Analysis of genomic and transcriptomic variations as prognostic signature for lung adenocarcinoma

MELK aggravates lung adenocarcinoma by regulating EZH2 ubiquitination and H3K27me3 histone methylation of LATS2

We tried to elucidate the possible roles of maternal embryonic leucine pull chain kinase (MELK) in lung adenocarcinoma (LUAD) growth and metastasis. Differentially expressed genes in LUAD samples were analysed by the GEPIA database. Clinical tissue samples and cells were collected for MELK, EZH2 and LATS2 expression determination. Co-IP assay was used to verify the interaction between EZH2 and MELK; CHX tracking assay and ubiquitination assay detected the degradation of MELK on EZH2 ubiquitination. ChIP assay detected the enrichment of EZH2 and H3K27me3 on the LATS2 promoter region. LUAD cells were selected for in vitro validation, and the tumorigenic ability of LUAD cells was also observed in a transplantation tumour model of LUAD nude mice. MELK and EZH2 were highly expressed in LUAD samples, while LATS2 was lowly expressed. MELK interacted with EZH2 to inhibit its ubiquitination degradation; EZH2 elevated H3K27me3 modification in the LATS2 promoter to lower LATS2 expression. Silencing MELK or EZH2 or overexpressing LATS2 restrained LUAD cell proliferation and invasion, and facilitated their apoptosis. Silencing MELK or EZH2 or overexpressing LATS2 suppressed tumour formation in nude mice. This study demonstrated that MELK aggravated LUAD by upregulating EZH2 and downregulating LATS2.

Tumor-suppressive action of miR-30a-5p in lung adenocarcinoma correlates with ABL2 inhibition and PI3K/AKT pathway inactivation

INTRODUCTION

ABL2 contributes to the oncogenic potential of cancers, pointing to its inhibition as a possible strategy against malignant diseases. Bioinformatics prediction of upstream effector miR-30a-5p for ABL2 allowed us to hypothesize and then validate mechanistic actions of miR-30a-5p in lung adenocarcinoma (LUAD).

MATERIALS AND METHODS

The ABL2 expression in LUAD was analyzed in the TCGA data, clinical samples, and cell lines. The shRNA-mediated silencing of ABL2 was introduced to illustrate its effect on malignant phenotypes of LUAD cells. The binding affinity between ABL2 and miR-30a-5p was verified by luciferase activity and RNA pull-down assay. Ectopic expression, knockdown methods, and PI3K inhibitor LY294002 were used to investigate their effects on in vitro biological characteristics and in vivo tumor growth of LUAD cells. Using nude mouse lung adenocarcinoma in situ and brain metastasis models to validate the inhibitory effect of miR-30a-5p on LUAD by regulating the ABL2/PI3K/AKT signaling axis.

RESULTS

High expression of ABL2 and poor expression of miR-30a-5p were noticed in LUAD tissues and cell lines. Importantly, miR-30a-5p was demonstrated to target and downregulate ABL2, subsequently inactivating the PI3K/AKT pathway. miR-30a-5p inhibited the malignant phenotypes of LUAD cells by inhibiting ABL2 expression and inactivating the PI3K/AKT pathway. For in vivo experiments, miR-30a-5p was substantiated to thwart tumor tumorigenesis by regulating the ABL2/PI3K/AKT axis. In addition, miR-30a-5p suppresses the occurrence and development of in situ lung cancer and brain metastasis via the ABL2/PI3K/AKT signaling pathway.

CONCLUSION

This study underscores the inhibitory role of miR-30a-5p in LUAD through the ABL2/PI3K/AKT axis, which may be a viable target for LUAD treatment.

TCGAnalyzeR: An Online Pan-Cancer Tool for Integrative Visualization of Molecular and Clinical Data of Cancer Patients for Cohort and Associated Gene Discovery

For humans, the parallel processing capability of visual recognition allows for faster comprehension of complex scenes and patterns. This is essential, especially for clinicians interpreting big data for whom the visualization tools play an even more vital role in transforming raw big data into clinical decision making by managing the inherent complexity and monitoring patterns interactively in real time. The Cancer Genome Atlas (TCGA) database's size and data variety challenge the effective utilization of this valuable resource by clinicians and biologists. We re-analyzed the five molecular data types, i.e., mutation, transcriptome profile, copy number variation, miRNA, and methylation data, of ~11,000 cancer patients with all 33 cancer types and integrated the existing TCGA patient cohorts from the literature into a free and efficient web application: TCGAnalyzeR. TCGAnalyzeR provides an integrative visualization of pre-analyzed TCGA data with several novel modules: (i) simple nucleotide variations with driver prediction; (ii) recurrent copy number alterations; (iii) differential expression in tumor versus normal, with pathway and the survival analysis; (iv) TCGA clinical data including metastasis and survival analysis; (v) external subcohorts from the literature, curatedTCGAData, and BiocOncoTK R packages; (vi) internal patient clusters determined using an iClusterPlus R package or signature-based expression analysis of five molecular data types. TCGAnalyzeR integrated the multi-omics, pan-cancer TCGA with ~120 subcohorts from the literature along with clipboard panels, thus allowing users to create their own subcohorts, compare against existing external subcohorts (MSI, Immune, PAM50, Triple Negative, IDH1, miRNA, metastasis, etc.) along with our internal patient clusters, and visualize cohort-centric or gene-centric results interactively using TCGAnalyzeR.

CENPF Upregulation is Associated with Immunosuppressive Status and Poor Clinical Outcomes in Lung Adenocarcinoma Validated by qRT-PCR

OBJECTIVE

CENPF-differentially expressed in various types of cancers-is a marker of poor prognosis. However, studies on the impact of CENPF on patient prognosis in lung adenocarcinoma regarding immune infiltration are lacking.

METHODS

CENPF expression profiles were analyzed in the GEO and TCGA databases. qRT-PCR was used to verify CENPF mRNA expression in lung adenocarcinoma cell lines. The prognostic value of CENPF was evaluated by combining data from clinical samples in the GEPIA2 and TCGA databases. Metascape and WebGestalt were used for enrichment analysis of gene sets most positively associated with CENPF. Immune cell infiltration score data were retrieved from TCGA and the correlation between CENPF expression and immune cell infiltration was analyzed.

RESULTS

CENPF expression was elevated in 29 types of cancer. CENPF was highly expressed and increased with tumor grade in lung adenocarcinoma. Immunohistochemical and qRT-PCR analyses revealed that CENPF expression was upregulated in lung adenocarcinoma tissues and cells. High expression of CENPF significantly worsened prognoses in patients with multiple malignancies, including lung adenocarcinoma. Results from gene set enrichment analysis indicated significant enrichment of the progesterone-mediated oocyte maturation pathway. Immune infiltration analysis revealed that CD4+ Th2 cell infiltration was significantly higher in the high CENPF expression group.

CONCLUSION

Upregulation of CENPF expression was related to poor progression-free survival, disease- free survival, and overall survival in patients with lung adenocarcinoma. High expression of CENPF was markedly related to genes associated with the immune checkpoint. Lung adenocarcinoma samples with high CENPF expression had increased CD4+ Th2 cell infiltration. Our findings indicate that CENPF promotes CD4+ Th2 cell infiltration through oncogenic activity and may be used as a biomarker for predicting patient outcomes in lung adenocarcinoma.

A robust six-gene prognostic signature based on two prognostic subtypes constructed by chromatin regulators is correlated with immunological features and therapeutic response in lung adenocarcinoma

Accumulating evidence has demonstrated that chromatin regulators (CRs) regulate immune cell infiltration and are correlated with prognoses of patients in some cancers. However, the immunological and prognostic roles of CRs in lung adenocarcinoma (LUAD) are still unclear. Here, we systematically revealed the correlations of CRs with immunological features and the survival in LUAD patients based on a cohort of gene expression datasets from the public TCGA and GEO databases and real RNA-seq data by an integrative analysis using a comprehensive bioinformatics method. Totals of 160 differentially expressed CRs (DECRs) were identified between LUAD and normal lung tissues, and two molecular prognostic subtypes (MPSs) were constructed and evaluated based on 27 prognostic DECRs using five independent datasets (p =0.016, <0.0001, =0.008, =0.00038 and =0.00055, respectively). Six differentially expressed genes (DEGs) (CENPK, ANGPTL4, CCL20, CPS1, GJB3, TPSB2) between two MPSs had the most important prognostic feature and a six-gene prognostic model was established. LUAD patients in the low-risk subgroup showed a higher overall survival (OS) rate than those in the high-risk subgroup in nine independent datasets (p <0.0001, =0.021, =0.016, =0.0099, <0.0001, =0.0045, <0.0001, =0.0038 and =0.00013, respectively). Six-gene prognostic signature had the highest concordance index of 0.673 compared with 19 reported prognostic signatures. The risk score was significantly correlated with immunological features and activities of oncogenic signaling pathways. LUAD patients in the low-risk subgroup benefited more from immunotherapy and were less sensitive to conventional chemotherapy agents. This study provides novel insights into the prognostic and immunological roles of CRs in LUAD.

Integrative pan-cancer analysis of cuproplasia-associated genes for the genomic and clinical characterization of 33 tumors

BACKGROUND

The molecular mechanisms driving tumorigenesis have continually been the focus of researchers. Cuproplasia is defined as copper-dependent cell growth and proliferation, including its primary and secondary roles in tumor formation and proliferation through signaling pathways. In this study, we analyzed the differences in the expression of cuproplasia-associated genes (CAGs) in pan-cancerous tissues and investigated their role in immune-regulation and tumor prognostication.

METHODS

Raw data from 11,057 cancer samples were acquired from multiple databases. Pan-cancer analysis was conducted to analyze the CAG expression, single-nucleotide variants, copy number variants, methylation signatures, and genomic signatures of micro RNA (miRNA)-messenger RNA (mRNA) interactions. The Genomics of Drug Sensitivity in Cancer and the Cancer Therapeutics Response Portal databases were used to evaluate drug sensitivity and resistance against CAGs. Using single-sample Gene Set Enrichment Analysis (ssGSEA) and Immune Cell Abundance Identifier database, immune cell infiltration was analyzed with the ssGSEA score as the standard.

RESULTS

Aberrantly expressed CAGs were found in multiple cancers. The frequency of single-nucleotide variations in CAGs ranged from 1% to 54% among different cancers. Furthermore, the correlation between CAG expression in the tumor microenvironment and immune cell infiltration varied among different cancers. ATP7A and ATP7B were negatively correlated with macrophages in 16 tumors including breast invasive carcinoma and esophageal carcinoma, while the converse was true for MT1A and MT2A . In addition, we established cuproplasia scores and demonstrated their strong correlation with patient prognosis, immunotherapy responsiveness, and disease progression ( P  <0.05). Finally, we identified potential candidate drugs by matching gene targets with existing drugs.

CONCLUSIONS

This study reports the genomic characterization and clinical features of CAGs in pan-cancers. It helps clarify the relationship between CAGs and tumorigenesis, and may be helpful in the development of biomarkers and new therapeutic agents.

Transcriptional profiles reveal histologic origin and prognosis across 33 The Cancer Genome Atlas tumor types

Background

In recent years, with the development of transcriptome sequencing, the molecular characteristics of tumors are gradually revealed. Because of the complexity of tumor transcriptome, there is a need to look for the molecular signatures which can be used to evaluate the tissue origin and cell stemness of tumors in order to promote the diagnosis and treatment of tumors.

Methods

Tumor tissue-specific gene sets (TTSGs) consisting of 200 genes were selected using RNA expression data of 9,875 patients from 33 tumor types. t-distributed Stochastic Neighbor Embedding (t-SNE) was used for dimensionality reduction and visualization of TTSGs in each tumor type. To evaluate oncogenic dedifferentiation and loss of cell stemness, Euclidean distance from each sample to a human embryo single-cell RNA-seq dataset (GSE36552) of TTSGs was calculated as TTSGs index indicating dissimilarity of tumors and embryo. TTSGs index was evaluated for prognosis in each tumor type. Two published signature indexes, the mRNA signature index (mRNAsi) and CIBERSORT, were compared to assess the correlation between the TTSGs index with cell stemness and immune microenvironment. Finally, the difference of prognosis, immune microenvironment and radiotherapy outcomes were compared between patients with high and low TTSGs index.

Results

In this study, all 33 tumor types in The Cancer Genome Atlas (TCGA) were embedded into isolated clusters by t-SNE and confirmed by k-nearest neighbors (kNN) algorithm. Clusters of squamous-cell carcinoma were adjacent to each other revealing similar histologic origin. Basal-like breast cancer was separated from luminal and HER-2-amplified subtypes and closed to squamous-cell carcinoma. TTSGs index was related to overall survival outcomes in cancers derived from liver, thyroid, brain, cervical and kidney. There was a positive correlation between mRNAsi and TTSGs index in pan-kidney and pan-neuronal cancers. Furthermore, cell fractions of M2 macrophages and total leukocytes increased in the group with higher TTSGs index. Patients with higher TTSGs index had longer overall survival time and less radiation therapy resistance compared to patients with lower TTSGs index.

Conclusions

The signature of TTSGs is related to tumor expression features that distinguish tumors of different histologic origin using t-SNE. The signature also relates to prognosis of certain kinds of tumors.

Automated Cellular-Level Dual Global Fusion of Whole-Slide Imaging for Lung Adenocarcinoma Prognosis

Histopathologic whole-slide images (WSI) are generally considered the gold standard for cancer diagnosis and prognosis. Survival prediction based on WSI has recently attracted substantial attention. Nevertheless, it remains a central challenge owing to the inherent difficulties of predicting patient prognosis and effectively extracting informative survival-specific representations from WSI with highly compounded gigapixels. In this study, we present a fully automated cellular-level dual global fusion pipeline for survival prediction. Specifically, the proposed method first describes the composition of different cell populations on WSI. Then, it generates dimension-reduced WSI-embedded maps, allowing for efficient investigation of the tumor microenvironment. In addition, we introduce a novel dual global fusion network to incorporate global and inter-patch features of cell distribution, which enables the sufficient fusion of different types and locations of cells. We further validate the proposed pipeline using The Cancer Genome Atlas lung adenocarcinoma dataset. Our model achieves a C-index of 0.675 (±0.05) in the five-fold cross-validation setting and surpasses comparable methods. Further, we extensively analyze embedded map features and survival probabilities. These experimental results manifest the potential of our proposed pipeline for applications using WSI in lung adenocarcinoma and other malignancies.

Integrated models of blood protein and metabolite enhance the diagnostic accuracy for Non-Small Cell Lung Cancer

BACKGROUND

For early screening and diagnosis of non-small cell lung cancer (NSCLC), a robust model based on plasma proteomics and metabolomics is required for accurate and accessible non-invasive detection. Here we aim to combine TMT-LC-MS/MS and machine-learning algorithms to establish models with high specificity and sensitivity, and summarize a generalized model building scheme.

METHODS

TMT-LC-MS/MS was used to discover the differentially expressed proteins (DEPs) in the plasma of NSCLC patients. Plasma proteomics-guided metabolites were selected for clinical evaluation in 110 NSCLC patients who were going to receive therapies, 108 benign pulmonary diseases (BPD) patients, and 100 healthy controls (HC). The data were randomly split into training set and test set in a ratio of 80:20. Three supervised learning algorithms were applied to the training set for models fitting. The best performance models were evaluated with the test data set.

RESULTS

Differential plasma proteomics and metabolic pathways analyses revealed that the majority of DEPs in NSCLC were enriched in the pathways of complement and coagulation cascades, cholesterol and bile acids metabolism. Moreover, 10 DEPs, 14 amino acids, 15 bile acids, as well as 6 classic tumor biomarkers in blood were quantified using clinically validated assays. Finally, we obtained a high-performance screening model using logistic regression algorithm with AUC of 0.96, sensitivity of 92%, and specificity of 89%, and a diagnostic model with AUC of 0.871, sensitivity of 86%, and specificity of 78%. In the test set, the screening model achieved accuracy of 90%, sensitivity of 91%, and specificity of 90%, and the diagnostic model achieved accuracy of 82%, sensitivity of 77%, and specificity of 86%.

CONCLUSIONS

Integrated analysis of DEPs, amino acid, and bile acid features based on plasma proteomics-guided metabolite profiling, together with classical tumor biomarkers, provided a much more accurate detection model for screening and differential diagnosis of NSCLC. In addition, this new mathematical modeling based on plasma proteomics-guided metabolite profiling will be used for evaluation of therapeutic efficacy and long-term recurrence prediction of NSCLC.

Multiple Omics Analysis of the Role of RBM10 Gene Instability in Immune Regulation and Drug Sensitivity in Patients with Lung Adenocarcinoma (LUAD)

OBJECTIVE

The RNA-binding protein RBM10 can regulate apoptosis during the proliferation and migration of pancreatic cancer, endometrial cancer, and osteosarcoma cells; however, the molecular mechanism underlying lung adenocarcinoma is rarely reported. Recent studies have detected multiple truncated and missense mutations in RBM10 in lung adenocarcinoma, but the role of RBM10 in lung adenocarcinoma is unclear. This study mainly explored the immune regulation mechanism of RBM10 in the development of lung adenocarcinoma and its influence on sensitivity to targeted therapy drugs.

METHODS

The transcriptome data of CGAP were used to analyze the RNA-seq data of lung adenocarcinoma patients from different subgroups by using the CIBERSORT algorithm to infer the relative proportion of various immune infiltrating cells, and Spearman correlation analysis was performed to determine the gene expression and immune cell content. In addition, this study utilized drug trial data from the GDSC database. The IC50 estimates for each specific targeted therapy were obtained by using a regression method, and the regression and prediction accuracy were tested via ten cross-validations with the GDSC training set. An immunohistochemical test was performed on the samples of 20 patients with lung adenocarcinoma in the subcomponent analysis of immune cells, and the protein expression of RBM10 in lung adenocarcinoma tissues was verified by cellular immunofluorescence assays. Nucleic acids were extracted at low temperatures, and qRT-PCR was used to verify the expression levels of the mRNA of RBM10 in lung adenocarcinoma tissues and normal tissues (p < 0.05).

RESULTS

After screening and inclusion using a machine language, the results showed that RBM10 was significantly highly expressed in the lung adenocarcinoma tissues. The related signaling pathways were mainly concentrated in ncRNA processing, rRNA metabolic processes, ribosome biogenesis, and the regulation of translation. The qRT-PCR for 20 lung adenocarcinoma tissues showed that the expression of RBM10 in these tissues was significantly different from that in normal tissues (p = 0.0255). Immunohistochemistry analysis and cell immunofluorescence staining also confirmed that RBM10 was involved in the immune regulation of lung adenocarcinoma tissues, and the number of immune cell aggregations was significantly higher than that of the control group. RBM10 regulates B cell memory-CIBERSORT (p = 0.042) and B cell memory-CIBERSOTRT-abs (p = 0.027), cancer-associated fibroblast-EPIC (p = 0.001), cancer-associated fibroblast- MCPCounter (p = 0.0037), etc. The risk score was significantly associated with the sensitivity of patients to lapatinib (p = 0.049), nilotinib (p = 0.015), pazopanib (p = 0.001), and sorafenib (p = 0.048).

CONCLUSIONS

RBM10 can inhibit the proliferation and invasion of lung adenocarcinoma cells through negative regulation and promote the apoptosis of lung adenocarcinoma cells through immunomodulatory mechanisms. The expression level of RBM10 affects the efficacy of targeted drug therapy and the survival prognosis of lung adenocarcinoma patients, which has a certain guiding significance for the clinical treatment of these patients.

Total alkaloids in Stephania tetrandra induce apoptosis by regulating BBC3 in human non-small cell lung cancer cells

PURPOSE

This study investigated the effects of total alkaloids in Stephania tetrandra (TAS) and the main alkaloid components tetrandrine, fangchinoline and cepharanthine on the biological function of lung cancer cells and the mechanism underlying the synergistic antitumor effects of TAS and cisplatin.

METHODS

RNA sequencing analysis was performed on TAS-treated H1299 cells. Differentially expressed genes were identified and analyzed, and the regulatory pathway was identified by gene set enrichment analysis. The mRNA and protein expression levels of the differentially expressed genes in cells were determined using quantitative reverse transcription-polymerase chain reaction and western blotting, respectively. Cell viability and wound healing assays evaluated the biological function of TAS and the main alkaloid components in non-small cell lung cancer (NSCLC) cells. Flow cytometry was used to determine the apoptosis rate in NSCLC cells.

RESULTS

TAS inhibited the proliferation and migration of A549 and H1299 cells and increased the apoptosis rate in a time- and dose-dependent manner. When H1299 cells were treated with TAS (7.5 µg/ml), MGLL and BBC3 were identified as the possible differentially expressed genes. Pathways associated with cisplatin resistance were screened to investigate the effect of TAS on the apoptosis of NSCLC cells. TAS may regulate fatty acid metabolism and induce apoptosis through the upregulated expression of MGLL and BBC3. The combination of TAS at noncytotoxic concentrations (A549: 1.0 μg/ml; H1299: 3.0 μg/ml) and cisplatin significantly inhibited the viability of A549 and H1299 cells.

CONCLUSION

TAS and the main alkaloid components exert anticancer activity in NSCLC by regulating tumor cell proliferation and apoptosis. Therefore, TAS and the main alkaloid components have the potential to be used as multi-targeted drugs for lung cancer treatment.

Proteomics analysis of serum from thymoma patients

Thymoma is the most common malignant tumor in thymic epithelial tumors (TETS). This study aimed to identify the changes in serum proteomics in patients with thymoma. Proteins were extracted from twenty patients with thymoma serum and nine healthy controls and prepared for mass spectrometry (MS) analysis. Data independent acquisition (DIA) quantitative proteomics technique was used to examine the serum proteome. Differential proteins of abundance changes in the serum were identified. Bioinformatics was used to examine the differential proteins. Functional tagging and enrichment analysis were conducted using the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) databases. The string database was used to assess the interaction of different proteins. In all, 486 proteins were found in all samples. There were differences in 58 serum proteins between patients and healthy blood donors, 35 up-regulated and 23 down-regulated. These proteins are primarily exocrine and serum membrane proteins involved in controlling immunological responses and antigen binding, according to GO functional annotation. KEGG functional annotation showed that these proteins play a significant role in the complement and coagulation cascade and the phosphoinositide 3-kinase (PI3K)/protein kinase B (AKT) signal pathway. Notably, the KEGG pathway (complement and coagulation cascade) is enriched, and three key activators were up-regulated: von willebrand factor (VWF), coagulation factor v (F5) and vitamin k-dependent protein c (PC). Protein-protein interaction (PPI) analysis showed that six proteins ((VWF, F5, thrombin reactive protein 1 (THBS1), mannose-binding lectin-associated serine protease 2 (MASP2), apolipoprotein B (APOB), and apolipoprotein (a) (LPA)) were up-regulated and two proteins (Metalloproteinase inhibitor 1(TIMP1), ferritin light chain (FTL)) were down-regulated. The results of this study showed that several proteins involved in complement and coagulation cascades were up-regulated in the serum of patients.

The ethyl acetate extract of Wenxia Changfu Formula inhibits the carcinogenesis of lung adenocarcinoma by regulating PI3K-AKT signaling pathway

Lung adenocarcinoma is the most common type of lung cancer. With a rise in new cases worldwide each year, early diagnosis and treatment are very important. Network pharmacology provides the effective way to evaluate poly-pharmacological effects and anticancer molecular mechanisms of drugs. The aim of the present study was to explore the anti-tumor mechanism of ethyl acetate extract of Wenxia Changfu Formula (WFEA) in lung adenocarcinoma by using analytical chemistry, network pharmacology and molecular biology. A total of 193 compounds were identified from WFEA, mainly including esters, phenols, ketones and alkaloids. Totally, 374 targets were regarded as potential targets of WFEA against lung adenocarcinoma. Interestingly, PI3K-AKT was found to be one of the significantly enriched signaling pathways of targets of WFEA against lung adenocarcinoma. AKT1, MMP3, CASP3 and BCL2 had strong binding effect with compound molecules of WFEA. Some combinations with the best docking binding were identified, including quercetin/oleanolic_acid/emodin/aloe_emodin/catechin-AKT1 and quercetin-MMP3. In lung adenocarcinoma cells, the WFEA inhibited the proliferation, migration and invasion, and promoted the apoptosis. Moreover, the WFEA inhibited the mRNA expression of MMP3 and Bcl-2 and promoted the mRNA expression of Caspase3. In addition, WFEA inhibited the protein phosphorylation of AKT and PI3K. The WFEA had a significant inhibitory effect on lung adenocarcinoma cells, which could inhibit cell proliferation, invasion and metastasis, and induce cell apoptosis. The mechanism of action of WFEA may be involved in the regulation of the PI3K-AKT signaling pathway in the lung adenocarcinoma.

Circular RNA Circ_0005962 Contributes to Lung Adenocarcinoma Cell Proliferation and Stem Cell Formation Through Sponging of miR-3611 and Modulating CYP24A1 Expression

Recently, several studies have revealed that circular RNAs (circRNAs) play significant roles in various tumors, including lung adenocarcinoma (LUAD). Furthermore, it has been reported that circ_0005962 was upregulated in LUAD cells. Accordingly, this research is designed to investigate the mechanism of circ_0005962 on LUAD development. Circ_0005962, microRNA-3611 (miR-3611), and Cytochrome P450 family 24 subfamily A member 1 (CYP24A1) level were detected by real-time quantitative polymerase chain reaction (RT-qPCR). Cell proliferation ability, cell cycle progression, and sphere formation ability were detected using Cell Counting Kit-8 (CCK-8), 5-ethynyl-2'-deoxyuridine (EdU), Colony formation, flow cytometry, and sphere formation assay. Protein levels of proliferating cell nuclear antigen (PCNA), Ki67, NANOG, CD133, OCT4, and CYP24A1 were determined using Western blot assay. Using bioinformatics software (Starbase3.0 and TargetScan), the binding between miR-3611 and circ_0005962 or CYP24A1 was predicted and proved using RNA Immunoprecipitation (RIP) and RNA pull-down assays. A xenograft tumor model in vivo was used to analyze the biological role of circ_0005962 on LUAD cell growth. Increased circ_0005962 and CYP24A1, and reduced miR-3611 were observed in LUAD tissues and cell lines. Functional assays testified that circ_0005962 depletion might hinder LUAD cell proliferation and sphere formation capability, but induced cell apoptosis in vitro. Molecular mechanism experiments exhibited that circ_0005962 served as a miR-3611 sponge and mediated CYP24A1 content by absorbing miR-3611. Additionally, silencing of circ_0005962 inhibited tumor growth in xenograft modes. Together, circ_0005962 was overexpressed in LUAD, and its deficiency repressed LUAD progression via targeting the miR-3611/ CYP24A1 axis, providing a novel mechanism for understanding the development of LUAD.

Identification of prognostic values of the transcription factor-CpG-gene triplets in lung adenocarcinoma: A narrative review

OBJECTIVE

Abnormal DNA methylation can regulate carcinogenesis in lung adenocarcinoma (LUAD), while transcription factors (TFs) mediate methylation in a site-specific manner to affect downstream transcriptional regulation and tumor progression. Therefore, this study aimed to explore the TF-methylation-gene regulatory relationships that influence LUAD prognosis.

METHODS

Differential analyses of methylation sites and genes were generated by integrating transcriptome and methylome profiles from public databases. Through target gene identification, motif enrichment in the promoter region, and TF prediction, TF-methylation and methylation-gene relation pairs were obtained. Then, the prognostic TF-methylation-gene network was constructed using univariate Cox regression analysis. Prognostic models were constructed based on the key regulatory axes. Finally, Kaplan-Meier curves were created to evaluate the model efficacy and the relationship between candidate genes and prognosis.

RESULTS

A total of 1878 differential expressed genes and 1233 differential methylation sites were screened between LUAD and normal samples. Then 10 TFs were predicted to bind 144 enriched motifs. After integrating TF-methylation and methylation-gene relations, a prognostic TF-methylation-gene network containing 4 TFs, 111 methylation sites, and 177 genes was constructed. In this network, ERG-cg27071152-MTURN and FOXM1-cg19212949-PTPR regulatory axes were selected to construct the prognostic models, which showed robust abilities in predicting 1-, 3-, and 5-year survival probabilities. Finally, ERG and MTURN were downregulated in LUAD samples, whereas FOXM1 and PTPR were upregulated. Their expression levels were related to LUAD prognosis.

CONCLUSION

ERG-cg27071152-MTURN and FOXM1-cg19212949-PTPR regulatory axes were proposed as potential biomarkers for predicting the prognosis of LUAD.

In lung adenocarcinoma, low expression of the cell surface extracellular nucleotidase CD39 is related to immune infiltration and a poor prognosis

Background

Extracellular nucleotidase on the cell surface CD39 plays a crucial role in the tumor microenvironment in the immunosuppressive adenosine pathway. However, the association between CD39 and lung adenocarcinoma has rarely been recorded. This study aimed to explore the involvement of CD39 in the biological processes of lung cancer.

Methods

First, a prediction model was established by analyzing the expression of CD39 in lung adenocarcinoma and its relationships with clinical evidence of lung adenocarcinoma using The Cancer Genome Atlas (TCGA) and Tumor IMmune Estimation Resource (TIMER) databases. In the TCGA and TIMER databases, the relationship between CD39 and immune cells and the relationship with immune-related expressed genes were studied. Subsequently, using gene set enrichment analysis (GSEA), the potential mechanism of action was investigated.

Results

Lung adenocarcinoma patients with elevated CD39 expression had improved overall survival (OS), disease-specific survival (DSS), and progression-free interval (PFI). CD39 expression was reduced in lung adenocarcinoma tumor tissue in the TCGA and TIMER databases. The nomogram's C-index was 0.688 (0.665-0.712), indicating some consistency in the prediction model. According to the TIMER and TCGA databases, CD39 expression was strongly connected with several immune cells invading and with immune checkpoint-related markers such as PDCD1, CD274, CTLA-4, and several functional T cells. GSEA revealed that CD39 influences the extracellular matrix, immunological microenvironment, programmed death 1 (PD-1) expression, glucose metabolism, PTEN stability, inflammatory response, and angiogenesis in lung cancer.

Conclusions

The current study's findings demonstrated that CD39 can be employed as a possible predictive biomarker for lung adenocarcinoma and may enhance the patients' poor prognosis by preventing the immunological escape of tumor cells from the lung adenocarcinoma tumor microenvironment.

Signatures of Co-Deregulated Genes and Their Transcriptional Regulators in Lung Cancer

Despite the significant progress made towards comprehending the deregulated signatures in lung cancer, these vary from study to study. We reanalyzed 25 studies from the Gene Expression Omnibus (GEO) to detect and annotate co-deregulated signatures in lung cancer and in single-gene or single-drug perturbation experiments. We aimed to decipher the networks that these co-deregulated genes (co-DEGs) form along with their upstream regulators. Differential expression and upstream regulators were computed using Characteristic Direction and Systems Biology tools, including GEO2Enrichr and X2K. Co-deregulated gene expression profiles were further validated across different molecular and immune subtypes in lung adenocarcinoma (TCGA-LUAD) and lung adenocarcinoma (TCGA-LUSC) datasets, as well as using immunohistochemistry data from the Human Protein Atlas, before being subjected to subsequent GO and KEGG enrichment analysis. The functional alterations of the co-upregulated genes in lung cancer were mostly related to immune response regulating the cell surface signaling pathway, in contrast to the co-downregulated genes, which were related to S-nitrosylation. Networks of hub proteins across the co-DEGs consisted of overlapping TFs (SOX2, MYC, KAT2A) and kinases (MAPK14, CSNK2A1 and CDKs). Furthermore, using Connectivity Map we highlighted putative repurposing drugs, including valproic acid, betonicine and astemizole. Similarly, we analyzed the co-DEG signatures in single-gene and single-drug perturbation experiments in lung cancer cell lines. In summary, we identified critical co-DEGs in lung cancer providing an innovative framework for their potential use in developing personalized therapeutic strategies.

The expression of POSTN and immune cell infiltration are prognostic factors of lung adenocarcinoma

The objective of this study is to identify useful prognostic factors of lung adenocarcinoma (LUAD) by evaluating the changes of periostin (POSTN) expression activity and immune cell infiltration using public data repositories. The gene and protein expressions of POSTN in LUAD were collected and analyzed from Ualcan and Human Protein Atlas online database. The cell infiltration data of immune cells in LUAD patients were retrieved and processed using the TIMER tool. Cox regression analysis was employed to screen and characterize prognosis factors, Kaplan-Meier Plotter was used to analyze the survival curve of LUAD patients, and receiver operating characteristic curve was used to calculate the relationship between temporal POSTN expressions and the prognosis of LUAD. The expression activity of POSTN in LUAD was higher than that in normal tissues. With the exception of B cell which showed opposite correlationship, the infiltration of most immune cells, namely CD8+ T cells, macrophages, neutrophils, and dendritic cells, was positively correlated with the POSTN expression activity. Together, our investigation suggested that the POSTN expression activity and B-cell infiltration were the prognostic factors of LUAD. In addition, the 1-year negative predictive value of the POSTN expression activity for overall survival, disease-free survival, and progression-free interval was 0.902, 0.926, and 0.838, respectively. Along with decreased B-cell infiltration, the elevated expression of POSTN is an important factor of LUAD prognosis.

Correlation Between the RNA Methylation Genes and Immune Infiltration and Prognosis of Patients with Hepatocellular Carcinoma: A Pan-Cancer Analysis

Background

RNA methylation is one of the most common RNA modifications and is dynamic and reversible. The enzymes and downstream effectors associated with RNA methylation modifications can be targeted to regulate RNA methylation levels. This mechanism can affect RNA processing, metabolism, cell proliferation and migration, and regulation of physiological or pathological processes. The aim of this study was to investigate the role of RNA methylation-related genes in hepatocellular carcinoma (HCC).

Methods

Baseline RNA methylation data were extracted from The Cancer Genome Atlas database. The expression pattern, predictive value, mutational profile, and interaction network of RNA methylation genes in pancancer were examined. Then, the association between the expression of RNA methylation genes and immune infiltration was investigated. In addition, a risk score model for HCC was developed and analyzed.

Results

Cancer cells had a higher expression of RNA methylation genes than normal cells in some cancer cells, and a higher expression of RNA methylation genes could negatively affect patient prognosis. Enrichment analysis revealed that RNA methylation genes are involved in the mRNA surveillance pathway and RNA degradation and transport. A 4-gene (ALYREF, NSUN4, TRMT6, YTHDF1) prognostic signature was established to predict HCC prognosis based on RNA methylation-related genes. Finally, the role of prognostic models in HCC was validated.

Conclusion

RNA methylation genes can be an indicator of oncogenicity in relation to HCC prognosis and are associated with immune infiltration in the tumour microenvironment. This finding could provide clinicians with the opportunity to explore new strategic approaches.

A Novel Prognostic Signature Revealed the Interaction of Immune Cells in Tumor Microenvironment Based on Single-Cell RNA Sequencing for Lung Adenocarcinoma

Background

The tumor immune microenvironment (TIME) played an important role in immunotherapy prognosis and treatment response. Immune cells constitute a large part of the tumor microenvironment and regulate tumor progression. Our research is dedicated to studying the infiltrating immune cell in lung adenocarcinoma (LUAD) and seeking potential targets.

Methods

The scRNA-seq data were collected from our FDZSH and two public datasets. The code for cell-type mapping algorithms was downloaded from the CIBERSORTx portal. The bioinformatics data of LUAD patients could be approached from The Cancer Genome Atlas (TCGA) portal. Weighted gene coexpression network analysis (WGCNA) and least absolute shrinkage and selection operator (LASSO) analyses were performed to construct a risk model. TIMER2 and TIDE helped with the immune infiltration estimation, while PROGENy helped the cancer-related pathways' enrichment analysis. GSE31210 dataset and IMVigor ICB therapy cohort validated our findings as the external validation datasets.

Results

We clustered the scRNA-seq dataset (integrating our FDZSH datasets and other public datasets) into 23 subpopulations. After curated cell annotation, we implemented Cibersort and WGCNA analysis to anchor the brown module and natural killer cell cluster1 due to the most relationship with tumor trait. The overlap of the brown module gene, natural killer cell signature, and DEGs of tumor and adjacent normal samples was screened by LASSO Cox regression. The obtained 5-gene risk model showed an excellent prognostic performance in the validation dataset. Furthermore, there was a correlation between risk score and tumor-infiltrating immune cells and tumor genomics abnormity. Patients with higher risk scores had a significantly lower immunotherapy response rate.

Conclusion

Our observations implied that immune cells played a pivotal role in TIME and established a 5-gene signature (including IDH2, ADRB2, SFTPC, CCDC69, and CCND2) on the basement of nature killer markers targeted by WGCNA analysis. The significance of clinical outcome and immunotherapy response prediction was validated robustly.

Identification of Small Nucleolar RNA SNORD60 as a Potential Biomarker and Its Clinical Significance in Lung Adenocarcinoma

Non-small-cell lung cancer (NSCLC) is the leading cause of cancer deaths in the world and often diagnosed at an advanced stage, so it is urgent to explore the pathogenesis and new diagnostic biomarkers. Accumulated evidences suggested that small nucleolar RNAs (snoRNAs) played a key role in the development and progression of NSCLC. To examine differential expression snoRNA profile and identify snoRNAs with clinical significance in lung adenocarcinoma (LUAD), The Cancer Genome Atlas (TCGA) LUAD RNA sequencing dataset was used to investigate differential expression snoRNA signatures and compared with snoRNA PCR array analysis in pair-matched LUAD tissues. The diagnostic ability of SONRD60 was assessed using a receiver operating characteristic (ROC) curve. The Kaplan-Meier method was used to plot survival curves. Univariate and multivariate Cox regression analyses were used to investigate the prognostic effect of SNORD60 expression on LUAD. The results showed that SNORD60 was a significantly upregulated snoRNA after intersection analysis in LUAD cases. SNORD60 has 74.2% sensitivity and 75.3% specificity for the diagnosis of LUAD. Increased SNORD60 expression was linked with lymph node metastases and the TNM stage (P < 0.05). Pathological T category and lymph node metastases were independent prognostic factors for overall survival in a multivariate Cox regression study. Our findings demonstrated that SNORD60, a small nucleolar RNA, has an oncogenic function in LUAD and might be used as a new early diagnostic biomarker for LUAD.

Clinical Implications of Necroptosis Genes Expression for Cancer Immunity and Prognosis: A Pan-Cancer Analysis

Background

Necroptosis, a form of programmed cell death, is increasingly being investigated for its controversial role in tumorigenesis and progression. Necroptosis suppresses tumor formation and tumor development by killing tumor cells; however, the necrotic cells also promote tumor formation and tumor development via the immunosuppressive effect of necroptosis and inflammatory response caused by cytokine release. Thus, the exact mechanism of necroptosis in pan-cancer remains unknown.

Methods

The data of 11,057 cancer samples were downloaded from the TCGA database, along with clinical information, tumor mutation burden, and microsatellite instability information of the corresponding patients. We used the TCGA data in a pan-cancer analysis to identify differences in mRNA level as well as single nucleotide variants, copy number variants, methylation profiles, and genomic signatures of miRNA-mRNA interactions. Two drug datasets (from GDSC, CTRP) were used to evaluate drug sensitivity and resistance against necroptosis genes.

Results

Necroptosis genes were aberrantly expressed in various cancers. The frequency of necroptosis gene mutations was highest in lung squamous cell carcinoma. Furthermore, the correlation between necroptosis gene expression in the tumor microenvironment and immune cell infiltration varied for different cancers. High necroptosis gene expression was found to correlate with NK, Tfh, Th1, CD8_T, and DC cells. These can therefore be used as biomarkers to predict prognosis. By matching gene targets with drugs, we identified potential candidate drugs.

Conclusion

Our study showed the genomic alterations and clinical features of necroptosis genes in 33 cancers. This may help clarify the link between necroptosis and tumorigenesis. Our findings may also provide new approaches for the clinical treatment of cancer.

Drug repositioning in non-small cell lung cancer (NSCLC) using gene co-expression and drug–gene interaction networks analysis

Lung cancer is the most common cancer in men and women. This cancer is divided into two main types, namely non-small cell lung cancer (NSCLC) and small cell lung cancer (SCLC). Around 85 to 90 percent of lung cancers are NSCLC. Repositioning potent candidate drugs in NSCLC treatment is one of the important topics in cancer studies. Drug repositioning (DR) or drug repurposing is a method for identifying new therapeutic uses of existing drugs. The current study applies a computational drug repositioning method to identify candidate drugs to treat NSCLC patients. To this end, at first, the transcriptomics profile of NSCLC and healthy (control) samples was obtained from the GEO database with the accession number GSE21933. Then, the gene co-expression network was reconstructed for NSCLC samples using the WGCNA, and two significant purple and magenta gene modules were extracted. Next, a list of transcription factor genes that regulate purple and magenta modules' genes was extracted from the TRRUST V2.0 online database, and the TF–TG (transcription factors–target genes) network was drawn. Afterward, a list of drugs targeting TF–TG genes was obtained from the DGIdb V4.0 database, and two drug–gene interaction networks, including drug-TG and drug-TF, were drawn. After analyzing gene co-expression TF–TG, and drug–gene interaction networks, 16 drugs were selected as potent candidates for NSCLC treatment. Out of 16 selected drugs, nine drugs, namely Methotrexate, Olanzapine, Haloperidol, Fluorouracil, Nifedipine, Paclitaxel, Verapamil, Dexamethasone, and Docetaxel, were chosen from the drug-TG sub-network. In addition, nine drugs, including Cisplatin, Daunorubicin, Dexamethasone, Methotrexate, Hydrocortisone, Doxorubicin, Azacitidine, Vorinostat, and Doxorubicin Hydrochloride, were selected from the drug-TF sub-network. Methotrexate and Dexamethasone are common in drug-TG and drug-TF sub-networks. In conclusion, this study proposed 16 drugs as potent candidates for NSCLC treatment through analyzing gene co-expression, TF–TG, and drug–gene interaction networks.

Construction and evaluation of a nomogram for predicting survival in patients with lung cancer

BACKGROUND

Lung cancer is a heterogeneous disease with a severe disease burden. Because the prognosis of patients with lung cancer varies, it is critical to identify effective biomarkers for prognosis prediction.

METHODS

A total of 2325 lung cancer patients were integrated into four independent sets (training set, validation set I, II and III) after removing batch effects in our study. We applied the microarray data algorithm to screen the differentially expressed genes in the training set. The most robust markers for prognosis were identified using the LASSO-Cox regression model, which was then used to create a Cox model and nomogram.

RESULTS

Through LASSO and multivariate Cox regression analysis, eight genes were identified as prognosis-associated hub genes, followed by the creation of prognosis-associated risk scores (PRS). The results of the Kaplan-Meier analysis in the three validation sets demonstrate the good predictive performance of PRS, with hazard ratios of 2.38 (95% confidence interval (CI), 1.61-3.53) in the validation set I, 1.35 (95% CI, 1.06-1.71) in the validation set II, and 2.71 (95% CI, 1.77-4.18) in the validation set III. Additionally, the PRS demonstrated superior survival prediction in subgroups by age, gender, p-stage, and histologic type (p < 0.0001). The complex model integrating PRS and clinical risk factors also have a good predictive performance for 3-year overall survival.

CONCLUSIONS

In this study, we developed a PRS signature to help predict the survival of lung cancer. By combining it with clinical risk factors, a nomogram was established to quantify the individual risk assessments.

Genetic polymorphisms and protein levels in vocal fold leukoplakia: a systematic review

Vocal fold leukoplakia (VFL) has a risk of malignant transformation. Therefore, patients can have symptoms such as dysphonia, vocal strain, difficulty breathing, and dysphagia. Additionally, there is a genetic predisposition that can be associated with genetic polymorphisms. We aimed to evaluate the influence of genetic polymorphisms and protein levels in the etiology of VFL. Our study followed the PRISMA checklist and was registered on PROSPERO database. The questions were: "Are genetic polymorphisms involved in the etiology of VFL? Are protein levels altered in patients with VFL?". Eligibility criteria were case control studies that compared the presence of polymorphisms or/and protein levels of subjects diagnosed with VFL and healthy controls. Of the 905 articles retrieved, five articles with a total of 1038 participants were included in this study. The C allele of the single nucleotide polymorphisms (SNP)-819 T/C IL-10, A allele of the SNP -592 A/C IL-10, CT genotype of the SNP rs11886868 C/T BCL11A, GG genotype of the SNP rs4671393 A/G BCL11A, LL genotype, and L allele of (GT)n repeat polymorphisms of the HO-1 were risk factors for VFL development. Nevertheless, there was a lack of association between VFL and the -1082 A/G IL-10, rs14024 CK-1, and -309 T/G Mdm2 SNPs. The concentrations of the MDM2, BCL11A, and HO-1 proteins were modified, while IL-10 levels were normally expressed in these subjects. In conclusion, most markers evaluated in this review could be potential indicators to develop effective therapies, avoiding a malignant transformation of the lesion.

Modelling the bioinformatics tertiary analysis research process

Background

With the advancements of Next Generation Techniques, a tremendous amount of genomic information has been made available to be analyzed by means of computational methods. Bioinformatics Tertiary Analysis is a complex multidisciplinary process that represents the final step of the whole bioinformatics analysis pipeline. Despite the popularity of the subject, the Bioinformatics Tertiary Analysis process has not yet been specified in a systematic way. The lack of a reference model results into a plethora of technological tools that are designed mostly on the data and not on the human process involved in Tertiary Analysis, making such systems difficult to use and to integrate.

Methods

To address this problem, we propose a conceptual model that captures the salient characteristics of the research methods and human tasks involved in Bioinformatics Tertiary Analysis. The model is grounded on a user study that involved bioinformatics specialists for the elicitation of a hierarchical task tree representing the Tertiary Analysis process. The outcome was refined and validated using the results of a vast survey of the literature reporting examples of Bioinformatics Tertiary Analysis activities.

Results

The final hierarchical task tree was then converted into an ontological representation using an ontology standard formalism. The results of our research provides a reference process model for Tertiary Analysis that can be used both to analyze and to compare existing tools, or to design new tools.

Conclusions

To highlight the potential of our approach and to exemplify its concrete applications, we describe a new bioinformatics tool and how the proposed process model informed its design.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04310-5.

Sophorae tonkinensis radix et rhizome-induced pulmonary toxicity: A study on the toxic mechanism and material basis based on integrated omics and bioinformatics analyses

The root and rhizome of Sophora tonkinensis Gagnep. (ST) are widely used for the treatment of tonsillitis, sore throats, and heat-evil-induced diseases in traditional Chinese medicine. However, the clinical application of ST is relatively limited due to its toxicity. The mechanism and material basis of ST-induced pulmonary toxicity are still unclear. In the present research, integrated omics and bioinformatics analyses were used to investigate the toxic mechanism and material basis of ST in lung tissue. Proteomics and metabonomics were integrated to analyze the differentially expressed proteins and metabolites. Joint pathway analysis was used to analyze the significantly dysregulated pathways. PubChem and the Comparative Toxicogenomics Database were applied for the screen of toxic targets and compounds. Integrated omics revealed that 323 proteins and 50 metabolites were differentially expressed after treating with ST, out of which 19 proteins and 1 metabolite were significantly enriched in seven pathways. Bioinformatics showed that 15 compounds may indirectly affect the expression of 9 toxic targets of ST. Multiple toxic targets of ST-induced pulmonary injury were found in the study, whose dysregulation may trigger pulmonary cancer, dyspnea, and oxidative stress. Multiple compounds may be the toxic material basis in response to these effects.

Elastic Net Models Based on DNA Copy Number Variations Predicts Clinical Features, Expression Signatures, and Mutations in Lung Adenocarcinoma

In the precision medicine of lung adenocarcinoma, the identification and prediction of tumor phenotypes for specific biomolecular events are still not studied in depth. Various earlier researches sheds light on the close correlation between genetic expression signatures and DNA copy number variations (CNVs), for which analysis of CNVs provides valuable information about molecular and phenotypic changes in tumorigenesis. In this study, we propose a comprehensive analysis combining genome-wide association analysis and an Elastic Net Regression predictive model, focus on predicting the levels of many gene expression signatures in lung adenocarcinoma, based upon DNA copy number features alone. Additionally, we predicted many other key phenotypes, including clinical features (pathological stage), gene mutations, and protein expressions. These Elastic Net prediction methods can also be applied to other gene sets, thereby facilitating their use as biomarkers in monitoring therapy.

Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data

The development of new computational approaches that are able to design the correct personalized drugs is the crucial therapeutic issue in cancer research. However, tumor heterogeneity is the main obstacle to developing patient-specific single drugs or combinations of drugs that already exist in clinics. In this study, we developed a computational approach that integrates copy number alteration, gene expression, and a protein interaction network of 73 basal breast cancer samples. 2509 prognostic genes harboring a copy number alteration were identified using survival analysis, and a protein–protein interaction network considering the direct interactions was created. Each patient was described by a specific combination of seven altered hub proteins that fully characterize the 73 basal breast cancer patients. We suggested the optimal combination therapy for each patient considering drug–protein interactions. Our approach is able to confirm well-known cancer related genes and suggest novel potential drug target genes. In conclusion, we presented a new computational approach in breast cancer to deal with the intra-tumor heterogeneity towards personalized cancer therapy.

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.