CAFs-derived rho-associated kinase1 mediated EMT to promote laryngeal squamous cell carcinoma metastasis.

BACKGROUND: Cancer-associated fibroblasts (CAFs) play an essential role in tumorigenesis and development of cancers. Nevertheless, the specific molecular mechanism of tumorigenesis and development in Laryngeal squamous cell carcinoma (LSCC) still unknown. METHODS: CAFs, CPFs and NFs were isolated and identified from laryngeal cancer, para-laryngeal cancer and normal tissues. Immunofluorescent staining, Rt-PCR and Western Blot were used to detect the expression of related proteins. Wound healing, migration, invasion and animal experiments were used to examine the ability of movement, migration, invasion and metastasis of LSCC. RESULTS: ROCK1, was highly expressed in CAFs and CAFs enhanced LSCC metastasis in vivo and vitro, and downregulation of ROCK1 in CAFs inhibited the migration and invasion of LSCC cells. While increasing ROCK1 expression in NFs promoted the migration and invasion of LSCC cells. Further studies revealed that epithelial-mesenchymal transition (EMT) and JAK2/STAT3/ERK1/2 pathway might play an essential role in promoting metastasis of LSCC. In addition, inhibition activity of ROCK1 or JAK2/STAT3/ERK1/2 signal molecules significantly reduced EMT and metastasis. CONCLUSIONS: CAFs-derived ROCK1 via JAK2/STAT3/ERK1/2 axis mediated EMT to promote LSCC metastasis and targeting ROCK1 might provide a potential treatment strategy for LSCC.

Stochastic Lag Time Parameterization for Markov State Models of Protein Dynamics.

Markov state models (MSMs) play a key role in studying protein conformational dynamics. A sliding count window with a fixed lag time is widely used to sample sub-trajectories for transition counting and MSM construction. However, sub-trajectories sampled with a fixed lag time may not perform well under different selections of lag time, which requires strong prior practice and leads to less robust estimation. To alleviate it, we propose a novel stochastic method from a Poisson process to generate perturbative lag time for sub-trajectory sampling and utilize it to construct a Markov chain. Comprehensive evaluations on the double-well system, WW domain, BPTI, and RBD-ACE2 complex of SARS-CoV-2 reveal that our algorithm significantly increases the robustness and power of a constructed MSM without disturbing the Markovian properties. Furthermore, the superiority of our algorithm is amplified for slow dynamic modes in complex biological processes.

Analysis of DNA methylation differences in gonads of the large yellow croaker.

DNA methylation is an essential epigenetic modification that significantly regulates gene expression during development and differentiation. In this study, genome-wide methylation analysis of different gonads of the large yellow croaker was performed using whole-genome bisulfite sequencing (WGBS), which has characterized DNA methylation patterns in gonad tissue and identified candidate regions for future studies. Clustering analysis revealed that male and neomale methylation patterns were close compared to female. Based on KEGG pathway analysis of differentially methylated genes, we obtained signaling pathways related to gonadal development. We further investigated the methylation status of previously reported sex determination genes, and found that these genes showed different methylation status in three types of gonads, which may provide important clues to reveal the sex determination genes in the large yellow croaker. Furthermore, combined with transcriptome analysis, we found 7 sex-related genes in three comparison groups where expression negatively correlated with methylation.

The Prognostic Signature and Potential Target Genes of Six Long Non-coding RNA in Laryngeal Squamous Cell Carcinoma.

Studies have shown that long non-coding RNA (lncRNA) may act as the carcinogenic factor or tumor suppressor of laryngeal squamous cell carcinoma (LSCC). This study aims to identify the prognostic value and potential target protein-coding genes (PCGs) of lncRNAs in LSCC. The LSCC datasets were collected from The Cancer Genome Atlas (TCGA). Statistical and bioinformatic methods were used to establish and evaluate the prognostic model, identify the correlation between lncRNAs and clinical characteristics, and screen for PCGs co-expressed with lncRNAs. Weighted gene co-expression network analysis (WGCNA) identified PCG modules associated with clinical characteristics. The expression of lncRNAs and PCGs was analyzed using our LSCC patients by RT-qPCR. LINC02154, LINC00528, SPRY4-AS1, TTTY14, LNCSRLR, and KLHL7-DT were selected to establish the prognostic model. The overall survival (OS) of low-risk patients forecasted by the model was significantly better than high-risk patients. Receiver operating characteristic (ROC) curve and concordance index (C-index) validated the accuracy of the prognostic model. Chi-square test showed that six lncRNAs were associated with one of the clinical characteristics, i.e., gender, clinical stage, T and N stage, respectively. WGCNA identified PCG modules associated with gender, clinical stage, T and N stage. We took the intersection of the PCG modules of WGCNA, the differentially expressed PCGs between LSCC and normal samples, and the PCGs co-expressed with six lncRNAs. The intersection PCGs survival analysis showed that four PCGs, i.e., STC2, TSPAN9, SMS, and TCEA3 affected the OS of LSCC. More importantly, the differential expression of six lncRNAs and four PCGs between LSCC and normal samples was verified by our LSCC patients. In conclusion, we successfully established a prognostic model based on six-lncRNA RiskScore and initially screened the potential target PCGs of six lncRNAs for further basic and clinical research.

Identifying Long Non-coding RNA of Prostate Cancer Associated With Radioresponse by Comprehensive Bioinformatics Analysis.

Although radiotherapy is greatly successful in the treatment of prostate cancer (PCa), radioresistance is still a major challenge in the treatment. To our knowledge, this study is the first to screen long non-coding RNAs (lncRNAs) associated with radioresponse in PCa by The Cancer Genome Atlas (TCGA). Bioinformatics methods were used to identify the differentially expressed lncRNAs and protein-coding genes (PCGs) between complete response (CR) and non-complete response (non-CR) groups in radiotherapy. Statistical methods were applied to identify the correlation between lncRNAs and radioresponse as well as lncRNAs and PCGs. The correlation between PCGs and radioresponse was analyzed using weighted gene co-expression network analysis (WGCNA). The three online databases were used to predict the potential target miRNAs of lncRNAs and the miRNAs that might regulate PCGs. RT-qPCR was utilized to detect the expression of lncRNAs and PCGs in our PCa patients. A total of 65 differentially expressed lncRNAs and 468 differentially expressed PCGs were found between the two groups of PCa. After the chi-square test, LINC01600 was selected to be highly correlated with radioresponse from the 65 differentially expressed lncRNAs. Pearson correlation analysis found 558 PCGs co-expressed with LINC01600. WGCNA identified the darkred module associated with radioresponse in PCa. After taking the intersection of the darkred module of WGCNA, differentially expressed PCGs between the two groups of PCa, and the PCGs co-expressed with LINC01600, three PCGs, that is, JUND, ZFP36, and ATF3 were identified as the potential target PCGs of LINC01600. More importantly, we detected the expression of LINC01600 and three PCGs using our PCa patients, and finally verified that LINC01600 and JUND were differentially expressed between CR and non-CR groups, excluding ZFP36 and ATF3. Meantime, the potential regulation ability of LINC01600 for JUND in PCa cell lines was initially explored. In addition, we constructed the competing endogenous RNA (ceRNA) network of LINC01600-miRNA-JUND. In conclusion, we initially reveal the association of LINC01600 with radioresponse in PCa and identify its potential target PCGs for further basic and clinical research.

The Expression and Effection of MicroRNA-499a in High-Tobacco Exposed Head and Neck Squamous Cell Carcinoma: A Bioinformatic Analysis.

Background: Few studies have directly investigated the differential expression of microRNAs (miRNAs) in head and neck squamous cell carcinoma (HNSCC) with low, medium, and high tobacco exposure. The purpose of this study is to screen the differentially expressed miRNAs and to investigate their clinical significance and potential biological mechanisms in the three groups of HNSCC. Methods: The datasets of HNSCC were obtained from The Cancer Genome Atlas (TCGA). The edgeR package was used to determine differentially expressed miRNAs and genes among the three groups of HNSCC. Statistical methods were applied to assess the clinical significance of miRNA and its correlation with genes. The correlation between gene expression and clinical characteristics was analyzed using weighted gene co-expression network analysis (WGCNA). Three online databases were used to predict the target genes of miRNAs. More importantly, qRT-PCR was employed to verify the differential expression of miRNAs and genes in our patients. Results: 32 differentially expressed miRNAs and 1,820 differentially expressed genes were found among the three groups of HNSCC. Patients with high expression of hsa-miR-499a had lower overall survival than the ones with low expression in high-tobacco exposed HNSCC. Cox regression analysis found that high expression of hsa-miR-499a and female were independent risk factors for prognosis in high-tobacco exposed HNSCC. Chi-square test found that hsa-miR-499a was associated with N stage in high-tobacco exposed HNSCC. WGCNA identified four gene modules associated with N stage in high-tobacco exposed HNSCC. Then three online databases were used to predict potential target genes for hsa-miR-499a, which were AEBP2 and ZNRF1. Pearson correlation analysis showed that hsa-miR-499a was negatively correlated with AEBP2 and ZNRF1. qRT-PCR supported bioinformatic results that hsa-miR-499a, AEBP2, and ZNRF1 were differentially expressed among the three groups of HNSCC in our patients. Conclusion: 32 differentially expressed miRNAs and 1,820 differentially expressed genes were successfully identified in HNSCC with low, medium, and high tobacco exposure. The patients with high expression of hsa-miR-499a had poor prognoses compared with patients with low expression in high-tobacco exposed HNSCC. Hsa-miR-499a was associated with N stage in high-tobacco exposed HNSCC. AEBP2 and ZNRF1 were the potential target genes of hsa-miR-499a.