Early diagnosis of lung cancer greatly reduces mortality; however, the lack of suitable plasma biomarkers presents a major obstacle. Recent studies showed that long noncoding RNAs (lncRNAs) played important roles in cancer initiation and development. Here, we identified differentially expressed lncRNAs in 20 lung cancer samples by using custom designed microarray and evaluated their expression in 118 lung cancer samples by real-time PCR (qRT-PCR). lncRNA16 (ENST00000539303) expression was significantly higher in lung cancer tissues (80/118) than in adjacent matched normal tissues. Importantly, this increase was similar to that in plasma (53/84) of lung cancer patients, including early stage. The role of lncRNA16 in lung cancer was studied in vitro and in vivo by using the lung cancer cell lines and xenograft mouse models. The results reveal that knockdown of lncRNA16 inhibited proliferation of PC9 cells in vitro and also inhibited tumor growth in xenograft mouse models. Overexpression of lncRNA16 promoted proliferation of A549 cells in vitro and also promoted tumor growth in xenograft mouse models. Specifically, we showed that lncRNA16 promoted G2/M transition by regulating cyclin B1 transcription. Together, our findings suggest that lncRNA16 is a promising biomarker suitable for early diagnosis of lung cancer, and a potential target for lung cancer treatment.
The squamous cell carcinoma antigen (SCCA) is a tumor marker that has gained increasing attention for its biological functions and significance in normal physiological and pathological processes. Not only SCCA but also circulating immune complexes of SCCA and immunoglobulin M (IgM) are involved in normal physiological and pathological processes, providing a background for numerous clinical studies aimed at assessing the potential role of SCCA, SCCA–IgM, and SCCA isoform complexes in clinical practice. Previous studies support the clinical value of SCCA as a tumor marker for either diagnosing squamous cancers or monitoring the response to radiotherapy or chemotherapy, tumor relapse, and treatment failure. However, these studies show contrasting results, making the diagnostic or prognostic value of SCCA controversial. To reduce clinical heterogeneity across studies and achieve a more accurate and reliable comparison of results, a standardized detection method, scoring system, and cutoff level need to be established. Moreover, despite the fact that performances of different methods are comparable, the dynamic observation of tumor marker kinetics should be conducted under the same method.
Hepatitis C is one of the most common types of viral hepatitis that impair human health. At present, there is still no effective specific therapy for hepatitis C virus infection. As host immunity is an important mechanism to defend against or clear infections, the interactions between the virus and the host immune response are crucial to the progress of the disease. Of note, hepatitis C virus infection has been reported to regulate cellular miRNAs, which play significant roles in many processes, including infection and immunity. In this review, we describe how miRNAs regulate the host immune response to hepatitis C virus via complex signaling pathways.
Background Keshan disease is an endemic cardiomyopathy of undefined causes. Being involved in the unclear pathogenesis of Keshan disease, a clear diagnosis, and effective treatment cannot be initiated. However, the rapid development of gut flora in cardiovascular disease combined with omics and big data platforms may promote the discovery of new diagnostic markers and provide new therapeutic options. This study aims to identify biomarkers for the early diagnosis and further explore new therapeutic targets for Keshan disease. Methods This cohort study consists of two parts. Though the first part includes 300 participants, however, recruiting will be continued for the eligible participants. After rigorous screening, the blood samples, stools, electrocardiograms, and ultrasonic cardiogram data would be collected from participants to elucidate the relationship between gut flora and host. The second part includes a prospective follow-up study for every 6 months within 2 years. Finally, deep mining of big data and rapid machine learning will be employed to analyze the baseline data, experimental data, and clinical data to seek out the new biomarkers to predict the pathogenesis of Keshan disease. Discussion Our study will clarify the distribution of gut flora in patients with Keshan disease and the abundance and population changes of gut flora in different stages of the disease. Through the big data platform analyze the relationship between environmental factors, clinical factors, and gut flora, the main factors affecting the occurrence of Keshan disease were identified, and the changed molecular pathways of gut flora were predicted. Finally, the specific gut flora and molecular pathways affecting Keshan disease were identified by metagenomics combined with metabonomic analysis. Trial registration: ChiCTR1900026639. Registered on 16 October 2019.
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