RESUMO
SUMMARY: We investigated Tweety Family Member 3 (TTYH3) level in lung adenocarcinoma (LUAD) and its relationship with immune infiltration in tumors by bioinformatics. Differential expressions of TTYH3 in lung cancer were analyzed with Oncomine, TIMER, GEO, UALCAN and HPA. Relationship of TTYH3 mRNA/protein levels with clinical parameters was analyzed by UALCAN. Co-expressed genes of TTYH3 in LUAD were analyzed using Cbioportal. Its relationship with LUAD prognosis was analyzed by Kaplan-Meier plotter. GO and KEGG analysis were performed. Correlation between TTYH3 and tumor immune infiltration were tested by TIMER, TISIDB and GEPIA. We found that TTYH3 was significantly increased in LUAD tissues. TTYH3 high expression was closely related to poor overall survival, post progression survival and first progression in LUAD patients. TTYH3 mRNA/protein levels were significantly associated with multiple pathways. Specifically, TTYH3 up-regulation was mostly related to biological regulation, metabolic process, protein blinding, extracellular matrix organization and pathways in cancer. Moreover, TTYH3 was positively associated with immune cell infiltration in LUAD. Finally, TTYH3 was highly expressed in LUAD as revealed by meta-analysis. TTYH3 is closely related to the prognosis of LUAD and immune cell infiltration, and it can be used as a prognostic biomarker for LUAD and immune infiltration.
Investigamos por bioinformática el nivel de Tweety Family Member 3 (TTYH3) con adenocarcinoma de pulmón (LUAD) y su relación con la infiltración inmune en tumores. Las expresiones diferenciales de TTYH3 en cáncer de pulmón se analizaron con Oncomine, TIMER, GEO, UALCAN y HPA. Con UALCAN se analizó la relación de los niveles de ARNm/proteína de TTYH3 con los parámetros clínicos. Los genes coexpresados de TTYH3 en LUAD se analizaron utilizando Cbioportal. Su relación con el pronóstico LUAD se analizó mediante plotter de Kaplan- Meier. Se realizaron análisis GO y KEGG. TIMER, TISIDB y GEPIA probaron la correlación entre TTYH3 y la infiltración inmune tumoral. Encontramos que TTYH3 aumentó significativamente en los tejidos LUAD. La alta expresión de TTYH3 estuvo estrechamente relacionada con una supervivencia general deficiente, supervivencia posterior a la progresión y primera progresión en pacientes con LUAD. Los niveles de ARNm/ proteína de TTYH3 se asociaron significativamente con múltiples vías. Específicamente, la regulación positiva de TTYH3 se relacionó principalmente con la regulación biológica, el proceso metabólico, el cegamiento de proteínas, la organización de la matriz extracelular y las vías en el cáncer. Además, TTYH3 se asoció positivamente con la infiltración de células inmunitarias en LUAD. Finalmente, TTYH3 se expresó altamente en LUAD como lo reveló el metanálisis. TTYH3 está estrechamente relacionado con el pronóstico de LUAD y la infiltración de células inmunitarias, y se puede utilizar como biomarcador pronóstico para LUAD y la infiltración de células inmunitarias.
Assuntos
Humanos , Canais de Cloreto/metabolismo , Adenocarcinoma de Pulmão/diagnóstico , Neoplasias Pulmonares/diagnóstico , Prognóstico , RNA Mensageiro , Linfócitos , Biomarcadores Tumorais , Canais de Cloreto/genética , Adenocarcinoma de Pulmão/imunologia , Adenocarcinoma de Pulmão/metabolismo , Neoplasias Pulmonares/imunologia , Neoplasias Pulmonares/metabolismoRESUMO
BACKGROUND: Recently, extensive cancer genomic studies have revealed mutational and clinical data of large cohorts of cancer patients. For example, the Pan-Lung Cancer 2016 dataset (part of The Cancer Genome Atlas project), summarises the mutational and clinical profiles of different subtypes of Lung Cancer (LC). Mutational and clinical signatures have been used independently for tumour typification and prediction of metastasis in LC patients. Is it then possible to achieve better typifications and predictions when combining both data streams? METHODS: In a cohort of 1144 Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LSCC) patients, we studied the number of missense mutations (hereafter, the Total Mutational Load TML) and distribution of clinical variables, for different classes of patients. Using the TML and different sets of clinical variables (tumour stage, age, sex, smoking status, and packs of cigarettes smoked per year), we built Random Forest classification models that calculate the likelihood of developing metastasis. RESULTS: We found that LC patients different in age, smoking status, and tumour type had significantly different mean TMLs. Although TML was an informative feature, its effect was secondary to the "tumour stage" feature. However, its contribution to the classification is not redundant with the latter; models trained using both TML and tumour stage performed better than models trained using only one of these variables. We found that models trained in the entire dataset (i.e., without using dimensionality reduction techniques) and without resampling achieved the highest performance, with an F1 score of 0.64 (95%CrI [0.62, 0.66]). CONCLUSIONS: Clinical variables and TML should be considered together when assessing the likelihood of LC patients progressing to metastatic states, as the information these encode is not redundant. Altogether, we provide new evidence of the need for comprehensive diagnostic tools for metastasis.
Assuntos
Adenocarcinoma de Pulmão , Carcinoma Pulmonar de Células não Pequenas , Carcinoma de Células Escamosas , Neoplasias Pulmonares , Adenocarcinoma de Pulmão/genética , Adenocarcinoma de Pulmão/patologia , Carcinoma Pulmonar de Células não Pequenas/patologia , Carcinoma de Células Escamosas/genética , Humanos , Neoplasias Pulmonares/genética , Neoplasias Pulmonares/patologia , Mutação/genéticaRESUMO
PURPOSE: Globally, lung cancer remains the most commonly diagnosed cancer and the leading cause of cancer-related mortality. Lung adenocarcinoma (LUAD) is a common subtype of lung cancer and carries a poor prognosis. Treatment outcomes biomarkers in LUAD are critical, and there is currently a paucity of data; therefore, there is a need for novel biomarkers and newer therapeutic targets. METHODS: Bayesian analysis was used to obtain the whole-genome t value of LUAD. Gene set enrichment analysis (GSEA) was conducted to obtain the normalized enrichment scores (NES) of the whole genome, and the Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway was analyzed using the Gene Set Analysis Toolkit. Herein, we investigated the PPP1R14D expression level at the protein level in LUAD and the impact of PPP1R14D knockdown on the proliferation and apoptosis of LUAD cells in vitro. RESULTS: A total of 483 LUAD samples and 59 normal control samples were included, and 904 differentially expressed genes (DEGs) and 504 LUAD-related genes reported in the literature were obtained. The DEGs showed that PPP1R14D was the most significantly up-regulated gene. Western blot of 30 cases of LUAD tissue and adjacent normal tissue also found that PPP1R14D was significantly highly expressed in cancer tissues. Lentivirus-mediated shRNA strategy effectively inhibited PPP1R14D expression in human LUAD cells DMS53, while PPP1R14D knockdown induced apoptosis and cell proliferation in DMS53 cells. CONCLUSION: Abnormally up-regulated PPP1R14D promotes the survival and proliferation of tumor cells in human LUAD and may serve as a therapeutic and diagnostic target for LUAD.