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Machine Learning-Based Comparative Analysis of Pan-Cancer and Pan-Normal Tissues Identifies Pan-Cancer Tissue-Enriched circRNAs Related to Cancer Mutations as Potential Exosomal Biomarkers.

ABSTRACT: A growing body of evidence has shown that circular RNA (circRNA) is a promising exosomal cancer biomarker candidate. However, global circRNA alterations in cancer and the underlying mechanism, essential for identification of ideal circRNA cancer biomarkers, remain under investigation. We comparatively analyzed the circRNA landscape in pan-cancer and pan-normal tissues. Using co-expression and LASSO regularization analyses, as well as a support vector machine, we analyzed 265 pan-cancer and 319 pan-normal tissues in order to identify the circRNAs with the highest ability to distinguish between pan-cancer and pan-normal tissues. We further studied their expression in plasma exosomes from patients with cancer and their relation with cancer mutations and tumor microenvironment landscape. We discovered that circRNA expression was globally reduced in pan-cancer tissues and plasma exosomes from cancer patients than in pan-normal tissues and plasma exosomes from healthy controls. We identified dynein axonemal heavy chain 14 (DNAH14), the top back-spliced gene exclusive to pan-cancer tissues, as the host gene of three pan-cancer tissue-enriched circRNAs. Among these three circRNAs, chr1_224952669_224968874_+ was significantly elevated in plasma exosomes from hepatocellular carcinoma and colorectal cancer patients. It was also related to the cancer mutation chr1:224952669: G>A, a splice acceptor variant, and was increasingly transcription-driven in cancer tissues. Moreover, pan-cancer tissue-enriched and pan-normal tissue-enriched circRNAs were associated with distinct tumor microenvironment patterns. Our machine learning-based analysis provides insights into the aberrant landscape and biogenesis of circRNAs in cancer and highlights cancer mutation-related and DNAH14-derived circRNA, chr1_224952669_224968874_+, as a potential cancer biomarker.

SUBMITTER: Wang X

PROVIDER: S-EPMC8479194 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Background: Circular RNA (circRNA) has been reported to be involved in the pathogenesis of cardiovascular disease (CVD), however, it is unclear whether circRNA carried by exosomes (exos) can be used as biomarkers for chronic coronary syndrome (CCS). This study aimed to investigate the expression pattern of exosomal circRNAs in the plasma of CCS patients, and to screen exo-circRNAs that can act as biomarkers for the diagnosis of CCS. Methods: High-throughput sequencing technology was carried out in the plasma exosomal RNA of 15 CCS patients and 15 non-cardiac chest pain patients (NCCP, control group) (sequencing cohort) to screen for differentially expressed circRNAs. Selected differentially expressed exo-circRNAs were further verified by real time polymerase chain reaction (RT-PCR) in a small-sample cohort (derivation cohort) and a large-sample cohort (validation cohort). Receiver operating characteristic (ROC) curve analysis was performed to evaluate the diagnostic value of exo-circRNAs for CCS patients. Results: By high-throughput sequencing of circRNAs in 15 CCS patients and 15 NCCP patients, 276 circRNAs differentially expressed in plasma exosomes of CCS patients were screened by using |log2(Fold change) |>1 and q-value<0.001, and 103 were up-regulated and 173 were down-regulated. Among the 103 up-regulated circRNAs, 5 circRNAs with high expression levels and significantly increased in plasma exosomes of CCS patients were selected for validation. Twice RT-PCR validation demonstrated that exo-hsa_circ_0075269 and exo-hsa_circ_0000284 were significantly up-regulated in patients with CCS (P<0.001). Circulating exo-hsa_circ_0075269 and exo-hsa_circ_0000284 yielded the area under the curve (AUC) values of 0.761 (P<0.001, 95%CI=0.669, 0.852) and 0.623 (P=0.015, 95%CI=0.522, 0.724) for CCS, respectively by ROC curve analysis. Moreover, AUC of hsa_circ_0075269 combined with hsa_circ_0000284 for diagnosing CCS was 0.741 (P<0.001, 95%CI=0.667, 0.850). Conclusion: The expression profile of circRNA in plasma exosomes of patients with CCS was significantly different from that of the control group. Plasma exo-hsa_circ_0075269 and exo-hsa_circ_0000284 have the potential to be new biomarkers for CCS diagnosis.

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Machine Learning-Based Comparative Analysis of Pan-Cancer and Pan-Normal Tissues Identifies Pan-Cancer Tissue-Enriched circRNAs Related to Cancer Mutations as Potential Exosomal Biomarkers.

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