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Piwi-interacting RNAs (piRNAs or piRs) are a novel class of non-coding RNAs that participate in germline development by silencing transposable elements and regulating gene expression. To date, the association between piRNAs and non‑small cell lung carcinoma (NSCLC) has not yet been elucidated. In the present study, we have demonstrated that a significant increase in piR-651 expression occurs in NSCLC. Furthermore, the abnormal expression of piR-651 was associated with cancer progression in the patients with NSCLC. The upregulation of piR-651 in A549 cells caused a significant increase in cell viability and metastasis. The percentage of arrested cells in the G0/G1 phase was lower after piR-651 overexpression compared with the controls. We also examined the expression of oncogenes and cancer suppressor genes following piR-651 overexpression in NSCLC cells. Only the expression levels of cyclin D1 and CDK4 significantly correlated with piR-651 expression both in vivo and in vitro. Furthermore, by injecting nude mice with A549 cells transfected with piR-651 plasmids to establish a xenograft model, we demonstrated that there was a correlation between piR-651 overexpression and tumor growth, which was mediated by cyclin D1 and CDK4. These findings strongly support the notion that piR-651 induces NSCLC progression through the cyclin D1 and CDK4 pathway and it may have applications as a potential diagnostic indicator and therapeutic target in the management of NSCLC.

An adaptive process monitoring approach with variable moving window principal component analysis (variable MWPCA) is proposed. On the basis of recursively updating the correlation matrix in both samplewise and blockwise manners, the approach combines the moving window technique with the classical rank-r singular value decomposition (R-SVD) algorithm to construct a new PCA model. Compared with previous MWPCA algorithms, the method not only improves the computation efficiency but also reduces the storage requirement. Furthermore, instead of a fixed window size, a variable moving window strategy is described in detail for accommodating normal process changes with different changing rates. The proposed method is applied to an illustrative case and a continuous stirred tank reactor process, and the monitoring results show better adaptability to both a slow drift and a set-point change than the results of using the conventional MWPCA with a fixed window size.

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