Circular RNAs (circRNAs) are a class of long, non-coding RNAs molecules that shape a covalently closed continuous loop which have no 5'-3' polarity and contain no polyA tail. CircRNAs also possess relatively jarless framework and are highly tissue-specific expressed in the eukaryotic transcriptome. Emerging evidences have discovered that thousands of endogenous circRNAs are present in mammalian cells and they mediate gene expression at the transcriptional or post-transcriptional level by binding to microRNAs or other molecules and then inhibit their function. Similarly, increasing evidence indicates that circRNAs may play a role in the development of several types of diseases, including atherosclerotic vascular disease risk, neurological disorders, prion diseases, osteoarthritis and diabetes. Furthermore, circRNAs exhibit aberrant expression in multiform types of cancer, including colorectal cancer, hepatocellular carcinoma and pancreatic ductal adenocarcinoma. And based on the function of circRNAs in cancer, we believe that circRNAs may serve as diagnostic or tumor promising biomarkers. Moreover, it will provide a new therapeutic target for the treatment of cancer.

Circular RNAs (circRNAs) have been identified play a vital role in various different types of cancer via sponging miRNAs (microRNAs). However, their role in lung adenocarcinoma (LUAD) remains largely unclear. In this study, we systematically characterized the circRNA expression profiles in the LUAD cancer tissues and paired adjacent noncancerous tissues. Three circRNAs were found to be significantly upregulated. Among them, has-circRNA-002178 was further confirmed to be upregulated in the LUAD tissues, and LUAD cancer cells. Subsequently, we also found has-circRNA-002178 could enhance PDL1 expression via sponging miR-34 in cancer cells to induce T-cell exhaustion. More importantly, circRNA-002178 could be detected in exosomes of plasma from LUAD patients and could serve as biomarkers for LUAD early diagnosis. Finally, we found circRNA-002178 could be delivered into CD8 + T cells to induce PD1 expression via exosomes. Taken together, our study revealed that circRNA-002178 could act as a ceRNA to promote PDL1/PD1 expression in lung adenocarcinoma.

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Android applications are developing rapidly across the mobile ecosystem, but Android malware is also emerging in an endless stream. Many researchers have studied the problem of Android malware detection and have put forward theories and methods from different perspectives. Existing research suggests that machine learning is an effective and promising way to detect Android malware. Notwithstanding, there exist reviews that have surveyed different issues related to Android malware detection based on machine learning. We believe our work complements the previous reviews by surveying a wider range of aspects of the topic. This paper presents a comprehensive survey of Android malware detection approaches based on machine learning. We briefly introduce some background on Android applications, including the Android system architecture, security mechanisms, and classification of Android malware. Then, taking machine learning as the focus, we analyze and summarize the research status from key perspectives such as sample acquisition, data preprocessing, feature selection, machine learning models, algorithms, and the evaluation of detection effectiveness. Finally, we assess the future prospects for research into Android malware detection based on machine learning. This review will help academics gain a full picture of Android malware detection based on machine learning. It could then serve as a basis for subsequent researchers to start new work and help to guide research in the field more generally.