Genome-Wide DNA Methylation Model for the Diagnosis of Prostate Cancer

Development and validation of cuproptosis-related lncRNAs associated with pancreatic cancer immune microenvironment based on single-cell

Background

Cuproptosis, a novel mode of cell death associated with the tricarboxylic acid (TCA) cycle, is relevant to the development of cancer. However, the impact of single-cell-based Cuproptosis-associated lncRNAs on the Tumor immune microenvironment (TIME) of Pancreatic adenocarcinoma (PAAD) and its potential value for individualized immunotherapy has not been clarified.

Methods

14 immune-related CRGs were screened by exploring the interaction between differentially expressed Immune-Related Genes (IRGs) and Cuproptosis-Related Genes (CRGs) in PAAD. Next, the expression amount and expression distribution of CRGs in single-cell samples were analyzed by focusing on 7-CRGs with significant expressions. On the one hand, MAP2K2, SOD1, and VEGFA, which were significantly differentially expressed between PAAD sites and normal tissues adjacent to them, were subjected to immunohistochemical validation and immune landscape analysis. On the other hand, from these 7-CRGs, prognostic signatures of lncRNAs were established by co-expression and LASSO-COX regression analysis, and their prognostic value and immune relevance were assessed. In addition, this study not only validated the hub CRGs and the lncRNAs constituting the signature in a PAAD animal model treated with immunotherapy-based combination therapy using immunohistochemistry and qRT-PCR but also explored the potential value of the combination of targeted, chemotherapy and immunotherapy.

Results

Based on the screening of 7-CRGs significantly expressed in a PAAD single-cell cohort and their co-expressed Cuproptosis-Related lncRNAs (CRIs), this study constructed a prognostic signature of 4-CRIs named CIR-score. A Nomogram integrating the CIR-score and clinical risk factors was constructed on this basis to predict the individualized survival of patients. Moreover, high and low-risk groups classified according to the median of signatures exhibited significant differences in clinical prognosis, immune landscape, bioenrichment, tumor burden, and drug sensitivity. And the immunohistochemical and qRT-PCR results of different mouse PAAD treatment strategies were consistent with the trend of inter-group variability in drug sensitivity of hub CRGs and CIR-score. The combination of immunotherapy, targeted therapy, and chemotherapy exhibited a better tumor suppression effect.

Conclusion

CIR-score, as a Cuproptosis-related TIME-specific prognostic signature based on PAAD single cells, not only predicts the prognosis and immune landscape of PAAD patients but also provides a new strategy for individualized immunotherapy-based combination therapy.

DSTN Hypomethylation Promotes Radiotherapy Resistance of Rectal Cancer by Activating the Wnt/β-Catenin Signaling Pathway

PURPOSE

Although surgical resection combined with neoadjuvant radiation therapy can reduce the local recurrence rate of rectal cancer, not all patients benefit from neoadjuvant radiation therapy. Therefore, screening for patients with rectal cancer who are sensitive or resistant to radiation therapy has great clinical significance.

METHODS AND MATERIALS

Patients with rectal cancer were selected according to postoperative tumor regression grade, and tumor samples were taken for detection. Differential genes between radiation-resistant and radiation-sensitive tissues were screened and validated by Illumina Infinium MethylationEPIC BeadChip, proteomics, Agena MassARRAY methylation, reverse transcription quantitative real-time polymerase chain reaction, and immunohistochemistry. In vitro and in vivo functional experiments verified the role of DSTN. Protein coimmunoprecipitation, western blot, and immunofluorescence were used to investigate the mechanisms of DSTN-related radiation resistance.

RESULTS

DSTN was found to be highly expressed (P < .05) and hypomethylated (P < .01) in rectal cancer tissues resistant to neoadjuvant radiation therapy. Follow-up data confirmed that patients with high expression of DSTN in neoadjuvant radiation therapy-resistant rectal cancer tissues had shorter disease-free survival (P < .05). DSTN expression increased after methyltransferase inhibitor inhibition of DNA methylation in colorectal cancer cells (P < .05). In vitro and in vivo experiments showed that knockdown of DSTN promoted the sensitivity of colorectal cancer cells to radiation therapy, and overexpression of DSTN promoted the resistance of colorectal cancer cells to radiation (P < .05). The Wnt/β-catenin signaling pathway was activated in colorectal cancer cells overexpressing DSTN. β-catenin was highly expressed in radiation therapy-resistant tissues, and there was a linear correlation between the expression of DSTN and β-catenin (P < .0001). Further studies showed that DSTN can bind to β-catenin and increase its stability.

CONCLUSIONS

The degree of DNA methylation and the expression level of DSTN can be used as biomarkers to predict the sensitivity of neoadjuvant radiation therapy for rectal cancer. DSTN and β-catenin are also expected to become a reference for the selection of neoadjuvant radiation therapy.

Epigenetics regulation of prostate cancer: Biomarker and therapeutic potential

Prostate cancer (CaP) is the second leading cause of cancer death and displays a broad range of clinical behavior from relatively indolent to aggressive metastatic disease. The etiology of most cases of CaP is not understood completely, which makes it imperative to search for the molecular basis of CaP and markers for early diagnosis. Epigenetic modifications, including changes in DNA methylation patterns, histone modifications, miRNAs, and lncRNAs are key drivers of prostate tumorigenesis. These epigenetic defects might be due to deregulated expression of the epigenetic machinery, affecting the expression of several important genes like GSTP1, RASSF1, CDKN2, RARRES1, IGFBP3, RARB, TMPRSS2-ERG, ITGB4, AOX1, HHEX, WT1, HSPE, PLAU, FOXA1, ASC, GPX3, EZH2, LSD1, etc. In this review, we highlighted the most important epigenetic gene alterations and their variations as a diagnostic marker and target for therapeutic intervention of CaP in the future. Characterization of epigenetic changes involved in CaP is obscure and adequate validation studies are still required to corroborate the present results that would be the impending future of transforming basic research settings into clinical practice.

Pan-cancer landscape of abnormal ctDNA methylation across human tumors

BACKGROUND

The aim of this paper is to explore the correlation between circulating tumor DNA (ctDNA) methylation and mutations and its value in clinical early cancer screening.

METHODS

We performed target region methylation sequencing and genome sequencing on plasma samples. Methylation models to distinguish cancer from healthy individuals have been developed using hypermethylated genes in tumors and validated in training set and prediction set.

RESULTS

We found that patients with cancer had higher levels of ctDNA methylation compared to healthy individuals. The level of ctDNA methylation in cell cycle, p53, Notch pathway in pan-cancer was significantly correlated with the number of mutations, and mutation frequency. Methylation burden in some tumors was significantly correlated with tumor mutational burden (TMB), microsatellite instability (MSI) and PD-L1. The ctDNA methylation differences in cancer patients were mainly concentrated in the Herpes simplex virus 1 infection pathway. The area under curve (AUC) of the training and prediction sets of the methylation model distinguishing cancer from healthy individuals were 0.93 and 0.92, respectively.

CONCLUSION

Our study provides a landscape of methylation levels of important pathways in pan-cancer. ctDNA methylation significantly correlates with mutation type, frequency and number, providing a reference for clinical application of ctDNA methylation in early cancer screening.

The Role of Long Noncoding RNAs on Male Infertility: A Systematic Review and In Silico Analysis

Male infertility is a complex disorder affecting many couples worldwide. Long noncoding RNAs (lncRNAs) regulate important cellular processes; however, a comprehensive understanding of their role in male infertility is limited. This systematic review investigates the differential expressions of lncRNAs in male infertility or variations in lncRNA regions associated with it. The PRISMA guidelines were used to search Pubmed and Web of Science (1 June 2022). Inclusion criteria were human participants, patients diagnosed with male infertility, and English language speakers. We also performed an in silico analysis investigating lncRNAs that are reported in many subtypes of male infertility. A total of 625 articles were found, and after the screening and eligibility stages, 20 studies were included in the final sample. Many lncRNAs are deregulated in male infertility, and interactions between lncRNAs and miRNAs play an important role. However, there is a knowledge gap regarding the impact of variants found in lncRNA regions. Furthermore, eight lncRNAs were identified as differentially expressed in many subtypes of male infertility. After in silico analysis, gene ontology (GO) and KEGG enrichment analysis of the genes targeted by them revealed their association with bladder and prostate cancer. However, pathways involved in general in tumorigenesis and cancer development of all types, such as p53 pathways, apoptosis, and cell death, were also enriched, indicating a link between cancer and male infertility. This evidence, however, is preliminary. Future research is needed to explore the exact mechanism of action of the identified lncRNAs and investigate the association between male infertility and cancer.

KIF2C affects sperm cell differentiation in patients with Klinefelter syndrome, as revealed by RNA-Seq and scRNA-Seq data

Klinefelter syndrome (KS) is a leading contributor to male infertility and is characterised by complex and diverse clinical features; however, genetic changes in the KS transcriptome remain largely unknown. We therefore used transcriptomic and single‐cell RNA sequencing (scRNA‐seq) datasets from KS versus normal populations through the Gene Expression Omnibus (GEO) database to identify gene biomarkers associated with the occurrence of KS. We identified a total of 700 differentially expressed genes (DEGs) and completed Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), enrichment pathway analysis and protein‐protein interaction (PPI) network analysis. A total of four unreported KS‐related hub genes (KIF2C, MRPS2, RPS15 and TSFM) were identified. Validation of the single‐cell sequencing dataset showed that only KIF2C and RPS15 were expressed in spermatocytes and that they were differentially expressed in sperm cells. Further construction of the developmental trajectories of these two genes in sperm cells showed that the KIF2C gene showed an upward trend throughout the differentiation and development of sperm cells. In conclusion, we report here that KIF2C may be closely related to the differentiation and development of sperm cells in KS patients, which is important for revealing the molecular mechanism of KS and conducting further studies.

A transcriptome-wide association study identifies novel candidate susceptibility genes for prostate cancer risk

A large proportion of heritability for prostate cancer risk remains unknown. Transcriptome-wide association study combined with validation comparing overall levels will help to identify candidate genes potentially playing a role in prostate cancer development. Using data from the Genotype-Tissue Expression Project, we built genetic models to predict normal prostate tissue gene expression using the statistical framework PrediXcan, a modified version of the unified test for molecular signatures and Joint-Tissue Imputation. We applied these prediction models to the genetic data of 79 194 prostate cancer cases and 61 112 controls to investigate the associations of genetically determined gene expression with prostate cancer risk. Focusing on associated genes, we compared their expression in prostate tumor vs normal prostate tissue, compared methylation of CpG sites located at these loci in prostate tumor vs normal tissue, and assessed the correlations between the differentiated genes' expression and the methylation of corresponding CpG sites, by analyzing The Cancer Genome Atlas (TCGA) data. We identified 573 genes showing an association with prostate cancer risk at a false discovery rate (FDR) ≤ 0.05, including 451 novel genes and 122 previously reported genes. Of the 573 genes, 152 showed differential expression in prostate tumor vs normal tissue samples. At loci of 57 genes, 151 CpG sites showed differential methylation in prostate tumor vs normal tissue samples. Of these, 20 CpG sites were correlated with expression of 11 corresponding genes. In this TWAS, we identified novel candidate susceptibility genes for prostate cancer risk, providing new insights into prostate cancer genetics and biology.

A DNA Methylation-Based Gene Signature Can Predict Triple-Negative Breast Cancer Diagnosis

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer (BC) subtype and lacks targeted treatment. It is diagnosed by the absence of immunohistochemical expression of several biomarkers, but this method still displays some interlaboratory variability. DNA methylome aberrations are common in BC, thereby methylation profiling could provide the identification of accurate TNBC diagnosis biomarkers. Here, we generated a signature of differentially methylated probes with class prediction ability between 5 non-neoplastic breast and 7 TNBC tissues (error rate = 0.083). The robustness of this signature was corroborated in larger cohorts of additional 58 non-neoplastic breast, 93 TNBC, and 150 BC samples from the Gene Expression Omnibus repository, where it yielded an error rate of 0.006. Furthermore, we validated by pyrosequencing the hypomethylation of three out of 34 selected probes (FLJ43663, PBX Homeobox 1 (PBX1), and RAS P21 protein activator 3 (RASA3) in 51 TNBC, even at early stages of the disease. Finally, we found significantly lower methylation levels of FLJ43663 in cell free-DNA from the plasma of six TNBC patients than in 15 healthy donors. In conclusion, we report a novel DNA methylation signature with potential predictive value for TNBC diagnosis.

Differential DNA Methylation in Prostate Tumors from Puerto Rican Men

In 2020, approximately 191,930 new prostate cancer (PCa) cases are estimated in the United States (US). Hispanic/Latinos (H/L) are the second largest racial/ethnic group in the US. This study aims to assess methylation patterns between aggressive and indolent PCa including DNA repair genes along with ancestry proportions. Prostate tumors classified as aggressive (n = 11) and indolent (n = 13) on the basis of the Gleason score were collected. Tumor and adjacent normal tissue were annotated on H&E (Haemotoxylin and Eosin) slides and extracted by macro-dissection. Methylation patterns were assessed using the Illumina 850K DNA methylation platform. Raw data were processed using the Bioconductor package. Global ancestry proportions were estimated using ADMIXTURE (k = 3). One hundred eight genes including AOX1 were differentially methylated in tumor samples. Regarding the PCa aggressiveness, six hypermethylated genes (RREB1, FAM71F2, JMJD1C, COL5A3, RAE1, and GABRQ) and 11 hypomethylated genes (COL9A2, FAM179A, SLC17A2, PDE10A, PLEKHS1, TNNI2, OR51A4, RNF169, SPNS2, ADAMTSL5, and CYP4F12) were identified. Two significant differentially methylated DNA repair genes, JMJD1C and RNF169, were found. Ancestry proportion results for African, European, and Indigenous American were 24.1%, 64.2%, and 11.7%, respectively. The identification of DNA methylation patterns related to PCa in H/L men along with specific patterns related to aggressiveness and DNA repair constitutes a pivotal effort for the understanding of PCa in this population.

An integrative multi-omics analysis to identify candidate DNA methylation biomarkers related to prostate cancer risk

It remains elusive whether some of the associations identified in genome-wide association studies of prostate cancer (PrCa) may be due to regulatory effects of genetic variants on CpG sites, which may further influence expression of PrCa target genes. To search for CpG sites associated with PrCa risk, here we establish genetic models to predict methylation (N = 1,595) and conduct association analyses with PrCa risk (79,194 cases and 61,112 controls). We identify 759 CpG sites showing an association, including 15 located at novel loci. Among those 759 CpG sites, methylation of 42 is associated with expression of 28 adjacent genes. Among 22 genes, 18 show an association with PrCa risk. Overall, 25 CpG sites show consistent association directions for the methylation-gene expression-PrCa pathway. We identify DNA methylation biomarkers associated with PrCa, and our findings suggest that specific CpG sites may influence PrCa via regulating expression of candidate PrCa target genes.

Whole Exome and Transcriptome RNA-Sequencing Model for the Diagnosis of Prostate Cancer

In our previous study, we developed a genome-wide DNA methylation model for the diagnosis of prostate cancer, and we pointed out that a considerable average error is associated with the current method for the diagnosis of prostate cancer, which is predicated on pathological assessment of biopsied tissue. In this study, we utilized whole exome and transcriptome RNA-sequencing (RNA-seq) data that were derived from 468 tumor samples and 51 normal samples of prostatic tissue, and we analyzed over 20,000 genes per sample. We were able to develop a mathematical model that classified tumor tissue versus normal tissue with a high accuracy. The overall sensitivity was 97.01%, and the overall specificity was 94.12%. The input variables to the model were the mRNA expression values of the following nine genes: ANGPT1, MED21, AOX1, PLP2, HPN, HPN-AS1, EPHA10, NKX2-3, and LRFN1. The model was validated with unknown samples, with a 10-fold cross-validation, and a leave-one-out cross-validation. We present here a genomic model, based on a whole exome and transcriptome RNA-seq analysis of biopsied prostatic tissue, that could be utilized in the diagnosis of prostate cancer.