Genome-wide gene expression profiling was conducted by Solexa sequencing in order to gain insight into the transcriptome dynamics that are associated with salt stress of cotton seedlings. A total of 145,794 and 138,518 clean tags were generated from the control and salinity libraries, respectively. Of these, 75,500 (51.8%) and 72,077 (52.0%) tags were matched to the reference genes. The most differentially regulated tags with a log2ratio [2 or [-2 (P \ 0.001) were analyzed further, representing 125 up-and 171 down-regulated genes except for unknown transcripts, which were classified into ten functional categories. The most enriched categories were those of metabolism, signaling pathway, environmental response and transcription. Many genes or biological pathways were found to be commonly shared between salt and other abiotic stresses in plants such as genes participating in environmental response, ABA signaling JA signaling, etc. Furthermore, the expression patterns of 12 genes were assessed by quantitative real-time PCR, and the results obtained showed general agreement with the Solexa data. Further analysis indicated the important roles of selected genes in salt tolerance by comparison with the mRNA levels in salt-tolerant cotton cultivar ZM3 with that in salt-sensitive cultivar LM6. Overall, we reveal the complex changes at the transcriptional level during salt stress of cotton seedlings and provide useful starting points for more in-depth analyses of cotton's salt tolerance.

The -160 C/A single nucleotide polymorphism of the E-cadherin gene promoter is associated with TCCB. This single nucleotide polymorphism may serve as a prognostic marker of TCCB.

Renal cell carcinoma (RCC) is a major healthcare burden globally. Tumor-derived extracellular vesicles (EVs) contribute to the formation of a pro-metastatic microenvironment. In the present study, we explored the role and mechanism of RCC cell 786-O-derived EVs (786-O-EVs) in RCC. First, 786-O-EVs were extracted and identified, and EV internalization of RCC cells was observed. RCC cell malignant behaviors and long noncoding RNA (lncRNA) metastasis-associated lung adenocarcinoma transcript 1 (MALAT1) expression patterns were detected before and after 786-O-EV treatment. MALAT1 was intervened to evaluate RCC cell behaviors. The downstream mechanism involving MALAT1 was predicted. In addition, the relationship among MALAT1, transcription factor CP2 like 1 (TFCP2L1) and ETS proto-oncogene 1, transcription factor (ETS1) was analyzed. TFCP2L1 expression patterns were measured after 786-O-EV exposure. Tumor xenograft formation assay and lung metastasis model were adopted to verify the role of 786-O-EVs in vivo in RCC. It was found that 786-O-EVs could be internalized by RCC cells. 786-O-EVs promoted RCC cell malignant behaviors, accompanied by elevated MALAT1 expression levels. The 786-O-EVs with MALAT1 knockdown attenuated the promotive effect of sole 786-O-EVs on RCC cells. MALAT1 located ETS1 in the TFCP2L1 promoter and negatively regulated TFCP2L1, and ETS1 protein could specifically bind to MALAT1. 786-O-EVs enhanced the binding of ETS1 and the TFCP2L1 promoter and decreased TFCP2L1 expression. In vivo, 786-O-EVs promoted tumor growth and RCC lung metastasis, which was suppressed following inhibition of MALAT1. Our findings indicated that 786-O-EVs promoted RCC invasion and metastasis by transporting MALAT1 to promote the binding of transcription factor ETS1 and TFCP2L1 promoter.

The study aimed to compare the clinicopathological features and prognosis between type I and type II papillary renal cell carcinoma (PRCC) and to investigate whether the subtypes of PRCC would affect oncological outcomes. A total of 102 patients with PRCC were recruited, of which 42 were type I PRCC and 60 type II. The clinicopathological features and oncologic outcomes of the patients were evaluated. The type II cases had a higher WHO/ISUP grading (P < 0.001), T (P = 0.003), N (P = 0.010) stage and stage grouping (P = 0.011) than the type I. During a median follow-up period of 61.4 months, 1-year cancer specific survival (CSS) of the type I was 100%, 5-year CSS was 95.2%, the 1-year CSS of the type II was 96.2%, and 5-year CSS was 75.7%. The univariate analysis showed that subtype, symptoms, TNM, stage grouping, WHO/ISUP grading and surgical methods appeared to affect prognosis of the patients with PRCC. However, multivariate analysis revealed that only stage grouping was the independent risk factor. After the stage grouping factor was adjusted for the analysis, there were no statistically significant differences in CSS (P = 0.214) and PFS (P = 0.190) between the localized type I and type II PRCC groups. Compared with type I PRCC, type II had higher pathological T, N stage and WHO/ISUP grading. However, it was the Stage grouping that made a great difference to oncological outcomes, rather than the subtype of PRCC.