress results in the release of stress hormones like leptin and cortisol, possibly influencing cancer tumor progression and growth. The cumulative effects of stress, anxiety, and depression can detrimentally affect liver health, impairing hepatic cell function and regeneration, ultimately expediting HCC progression. This study has identified the Long Non-coding RNA (LncRNA) MALAT1 as overexpressed under stress conditions. The Cancer Genome Atlas (TCGA) database, we've analyzed lncRNA expression in HCC patient cohorts. We want to comprehend the proteins and pathways influenced by stress-associated lncRNA MALAT1 expression in HCC, thereby illuminating potential mechanisms driving HCC progression under stressful conditions.
Methods: We engineered a stable cell line (SK-Hep1_Malat1) overexpressing LncRNA Malat1, utilizing transient transfection, puromycin selection, and FACS enrichment for GFP expression. Additionally, a vector-only control cell line was established for comparative analysis. TCGA data encompassing HCC patient tumors was analyzed to explore LncRNA Malat1's influence. Subsequently, High Throughput Nano-Liquid Chromatography Mass Spectrometry (LC-MS) was employed to identify proteins and pathways modulated in stable LncRNA Malat1-expressing cell lines. Further analyses included utilizing the Human Protein Atlas, Mechanism, Pathway Enrichment analyses, and STRING pathway analysis.
Results: The Cancer Genome Atlas (TCGA) database revealed heigh LncRNA Malat1 levels in HCC patient tumors compared to control tissues. Additionally, LncRNA Malat1 expression exhibited significant correlations with HCC progression, metastasis, and unfavorable clinical outcomes. We observed an upregulation of LncRNA MALAT1, ZNF384, and YB-1 genes to varying degrees, influenced by exposure to stress hormones (cortisol, leptin), and treatment duration. Furthermore, we identified the proteins and pathways modulated by lncRNA Malat1 overexpression in the SK-Hep1 cell line. The comprehensive results detailing these molecular alterations and pathway modulations will be presented.
Conclusion: The identified differential proteins will undergo rigorous validation using quantitative techniques such as western blotting, immunoprecipitation, co-immunoprecipitation, and immune-fluorescence assays. Future investigations will encompass skimmed assessments to deliver deeper into the modulated pathways. Our findings illuminate the upregulation of oncogenic long non-coding RNA and transcription factors, outlining a signaling pathway model influenced by stress factors in HCC cell lines. Understanding the pivotal role of stress factors in oncogenicity and HCC progression holds promise in developing therapeutics, particularly for populations where stress contributes significantly to cancer disparities. These insights offer potential avenues for targeted interventions aimed at mitigating the impact of stress on HCC, potentially improving outcomes for affected populations.
Recommended Citation
Hussain, Mohammad Shabir; Leslie, Sophia; Sanchez, Amayrani; Lopez, Samantha; Doxtater, Kyle; and Tripathi, Manish Kumar, "Proteomic analysis of stress associated factor overexpression in Hepatocellular carcinoma" (2024). Research Symposium. 23.
https://scholarworks.utrgv.edu/somrs/2024/talks/23
Proteomic analysis of stress associated factor overexpression in Hepatocellular carcinoma
Background: Hepatocellular carcinoma (HCC) constitutes a substantial portion, accounting for 85% to 90% of liver cancers worldwide. Notably, within the Hispanic population, liver cancer mortality rates are notably higher, particularly evident in regions like the South Texas Rio Grande Valley (RGV), where nearly 90% of the populace is Latino/Hispanic. This region grapples with poverty affecting nearly 30% of its residents, coupled with elevated rates of obesity, diabetes, and low-income households, thereby fostering a prevalent environment of stress. Stress can profoundly impact cancer outcomes by compromising immune functionality and triggering inflammatory responses, potentially impairing surveillance against oncogenic triggers. The activation of the Hypothalamic-Pituitary-Adrenal (HPA) axis during stress results in the release of stress hormones like leptin and cortisol, possibly influencing cancer tumor progression and growth. The cumulative effects of stress, anxiety, and depression can detrimentally affect liver health, impairing hepatic cell function and regeneration, ultimately expediting HCC progression. This study has identified the Long Non-coding RNA (LncRNA) MALAT1 as overexpressed under stress conditions. The Cancer Genome Atlas (TCGA) database, we've analyzed lncRNA expression in HCC patient cohorts. We want to comprehend the proteins and pathways influenced by stress-associated lncRNA MALAT1 expression in HCC, thereby illuminating potential mechanisms driving HCC progression under stressful conditions.
Methods: We engineered a stable cell line (SK-Hep1_Malat1) overexpressing LncRNA Malat1, utilizing transient transfection, puromycin selection, and FACS enrichment for GFP expression. Additionally, a vector-only control cell line was established for comparative analysis. TCGA data encompassing HCC patient tumors was analyzed to explore LncRNA Malat1's influence. Subsequently, High Throughput Nano-Liquid Chromatography Mass Spectrometry (LC-MS) was employed to identify proteins and pathways modulated in stable LncRNA Malat1-expressing cell lines. Further analyses included utilizing the Human Protein Atlas, Mechanism, Pathway Enrichment analyses, and STRING pathway analysis.
Results: The Cancer Genome Atlas (TCGA) database revealed heigh LncRNA Malat1 levels in HCC patient tumors compared to control tissues. Additionally, LncRNA Malat1 expression exhibited significant correlations with HCC progression, metastasis, and unfavorable clinical outcomes. We observed an upregulation of LncRNA MALAT1, ZNF384, and YB-1 genes to varying degrees, influenced by exposure to stress hormones (cortisol, leptin), and treatment duration. Furthermore, we identified the proteins and pathways modulated by lncRNA Malat1 overexpression in the SK-Hep1 cell line. The comprehensive results detailing these molecular alterations and pathway modulations will be presented.
Conclusion: The identified differential proteins will undergo rigorous validation using quantitative techniques such as western blotting, immunoprecipitation, co-immunoprecipitation, and immune-fluorescence assays. Future investigations will encompass skimmed assessments to deliver deeper into the modulated pathways. Our findings illuminate the upregulation of oncogenic long non-coding RNA and transcription factors, outlining a signaling pathway model influenced by stress factors in HCC cell lines. Understanding the pivotal role of stress factors in oncogenicity and HCC progression holds promise in developing therapeutics, particularly for populations where stress contributes significantly to cancer disparities. These insights offer potential avenues for targeted interventions aimed at mitigating the impact of stress on HCC, potentially improving outcomes for affected populations.