A triple-classification for differentiating renal oncocytoma from renal cell carcinoma subtypes and CK7 expression evaluation: a radiomics analysis.

BACKGROUND: To investigate the value of computed tomography (CT)-based radiomics model analysis in differentiating renal oncocytoma (RO) from renal cell carcinoma subtypes (chromophobe renal cell carcinoma, clear cell carcinoma) and predicting the expression of Cytokeratin 7 (CK7). METHODS: In this retrospective study, radiomics was applied for patients with RO, chRCC and ccRCC who underwent surgery between January 2013 and December 2019 comprised the training cohort, and the testing cohort was collected between January and October 2020. The corticomedullary (CMP) and nephrographic phases (NP) were manually segmented, and radiomics texture parameters were extracted. Support vector machine was generated from CMP and NP after feature selection. Shapley additive explanations were applied to interpret the radiomics features. A radiomics signature was built using the selected features from the two phases, and the radiomics nomogram was constructed by incorporating the radiomics features and clinical factors. Receiver operating characteristic curve was calculated to evaluate the above models in the two sets. Furthermore, Rad-score was used for correlation analysis with CK7. RESULTS: A total of 123 patients with RO, chRCC and ccRCC were analyzed in the training cohort and 57 patients in the testing cohort. Subsequently, 396 radiomics features were selected from each phase. The radiomics features combining two phases yielded the highest area under the curve values of 0.941 and 0.935 in the training and testing sets, respectively. The Pearson's correlation coefficient was statistically significant between Rad-score and CK7. CONCLUSION: We proposed a non-invasive and individualized CT-based radiomics nomogram to differentiation among RO, chRCC and ccRCC preoperatively and predict the immunohistochemical protein expression for accurate clinical diagnosis and treatment decision.

A downsampling method enables robust clustering and integration of single-cell transcriptome data.

The random noises, sampling biases, and batch effects often confound true biological variations in single-cell RNA-sequencing (scRNA-seq) data. Adjusting such biases is key to the robust discoveries in downstream analyses, such as cell clustering, gene selection and data integration. Here we propose a model-based downsampling algorithm based on minimal unbiased representative points (MURPXMBD). MURPXMBD is designed to retrieve a set of representative points by reducing gene-wise random independent errors, while retaining the covariance structure of biological origin hence provide an unbiased representation of the cell population. Subsequent validation using benchmark datasets shows that MURPXMBD can improve the quality and accuracy of clustering algorithms, and thus facilitate the discovery of new cell types. Besides, MURPXMBD also improves the performance of dataset integration algorithms. In summary, MURPXMBD serves as a useful noise-reduction method for single-cell sequencing analysis in biomedical studies.

Exposure to Tobacco Smoking Induces a subset of Activated Tumor-resident Tregs in Non-Small Cell Lung Cancer.

Tobacco smoking is the major cause of non-small-cell-lung cancer (NSCLC). However, it is barely known how smoking impact the tumor immune environment (TIME) of lung cancer. We integrated single-cell RNA-seq and bulk RNA-seq data from several studies to systematically study the impact of smoking on T cells in treatment naïve NSCLC patients. We defined a set of smoking-induced differentially expressed genes (SIDEGs) in different cells in TIME.. Specifically, we defined a smoking-related tumor-specific Treg subset, ADAM12+ CTLA4+ Tregs according to the trajectory analysis and highly express genes in cell adhesion pathways and lipid metabolism. Using independent datasets from treatment naïve patients, we found that the fraction of ADAM12+ CTLA4+ Tregs are significantly increased in patients with smoking history. Moreover, the fraction of ADAM12+ CTLA4+ Tregs are positively correlated with the fraction of exhausted T cells. Additionally, we reconstructed the spatial organization of the tumor immune microenvironment and found that ADAM12+ CTLA4+ Tregs more actively communicate with LAYN+CD8+ exhausted T cells compared with ADAM12-CTLA4+ Tregs. Our data demonstrate that smoking induced a unique subset of tumor-specific activated Tregs which interact with exhausted T cells in the TIME. Our findings not only explained how smoking impact the TIME but also provide new targets and biomarkers for precision immunotherapy of lung cancer.

Mitochondrial C1qbp promotes differentiation of effector CD8+ T cells via metabolic-epigenetic reprogramming.

Early-activated CD8+ T cells increase both aerobic glycolysis and mitochondrial oxidative phosphorylation (OXPHOS). However, whether and how the augmentation of OXPHOS regulates differentiation of effector CD8+ T cell remains unclear. Here, we found that C1qbp was intrinsically required for such differentiation in antiviral and antitumor immune responses. Activated C1qbp-deficient CD8+ T cells failed to increase mitochondrial respiratory capacities, resulting in diminished acetyl­coenzyme A as well as elevated fumarate and 2-hydroxyglutarate. Consequently, hypoacetylation of H3K27 and hypermethylation of H3K27 and CpG sites were associated with transcriptional down-regulation of effector signature genes. The effector differentiation of C1qbp-sufficient or C1qbp-deficient CD8+ T cells was reversed by fumarate or a combination of histone deacetylase inhibitor and acetate. Therefore, these findings identify C1qbp as a pivotal positive regulator in the differentiation of effector CD8+ T cells and highlight a metabolic-epigenetic axis in this process.

Exonic variants undergoing allele-specific selection in cancers.

BACKGROUND: Allelic imbalance (AI) in tumors is caused by chromosomal and sub-chromosomal gains and losses. RESULTS: We evaluated AI at 109,086 germline exonic SNP loci in four cancer types, and identified a set of SNPs that demonstrate strong tumor allele specificity in AI events. Further analyses demonstrated that these alleles show consistently different frequencies in the cancer population compared to the healthy population and are significantly enriched for predicted protein-damaging variants. Moreover, genes harboring SNPs that demonstrate allele specificity are enriched for cancer-related biological processes and are more likely to be essential in cancer cells. CONCLUSIONS: In summary, our study provides a unique and complementary method to identify genes and variants that are relevant to carcinogenesis.

Cis- and Trans-Acting Expression Quantitative Trait Loci of Long Non-Coding RNA in 2,549 Cancers With Potential Clinical and Therapeutic Implications.

Many cancer risk loci act as expression quantitative trait loci (eQTLs) of transcripts including non-coding RNA. Long non-coding RNAs (lncRNAs) are implicated in various human cancers. However, the pathological and clinical impacts of the genetic determinants of lncRNAs in cancers remain largely unknown. In this study, we performed eQTL mapping of lncRNA expression (elncRNA) in 11 TCGA cancer types and characterized the biological processes of elncRNAs in the setting of genomic location, cancer treatment responses, and immune microenvironment. As a result, 10.86% of the cis-eQTLs and 1.67% of the trans-eQTLs of lncRNA were related to known genome-wide association studies (GWAS) cancer risk loci. The elncRNAs are significantly enriched for those which are previously annotated as predictive of drug sensitivities in cancer cell lines. We further revealed the downstream transcriptomic effectors of eQTL-elncRNA pairs. Our data specifically suggested that the genes affected by eQTL-elncRNA associations are enriched in the immune system processes and eQTL-elncRNA associations influence the constitution of tumor infiltrating lymphocytes. In ovarian cancer, the "rs34631313-AC092580.4" pair was associated with increased fraction of CD8+ T cells and M1 Macrophage; whereas in KIRC, the "rs9546285-LINC00426" pair was associated with increased fraction of CD8+ T cells and a decreased fraction of M2 macrophages. Our findings provide a systematic view of the transcriptomic impacts of the eQTL landscape of lncRNA in human cancers and suggest its strong potential relevance to cancer immunity and treatment.

DNA methylome and transcriptome landscapes revealed differential characteristics of dioecious flowers in papaya.

Separate sexes in dioecious plants display different morphology and physiological characteristics. The differences between the two sexes lie in their highly differentiated floral characteristics and in sex-related phenotype, which is genetically determined and epigenetically modified. In dioecious papaya (Carica papaya L.), global comparisons of epigenetic DNA methylation and gene expressions were still limited. We conducted bisulfite sequencing of early-stage flowers grown in three seasons (spring, summer and winter) and compared their methylome and transcriptome profiles to investigate the differential characteristics of male and female in papaya. Methylation variances between female and male papaya were conserved among three different seasons. However, combined genome-scale transcriptomic evidence revealed that most methylation variances did not have influence on the expression profiles of neighboring genes, and the differentially expressed genes were most overrepresented in phytohormone signal transduction pathways. Further analyses showed diverse stress-responsive methylation alteration in male and female flowers. Male flower methylation was more responsive to stress whereas female flower methylation varied less under stress. Early flowering of male papaya in spring might be associated with the variation in the transcription of CpSVP and CpAP1 coinciding with their gene-specific hypomethylation. These findings provide insights into the sex-specific DNA methylation and gene expression landscapes of dioecious papaya and a foundation to investigate the correlation between differentiated floral characteristics and their candidate genes.

Positional cloning and characterization of the papaya diminutive mutant reveal a truncating mutation in the CpMMS19 gene.

The papaya diminutive mutant exhibits miniature stature, retarded growth and reduced fertility. This undesirable mutation appeared in the variety 'Sunset', the progenitor of the transgenic line 'SunUp', and was accidentally carried forward into breeding populations. The diminutive mutation was mapped to chromosome 2 and fine mapped to scaffold 25. Sequencing of a bacterial artificial chromosome in the fine mapped region led to the identification of the target gene responsible for the diminutive mutant, a gene orthologous to MMS19 with a 36.8 kb deletion co-segregating with the diminutive mutant. The genomic sequence of CpMMS19 is 62 kb, consisting of 20 exons and 19 introns. It encodes a protein of 1143 amino acids while the diminutive allele encodes a truncated protein of 287 amino acids. Expression of the full-length CpMMS19 was able to complement the thermosensitive growth of the yeast mms19 deletion mutant while expression of the diminutive allele resulted in increased thermosensitivity. Over-expression of the diminutive allele in Arabidopsis met18 mutant results in a high frequency of seed abortion. The papaya diminutive phenotype is caused by an alteration in gene function rather than a loss-of-function mutation. SCAR (sequence characterized amplified region) markers were developed for rapid detection of the diminutive allele in breeding populations.