Long non-coding RNA RP11-468E2.5 curtails colorectal cancer cell proliferation and stimulates apoptosis via the JAK/STAT signaling pathway by targeting STAT5 and STAT6.

BACKGROUND: Long non-coding RNAs (lncRNAs) are tumor-associated biological molecules and have been found to be implicated in the progression of colorectal cancer (CRC). This study aims to examine the effects of lncRNA RP11-468E2.5 and its target genes (STAT5 and STAT6) on the biological activities of CRC cells via the Janus kinase-signal transducer and activator of transcription (JAK/STAT) signaling pathway. METHODS: We initially screened the GEO database for differentially expressed lncRNAs related to CRC and then made a prediction of the implicated target genes. Then we collected CRC tissues and adjacent normal tissues from 169 CRC patients. Human CRC HCT116 and SW480 cells were treated with small interference RNA (siRNA) against RP11-468E2.5, AG490 (an inhibitor of the JAK/STAT signaling pathway), or both in combination. Next, we measured the effects of RP11-468E2.5 treatment on cellular activities such as cell viability, cycle distribution and cell apoptosis, and studied interactions among RP11-468E2.5, STAT5/STAT6, and the JAK/STAT signaling pathway. Finally, an in vivo tumor formation assay was performed to observe the effect of RP11-468E2.5 on tumor growth. RESULTS: The CRC-related gene microarray data showed low expression of RP11-468E2.5 in CRC surgical specimens. However, RP11-468E2.5 was confirmed to target STAT5 and STAT6, which participate in the JAK/STAT signaling pathway. CRC tissues showed lower expression of RP11-468E2.5, higher expression of STAT5, STAT6 and of the cell cycle marker Cyclin D1 (CCND1), compared to the findings in adjacent normal tissues. The treatment of siRNA against RP11-468E2.5 increased expression of JAK2, STAT3, STAT5, STAT6, CCND1 and Bcl-2 along with the extent of STAT3, STAT5 and STAT6 phosphorylation, while lowering expression of P21 and P27. Treatment with AG490 exhibited approximately opposite effects, whereas siRNA against RP11-468E2.5 treatment stimulated CRC cell proliferation and reduced cell apoptosis, while promoting cell cycle entry; AG490 treatment reversed these results. CONCLUSIONS: Altogether, we conclude that up-regulation of RP11-468E2.5 inhibits the JAK/STAT signaling pathway by targeting STAT5 and STAT6, thereby suppressing cell proliferation and promoting cell apoptosis in CRC.

Unveiling tropospheric ozone by the traditional atmospheric model and machine learning, and their comparison:A case study in hangzhou, China.

Tropospheric ozone in the surface air has become the primary atmospheric pollutant in Hangzhou, China, in recent years. Previous analysis is not enough to decode it for better regulation. Therefore, we use the traditional atmospheric model, Weather Research and Forecasting coupled with Community Multi-scale Air Quality (WRF-CMAQ), and machine learning models, Extreme Learning Machine (ELM), Multi-layer Perceptron (MLP), Random Forest (RF) and Recurrent Neural Network (RNN) to analyze and predict the ozone in the surface air in Hangzhou, China, using meteorology and air pollutants as input. We firstly quantitatively demonstrate that the dew-point deficit, instead of temperature and relative humidity, is the predominant meteorological factor in shaping tropospheric ozone. Urban heat island, daily direct solar radiation time, wind speed and wind direction play trivial role in impacting tropospheric ozone. NO2 is the primary influential factors both for hourly ozone and daily O3-8 h due to the titration effect. The most environmental-friendly way to mitigate the ozone pollution is to lower the volatile organic compounds (VOCs) with the highest ozone formation potentials. We deduce that the tropospheric ozone formation process tends to be not only non-linear but also non-smooth. Compared with the traditional atmospheric models, machine learning, whose characteristics are rapid convergence, short calculating time, adaptation of forecasting episodes, small program memory, higher accuracy and less cost, is able to predict tropospheric ozone more accurately.