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RNA-seq of small cell lung cancer [circRNA-Seq]

ABSTRACT: RNA-seq of small cell lung cancer [circRNA-Seq]

PROVIDER: PRJNA798093 | ENA |

REPOSITORIES: ENA

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RNA-seq of small cell lung cancer [circRNA-Seq]

Project description:We used CCK-8 experiment to determine the chemotherapy tolerance of small cell lung cancer cells, from which we found chemotherapy-sensitive cells (H446) and chemotherapy-tolerant cells (SHP77).By RNA-SEQ, we identified 401 circRNAs with abnormal expression associated with chemotherapy tolerance, including 212 up-regulated circRNAs and 189 down-regulated circRNAs. Through qRT-PCR verification, several circRNAs related to chemotherapy tolerance of small cell lung cancer were found, which helped us to further clarify the mechanism of chemotherapy tolerance of small cell lung cancer.

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Project description:Non-small cell lung cancer (NSCLC) is a common lung disorder.Recent studies have shown that circRNA plays critical roles in a myriad of biological processes and human diseases,Since the roles of circRNA in NSCLC remain unknown,they were investigated in the study.Our findings indicate that the expression profiles of circRNAs have changed in NSCLC as compared with normal lung, and may provide novel insight into the molecular mechanism underlying the disease and potential novel diagnostic or therapeutic targets for NSCLC.

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Project description:We used CCK-8 experiment to determine the chemotherapy tolerance of small cell lung cancer cells, from which we found chemotherapy-sensitive cells (H446) and chemotherapy-tolerant cells (SHP77).By RNA-SEQ, we found 99 miRNAs with abnormal expression associated with chemotherapy tolerance, including 69 up-regulated miRNAs and 30 down-regulated miRNAs. Several miRNAs related to chemotherapy tolerance of small cell lung cancer were found through qRT-PCR verification, which helped us to further clarify the mechanism of chemotherapy tolerance of small cell lung cancer.

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Combinatorial blood platelets-derived circRNA and mRNA signature for early-stage lung cancer

Project description:Despite the diversity of liquid biopsy transcriptomic repertoire, numerous studies often 30 exploit only a single RNA type signature for diagnostic biomarker potential. This frequently results 31 in insufficient sensitivity and specificity necessary to reach diagnostic utility. Combinatorial biomarker approaches may offer a more reliable diagnosis. Here we investigated the synergistic contributions of circRNA and mRNA signatures derived from blood platelets as biomarkers for lung cancer detection. We developed a comprehensive bioinformatics pipeline permitting analysis of platelet-circRNA and mRNA derived from non-cancer individuals and lung cancer patients. An optimal selected signature is then used to generate the predictive classification model using machine learning algorithm. Using an individual signature of 21 circRNA and 28 mRNA, the predictive models reached an Area Under the Curve (AUC) of 0.88 and 0.81, respectively. Importantly, combinatorial analysis including both types of RNAs resulted in an 8-target signature (6 mRNA and 2 40 circRNA) enhancing the differentiation of lung cancer from controls (AUC of 0.92). Additionally, we identified five biomarkers potentially specific for early-stage detection of lung cancer. Our proof-of-concept study presents the first multi-analyte-based approach for the analysis of platelets-derived biomarkers, providing a potential combinatorial diagnostic signature for lung cancer detection.

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