DataSheet1_Identification of senescence-associated long non-coding RNAs to predict prognosis and immune microenvironment in patients with hepatocellular carcinoma.docx

Background: Cellular senescence plays a complicated and vital role in cancer development because of its divergent effects on tumorigenicity. However, the long non-coding RNAs (lncRNAs) associated with tumor senescence and their prognostic value in hepatocellular carcinoma (HCC) remain unexplored.Methods: The trans-cancer oncogene-induced senescence (OIS) signature was determined by gene set variation analysis (GSVA) in the cancer genome atlas (TCGA) dataset. The OIS-related lncRNAs were identified by correlation analyses. Cox regression analyses were used to screen lncRNAs associated with prognosis, and an optimal predictive model was created by regression analysis of the least absolute shrinkage and selection operator (LASSO). The performance of the model was evaluated by Kaplan-Meier survival analyses, nomograms, stratified survival analyses, and receiver operating characteristic curve (ROC) analyses. Gene set enrichment analysis (GSEA) and cell-type identification by estimating relative subsets of RNA transcripts (CIBERSORT) were carried out to explore the functional relevance and immune cell infiltration, respectively.Results: Firstly, we examined the pan-cancer OIS signature, and found several types of cancer with OIS strongly associated with the survival of patients, including HCC. Subsequently, based on the OIS signature, we identified 76 OIS-related lncRNAs with prognostic values in HCC. We then established an optimal prognostic model based on 11 (including NRAV, AC015908.3, MIR100HG, AL365203.2, AC009005.1, SNHG3, LINC01138, AC090192.2, AC008622.2, AL139423.1, and AC026356.1) of these lncRNAs by LASSO-Cox regression analysis. It was then confirmed that the risk score was an independent and potential risk indicator for overall survival (OS) (HR [95% CI] = 4.90 [2.74–8.70], p Conclusion: Based on 11 OIS-related lncRNAs, we established a promising prognostic predictor for HCC patients, and highlighted the potential immune microenvironment-modulatory roles of OIS in HCC, providing a broad molecular perspective of tumor senescence.</p

Additional file 1 of Genome-wide association study identifies new loci associated with noise-induced tinnitus in Chinese populations

Additional file 1: Supplementary Figure 1. The principal components analyses (PCA) of the population in the discovery stage in this study and reference populations from the 1,000 Genomes Project. Supplementary Figure 2. The genotypes of rs2846071 are significantly associated with the expression levels of WNT11 in several types of brain tissues from GTEx. Supplementary Figure 3. Colocalization analyses of the association signals from GWAS and brain eQTL data at the 11q13.5 and 12p13.31 loci. Supplementary Figure 4. Chromatin state segmentations for rs1800692 and rs4149570 using the ENCODE data. Supplementary Figure 5. The genotypes of rs4149577 are significantly associated with the expression levels of TNFRSF1A in several types of brain tissue from GTEx. Supplementary Figure 6. Proxy plots for 11q13.5 and 12p13.31 regions in Chinese Han Chinese and European populations. Supplementary Figure 7. Linkage disequilibrium plots for 11q13.5 and 12p13.31 regions in Chinese Han Chinese and European populations. Supplementary Figure 8. Power to detect the genetic effects of rs2846071 and rs4149577. Supplementary Table 1. Summary of the case/control populations used in this study. Supplementary Table 2. Summary of the genotyped and imputed SNPs in the discovery stage. Supplementary Table 3. Summary of the SNPs that have been reported to be associated with tinnitus in previous studies. Supplementary Table 4. Summary of the top 22 SNPs in the discovery stage. Supplementary Table 5. Primers used for SNPs genotyping in the replication stage. Supplementary Table 6. Summary of the association results in the replication stage. Supplementary Table 7. Stratification analyses of rs2846071 and rs4149577 by age. Supplementary Table 8. The predicted functional relevance of rs2846071, rs4149577 and the other SNPs in strong or moderate LD with them. Supplementary Table 9. Pathway analyses based on i-GSEA4GWAS. Supplementary Table 10. The allele and genotype frequencies of rs2846071 and rs4149577 in different populations

Image_2_A Study on the Radiosensitivity of Radiation-Induced Lung Injury at the Acute Phase Based on Single-Cell Transcriptomics.tif

Background and AimsRadiation-induced lung injury (RILI) is the most common complication associated with chest tumors, such as lung and breast cancers, after radiotherapy; however, the pathogenic mechanisms are unclear. Single-cell RNA sequencing has laid the foundation for studying RILI at the cellular microenvironmental level. This study focused on changes during the acute pneumonitis stage of RILI at the cellular microenvironmental level and investigated the interactions between different cell types.MethodsAn acute RILI model in mice and a single-cell transcriptional library were established. Intercellular communication networks were constructed to study the heterogeneity and intercellular interactions among different cell types.ResultsA single-cell transcriptome map was established in a mouse model of acute lung injury. In total, 18,500 single-cell transcripts were generated, and 10 major cell types were identified. The heterogeneity and radiosensitivity of each cell type or subtype in the lung tissues during the acute stage were revealed. It was found that immune cells had higher radiosensitivity than stromal cells. Immune cells were highly heterogeneous in terms of radiosensitivity, while some immune cells had the characteristics of radiation resistance. Two groups of radiation-induced Cd8+Mki67+ T cells and Cd4+Cxcr6+ helper T cells were identified. The presence of these cells was verified using immunofluorescence. The ligand-receptor interactions were analyzed by constructing intercellular communication networks. These explained the origins of the cells and revealed that they had been recruited from endothelial cells to the inflammatory site.ConclusionsThis study revealed the heterogeneity of in vivo radiosensitivity of different cell types in the lung at the initial stage post irradiation</p

Image_1_A Study on the Radiosensitivity of Radiation-Induced Lung Injury at the Acute Phase Based on Single-Cell Transcriptomics.tif

Background and AimsRadiation-induced lung injury (RILI) is the most common complication associated with chest tumors, such as lung and breast cancers, after radiotherapy; however, the pathogenic mechanisms are unclear. Single-cell RNA sequencing has laid the foundation for studying RILI at the cellular microenvironmental level. This study focused on changes during the acute pneumonitis stage of RILI at the cellular microenvironmental level and investigated the interactions between different cell types.MethodsAn acute RILI model in mice and a single-cell transcriptional library were established. Intercellular communication networks were constructed to study the heterogeneity and intercellular interactions among different cell types.ResultsA single-cell transcriptome map was established in a mouse model of acute lung injury. In total, 18,500 single-cell transcripts were generated, and 10 major cell types were identified. The heterogeneity and radiosensitivity of each cell type or subtype in the lung tissues during the acute stage were revealed. It was found that immune cells had higher radiosensitivity than stromal cells. Immune cells were highly heterogeneous in terms of radiosensitivity, while some immune cells had the characteristics of radiation resistance. Two groups of radiation-induced Cd8+Mki67+ T cells and Cd4+Cxcr6+ helper T cells were identified. The presence of these cells was verified using immunofluorescence. The ligand-receptor interactions were analyzed by constructing intercellular communication networks. These explained the origins of the cells and revealed that they had been recruited from endothelial cells to the inflammatory site.ConclusionsThis study revealed the heterogeneity of in vivo radiosensitivity of different cell types in the lung at the initial stage post irradiation</p

Table_1_A Study on the Radiosensitivity of Radiation-Induced Lung Injury at the Acute Phase Based on Single-Cell Transcriptomics.xlsx

Background and AimsRadiation-induced lung injury (RILI) is the most common complication associated with chest tumors, such as lung and breast cancers, after radiotherapy; however, the pathogenic mechanisms are unclear. Single-cell RNA sequencing has laid the foundation for studying RILI at the cellular microenvironmental level. This study focused on changes during the acute pneumonitis stage of RILI at the cellular microenvironmental level and investigated the interactions between different cell types.MethodsAn acute RILI model in mice and a single-cell transcriptional library were established. Intercellular communication networks were constructed to study the heterogeneity and intercellular interactions among different cell types.ResultsA single-cell transcriptome map was established in a mouse model of acute lung injury. In total, 18,500 single-cell transcripts were generated, and 10 major cell types were identified. The heterogeneity and radiosensitivity of each cell type or subtype in the lung tissues during the acute stage were revealed. It was found that immune cells had higher radiosensitivity than stromal cells. Immune cells were highly heterogeneous in terms of radiosensitivity, while some immune cells had the characteristics of radiation resistance. Two groups of radiation-induced Cd8+Mki67+ T cells and Cd4+Cxcr6+ helper T cells were identified. The presence of these cells was verified using immunofluorescence. The ligand-receptor interactions were analyzed by constructing intercellular communication networks. These explained the origins of the cells and revealed that they had been recruited from endothelial cells to the inflammatory site.ConclusionsThis study revealed the heterogeneity of in vivo radiosensitivity of different cell types in the lung at the initial stage post irradiation</p

The genotype frequencies of polymorphisms in the <i>PTEN</i>, <i>AKT1</i> and <i>p53</i> genes in patients with nasopharyngeal carcinoma and controls.

<p>NOTE: The number of samples that were genotyped varies due to genotyping failure for some individuals.</p><p>Abbreviations: OR, odds ratio; CI, confidence interval; NA, not applicable.</p>a<p>ORs and <i>P</i> values were adjusted for age, sex, smoking and drinking status, smoking level and nationality.</p

Combined effects of the genetic variants in the <i>PTEN</i>, <i>AKT1</i>, <i>MDM2</i> and <i>p53</i> genes on the risk of nasopharyngeal carcinoma.

<p>Abbreviations: OR, odds ratio; CI, confidence interval.</p>a<p><i>χ²</i> test for the distribution of genotypes between patients and control subjects.</p>b<p><i>P</i> values were calculated by multivariate logistic regression, adjusted for age, sex, smoking and drinking status, smoking level, and nationality.</p>c<p><i>χ</i>² test for the <i>P</i>_trend value of genotypes between patients and control subjects.</p>d<p>Low-risk group, individuals carrying 0–2 risk genotypes; high-risk group, individuals carrying 3-4 risk genotypes.</p><p>*<i>P</i> value remained significant after c°rrection for multiple comparisons (<i>P</i> = 0.048).</p

Additional file 1 of Integrative single-cell transcriptomic analyses reveal the cellular ontological and functional heterogeneities of primary and metastatic liver tumors

Additional file 1: Figure S1. Overview of the single-cell atlas from primary and metastatic liver tumors and non-tumor tissues. Figure S2. Characteristic heterogeneity of B cells in primary and metastatic liver tumors. Figure S3. Ontological and functional changes of CD4+ T cells in primary and metastatic liver tumors. Figure S4. Functional changes and clinical relevance of CD8+ T cell signatures in primary and metastatic liver tumors. Figure S5. Characteristics of myeloid cells in primary and metastatic liver tumors. Figure S6. Clinical relevance and spatial distribution of macrophage signatures in primary and metastatic liver tumors. Figure S7. Spatial distributions of the fibroblasts, the involved ligands, the malignant epithelial cells and the involved receptors in metastatic liver tumors

Haplotypes of <i>AKT1</i> polymorphisms and the risk of nasopharyngeal carcinoma.

<p>(<i>a</i>) Genomic structure of the <i>AKT1</i> locus and the polymorphic sites used. Exons (boxes) and introns are not drawn to scale; open boxes represent noncoding sequences, and filled boxes represent coding sequences. The physical distance between SNPs is shown in nucleotides. (<i>b</i>) Linkage disequilibrium (LD) map of SNPs based on <i>D</i> ´. (<i>c</i>) LD map of SNPs based on <i>r</i>². (<i>d</i>) Global <i>P</i> values from single-locus and multi-locus (two to five) based association analysis. (<i>e</i>) Haplotypes showing significant genetic associations with the risk of nasopharyngeal carcinoma. The two-SNP core haplotype is highlighted in gray.</p

Stratification analysis of the combined genotypes of the <i>PTEN</i>, <i>AKT1</i>, <i>MDM2</i> and <i>p53</i> polymorphisms and risk of nasopharyngeal carcinoma.

<p>Abbreviations: OR, odds ratio; CI, confidence interval.</p>a<p>ORs and <i>P</i> values were calculated by multivariate logistic regression, adjusted for age, sex, smoking and drinking status, smoking level and nationality when appropriate within the strata.</p>b<p>For differences in ORs within each stratum.</p>c<p>Low-risk group, individuals carrying 0–2 risk genotypes; high-risk group, individuals carrying 3–4 risk genotypes.</p