Novel defined N7-methylguanosine modification-related lncRNAs for predicting the prognosis of laryngeal squamous cell carcinoma

Objective:Through integrated bioinformatics analysis,the goal of this work was to find new,characterised N7-methylguanosine modification-related long non-coding RNAs(m7G-lncRNAs)that might be used to predict the prognosis of laryngeal squamous cell carcinoma(LSCC).Methods:The clinical data and LSCC gene expression data for the current investigation were initially retrieved from the TCGA database&sanitised.Then,using co-expression analysis of m7G-associated mRNAs&lncRNAs&differential expression analysis(DEA)among LSCC&normal sample categories,we discovered lncRNAs that were connected to m7G.The prognosis prediction model was built for the training category using univariate&multivariate COX regression&LASSO regression analyses,&the model’s efficacy was checked against the test category data.In addition,we conducted DEA of prognostic m7G-lncRNAs among LSCC&normal sample categories&compiled a list of co-expression networks&the structure of prognosis m7G-lncRNAs.To compare the prognoses for individuals with LSCC in the high-&low-risk categories in the prognosis prediction model,survival and risk assessments were also carried out.Finally,we created a nomogram to accurately forecast the outcomes of LSCC patients&created receiver operating characteristic(ROC)curves to assess the prognosis prediction model’s predictive capability.Results:Using co-expression network analysis&differential expression analysis,we discovered 774 m7G-lncRNAs and 551 DEm7G-lncRNAs,respectively.We then constructed a prognosis prediction model for six m7G-lncRNAs(FLG−AS1,RHOA−IT1,AC020913.3,AC027307.2,AC010973.2 and AC010789.1),identified 32 DEPm7G-lncRNAs,analyzed the correlation between 32 DEPm7G-lncRNAs and 13 DEPm7G-mRNAs,and performed survival analyses and risk analyses of the prognosis prediction model to assess the prognostic performance of LSCC patients.By displaying ROC curves and a nomogram,we finally checked the prognosis prediction model's accuracy.Conclusion:By creating novel predictive lncRNA signatures for clinical diagnosis&therapy,our findings will contribute to understanding the pathogenetic process of LSCC.

Distribution of phytoplankton in the East China Sea and the southern Yellow Sea in spring in relation to environmental variables and dimethylsulfide compounds

The coastal ecosystems are highly sensitive to climate change and are usually influenced by variations in phytoplankton communities and water physiochemical factors.In the present study,the phytoplankton community,chlorophyll a(Chl a)and their relationships with environmental variables and dimethylsulfide(DMS)and dimethylsulfoniopropionate(DMSP)were investigated in spring 2017(March 24 to April 16)in the East China Sea(26.0°-33.0°N,120.0°-128.0°E)and southern Yellow Sea(31.0°-36.0°N,120.0°-125.0°E).The spatial distributions of phytoplankton species composition and cell density were investigated by qualitative and quantitative methods and were compared with historical data to study phytoplankton species succession in the survey area.The results showed that there were 275 phytoplankton species belonging to 90 genera and 6 phyla in the survey area,of which 208 species belonged to 62 genera of Bacillariophyta and 56 species belonged to 20 genera of Pyrrophyta.The dominant phytoplankton species were Skeletonema dohrnii,Chaetoceros vanheurckii and Prorocentrum donghaiense.The phytoplankton cell densities ranged from 0.06×10^(4)cells/L to 418.73×10^(4)cells/L,with an average value of 21.46×10^(4)cells/L.In spring,the average ratio of Bacillariophyta/Pyrrophyta was41.13 for the entire study area.The areas with high phytoplankton cell density were mainly distributed in the northern South Yellow Sea and offshore waters of the East China Sea.According to a canonical correspondence analysis among phytoplankton and environmental parameters,the water Chl a concentrations were notably consistent with phytoplankton cell density(P<0.001),and both showed significant negative correlations with salinity and nitrite(P<0.05)and significant positive correlations with dissolved oxygen and pH(P<0.001).There was a significant positive correlation between diatom(both in cell density and in dominant species)and DMS(P<0.05),which indicated that diatoms play a greater role in DMS production in this investigated area.