Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis

Early detection and diagnosis of cancer with interpretable machine learning to uncover cancer-specific DNA methylation patterns

Cancer, a collection of more than two hundred different diseases, remains a leading cause of morbidity and mortality worldwide. Usually detected at the advanced stages of disease, metastatic cancer accounts for 90% of cancer-associated deaths. Therefore, the early detection of cancer, combined with current therapies, would have a significant impact on survival and treatment of various cancer types. Epigenetic changes such as DNA methylation are some of the early events underlying carcinogenesis. Here, we report on an interpretable machine learning model that can classify 13 cancer types as well as non-cancer tissue samples using only DNA methylome data, with 98.2% accuracy. We utilize the features identified by this model to develop EMethylNET, a robust model consisting of an XGBoost model that provides information to a deep neural network that can generalize to independent data sets. We also demonstrate that the methylation-associated genomic loci detected by the classifier are associated with genes, pathways and networks involved in cancer, providing insights into the epigenomic regulation of carcinogenesis.

Increased FGF2 expression promotes immune cell infiltration and correlates with an unfavorable prognosis in thyroid cancer

To delve into the expression and functions of FGF2 in patient with thyroid cancer (THCA), we conducted a systematic analysis of the association of FGF2 with immune cell infiltration, and prognosis in THCA patients. The transcription and protein levels, methylation, and biological properties of FGF2 were examined, along with its correlation with prognosis and immune cell infiltration in THCA patients using online databases UALCAN, Human Protein Atlas, Kaplan-Meier Plotter, DNMIVD, cBioPortal, GEPIA, Metascape, Linkedomics and TIMER. Clinical samples were collected for Western blot analyses. FGF2 was substantially overexpressed in the tumor group and shown correlations with age, tumor histology, nodal metastasis, and cancer stages. Moreover, higher expression of FGF2 (HR = 3.42, 95 % CI:1.57-7.44, p = 0.00099) was greatly correlated with poorer relapse-free survival in THCA patients, particularly in female patients. FGF2 methylation level was increased in the tumor group (p = 1.29E-6), and higher methylation levels of FGF2 were positively correlated with the poorer progression-free interval in THCA patients (p = 0.015). FGF2 mutations were markedly associated with shorter disease-free survival, with a mutation rate of 6 % among the total 498 THCA patients. FGF2 functions were potentially related to cell adhesion, cytokine-cytokine receptor interaction and angiogenesis. FGF2 expression showed positive correlations with the infiltration of B cells (Cor = 0.569, p = 1.04e-42), CD4+ T cells (Cor = 0.555, p = 9.43e-41), macrophages (Cor = 0.438, p = 2.94e-42), neutrophils (Cor = 0.578, p = 9.354e-45), and dendritic cells (Cor = 0.591, p = 5.00e-47). FGF2 is a potential prognostic marker in THCA patients, with its functions possibly related to cell adhesion, interaction of the cytokine-cytokine receptor, angiogenesis, and the promotion of multiple immune cell infiltration.

Abnormal methylation of HOXA11 promoter promotes tumor progression in testicular germ cell tumor

OBJECTIVE

To investigate the methylation of HOXA11 gene promoter in testicular germ cell tumor (GCT).

METHOD

The clinicopathological data of 63 patients with primary testicular GCT who underwent surgery during Apr. 2019 to Mar. 2021, were retrospectively analyzed. Their GCT tissue and paraneoplastic testicular tissue were obtained, and genomic DNA was extracted from both. The methylation of HOXA11 gene promoter region was detected by methylation-specific PCR (MSP). The incidence of HOXA11 methylation in testicular GCT and adjacent tissues was compared, and the connection between methylation level in testicular GCT and clinicopathologic features of patients was statistically analyzed. Testicular GCT cells were treated with methylated transferase inhibitor 5-Aza-dC in vitro, and HOXA11 mRNA expression was detected by real-time PCR.

RESULTS

The positive rate of HOXA11 promoter methylation in testicular GCT tissues was notably higher than that of paired adjacent tissues (P<0.05). The abnormal methylation of HOXA11 gene promoter was correlated with lymph node metastasis and TNM stage in patients (P<0.05). HOXA11 mRNA expression in testicular GCT cells treated with 5-Aza-dC was increased (P<0.05).

CONCLUSION

Abnormal methylation of HOXA11 gene promoter in testicular germ cell tumor tissue inhibits transcription and expression of HOXA11 gene. The abnormal methylation of HOXA11 promoter region is tightly associated with lymph node metastasis and TNM staging in testicular germ cell tumors.

Construction of lncRNA/Pseudogene-miRNA Network Based on In Silico Approaches for Glycolysis Pathway to Identify Prostate Adenocarcinoma-Related Potential Biomarkers

LncRNAs, pseudogenes, and miRNAs participate a fundamental function in tumorigenesis, metabolism, and invasion of cancer cells, although their regulation of tumor glycolysis in prostate adenocarcinoma (PRAD) is thoroughly not well studied. In this study, we applied transcriptomic, proteomic, and medical information to identify glycolysis-related key genes and modules associated with PRAD. Then, the glycolysis-related lncRNA/lncRNAs/pseudogenes-miRNA-mRNA network was constructed. Analysis of DNA methylation status and expression data determined a DNA methylation-dysregulated three-DE-mRNAs signature for predicting diagnosis, ANGPTL4, GNE, and HSPA in PRAD patients and healthy control. Several lncRNAs/pseudogenes, significantly correlated with the overall survival PVT1, CA5BP1, MIRLET7BHG, SNHG12, and ZNF37BP and disease-free survival status, MALAT1, GUSBP11, MIRLET7BHG, and SNHG1, of patients with PRAD were determined. The methylation profile of DE-lncRNA/pseudogenes was significantly proper for predicting PRAD prognostic model. The transcription level of 6 DE-mRNA ANGPTL4, QSOX1, BIK, CLDN3, DDIT4, and TFF3 was correlated with cancer-related fibroblast infiltration in PRAD. The mutated form of 7 mRNAs, COL5A1, IDH1, HK2, DDIT4, GNE, and QSOX1, was associated with PRAD. In addition to the glycolysis pathway, DE-RNAs play regulatory roles on several pathways, including DNA damage, RTK, cell cycle, RAS/MAPK, TSC/mTOR and PI3K/AKT, AR hormone, and EMT. Overall, our study improves our knowledge of the relation between lncRNAs/pseudogenes and miRNA related to glycolysis and PRAD pathogenesis.

Association of tet methylcytosine dioxygenase 2 and 5-hydroxymethylcytosine in endometrioid adenocarcinoma and its clinical significance

BACKGROUND

Aberrant DNA methylation is a vital molecular alteration commonly detected in type I endometrial cancers (EC), and tet methylcytosine dioxygenase 2 (TET2) and 5-hydroxymethylcytosine (5hmC) play significant roles in DNA demethylation. However, little is known about the function and correlation of TET2 and 5hmC co-expressed in EC. This study intended to investigate the clinical significance of TET2 and 5hmC in EC.

METHODS

The levels of TET2 and 5hmC were detected in 326 endometrial tissues by immumohistochemistry, and the correlation of their level was detected by Pearson analysis. The association between the levels of TET2 and 5hmC and clinicopathologic characteristics was analyzed. Prognostic value of TET2 and 5hmC was explored by Kaplan-Meier analysis. The Cox proportional hazard regression model was used for univariate and multivariate analyses.

RESULTS

Based on the analysis results, TET2 protein level was positively correlated with 5hmC level in EC tissues (r = 0.801, P < 0.001). TET2+5hmC+ (high TET2 and high 5hmC) association was significantly associated with well differentiation, myometrial invasion, negative lymph node metastasis, and tumor stage in EC. Association of TET2 and 5hmC was confirmed as a prognostic factor (HR = 2.843, 95%CI = 1.226-3.605, P = 0.007) for EC patients, and EC patients with TET2-5hmC- level had poor overall survival.

CONCLUSIONS

In summary, the association of TET2 and 5hmC was downregulated in EC tissues, and may be a potential poor prognostic indicator for EC patients. Combined detection of TET2 and 5hmC may be valuable for the diagnosis and prognosis of EC.

BNIP3 and DAPK1 methylation in peripheral blood leucocytes are noninvasive biomarkers for gastric cancer

OBJECTIVE

The objective of this study is to comprehensively investigate the potential value of BNIP3 and DAPK1 methylation in peripheral blood leukocytes as a non-invasive biomarker for the detection of gastric cancer (GC), prediction of chemotherapy efficacy, and prognosis assessment.

PATIENTS AND METHODS

Initially, multiple bioinformatic analyses were employed to explore the genetic landscape and biological effects of BNIP3 and DAPK1 in GC tissues. Subsequently, case-control and prospective follow-up studies were conducted to compare the differences in BNIP3 and DAPK1 methylation levels in peripheral blood leukocytes among GC patients and healthy controls, as well as between patients exhibiting sensitivity and resistance to platinum plus fluorouracil treatment, and between patients with varying survival outcomes of GC. Additionally, several predictive nomograms were constructed based on the identified CpG sites and relevant clinical parameters to forecast the occurrence of GC, chemotherapy efficacy, and prognosis.

RESULTS

The upregulation of BNIP3 and DAPK1 was found to be associated with the development and poorer survival outcomes of GC. Furthermore, the expression of BNIP3/DAPK1 exhibited an inverse relationship with their DNA methylation levels and demonstrated a positive correlation with immune cell infiltration, as well as the IC50 values of 5-Fluorouracil and Cisplatin in GC tissues. Increased infiltration of macrophages in the high-expression groups was observed to be linked to unfavorable GC survival. In the case-control and follow-up studies, lower methylation levels of BNIP3 and DAPK1 were identified in the peripheral leukocytes of GC patients compared to healthy controls. Hypomethylation was also associated with more aggressive subtypes, diminished chemotherapy efficacy, and poorer survival outcomes in GC.

CONCLUSION

The DNA methylation of BNIP3 and DAPK1 in peripheral blood leukocytes holds promise as a novel non-invasive biomarker for predicting the occurrence of GC, chemotherapy efficacy, and prognosis assessment.

Discovery of novel DNA methylation biomarker panels for the diagnosis and differentiation between common adenocarcinomas and their liver metastases

Differentiation between adenocarcinomas is sometimes challenging. The promising avenue for discovering new biomarkers lies in bioinformatics using DNA methylation analysis. Utilizing a 2853-sample identification dataset and a 782-sample independent verification dataset, we have identified diagnostic DNA methylation biomarkers that are hypermethylated in cancer and differentiate between breast invasive carcinoma, cholangiocarcinoma, colorectal cancer, hepatocellular carcinoma, lung adenocarcinoma, pancreatic adenocarcinoma and stomach adenocarcinoma. The best panels for cancer type exhibit sensitivity of 77.8-95.9%, a specificity of 92.7-97.5% for tumors, a specificity of 91.5-97.7% for tumors and normal tissues and a diagnostic accuracy of 85.3-96.4%. We have shown that the results can be extended from the primary cancers to their liver metastases, as the best panels diagnose and differentiate between pancreatic adenocarcinoma liver metastases and breast invasive carcinoma liver metastases with a sensitivity and specificity of 83.3-100% and a diagnostic accuracy of 86.8-91.9%. Moreover, the panels could detect hypermethylation of selected regions in the cell-free DNA of patients with liver metastases. At the same time, these were unmethylated in the cell-free DNA of healthy donors, confirming their applicability for liquid biopsies.

Focusing on the Abnormal Events of NPC1, NPC2, and NPC1L1 in Pan-Cancer and Further Constructing LUAD and KICH Prediction Models

Cancer's high incidence and death rate jeopardize human health and life, and it has become a global public health issue. Some members of NPCs have been studied in a few cancers, but comprehensive and prognostic analysis is lacking in most cancers. In this study, we used the Cancer Genome Atlas (TCGA) data genomics and transcriptome technology to examine the differential expression and prognosis of NPCs in 33 cancer samples, as well as to investigate NPCs mutations and their effect on patient prognosis and to evaluate the methylation level of NPCs in cancer. The linked mechanisms and medication resistance were subsequently investigated in order to investigate prospective tumor therapy approaches. The relationships between NPCs and immune infiltration, immune cells, immunological regulatory substances, and immune pathways were also investigated. Finally, the LUAD and KICH prognostic prediction models were built using univariate and multivariate COX regression analysis. Additionally, the mRNA and protein levels of NPCs were also identified.

Carcinogenic roles of MAFG-AS1 in human cancers

The MAF bZIP transcription factor G-antisense RNA 1 (MAFG-AS1) is located on chromosome 17. MAFG-AS1 was upregulated in 15 human cancers. MAFG-AS1 not only suppresses 16 miRNAs but also directly impacts 22 protein-coding genes' expression. Notably, abnormal MAFG-AS1 expression is connected to clinicopathological characteristics and a worse prognosis in a variety of cancers. Moreover, MAFG-AS1 takes its part in the tumorigenesis and progression of various human malignancies by suppressing apoptosis and promoting proliferation, migration, invasion, aerobic glycolysis, ferroptosis, angiogenesis, EMT, and metastasis. Besides, it can predict treatment effectiveness in ER + breast cancer, urothelial bladder carcinoma, and liver cancer by functioning as a trigger of resistance to tamoxifen, sorafenib, and cisplatin. This study systematically presents the functions of MAFG-AS1 in various cancers, as well as the findings of bioinformatics analyses of the MAFG-AS1, which should give clear advice for future research.

Importance of GWAS Risk Loci and Clinical Data in Predicting Asthma Using Machine-learning Approaches

INTRODUCTION

To understand the risk factors of asthma, we combined genome-wide association study (GWAS) risk loci and clinical data in predicting asthma using machine-learning approaches.

METHODS

A case-control study with 123 asthmatics and 100 controls was conducted in the Zhuang population in Guangxi. GWAS risk loci were detected using polymerase chain reaction, and clinical data were collected. Machine-learning approaches were used to identify the major factors that contribute to asthma.

RESULTS

A total of 14 GWAS risk loci with clinical data were analyzed on the basis of 10 times the 10-fold cross-validation for all machine-learning models. Using GWAS risk loci or clinical data, the best performances exhibited area under the curve (AUC) values of 64.3% and 71.4%, respectively. Combining GWAS risk loci and clinical data, the XGBoost established the best model with an AUC of 79.7%, indicating that the combination of genetics and clinical data can enable improved performance. We then sorted the importance of features and found the top six risk factors for predicting asthma to be rs3117098, rs7775228, family history, rs2305480, rs4833095, and body mass index.

CONCLUSION

Asthma-prediction models based on GWAS risk loci and clinical data can accurately predict asthma, and thus provide insights into the disease pathogenesis.

Pan-precancer and cancer DNA methylation profiles revealed significant tissue specificity of interrupted biological processes in tumorigenesis

DNA methylation (DNAme) alterations are known to initiate from the precancerous stage of tumorigenesis. Herein, we investigated the global and local patterns of DNAme perturbations in tumorigenesis by analysing the genome-wide DNAme profiles of the cervix, colorectum, stomach, prostate, and liver at precancerous and cancer stages. We observed global hypomethylation in tissues of both two stages, except for the cervix, whose global DNAme level in normal tissue was lower than that of the other four tumour types. For alterations shared by both stages, there were common hyper-methylation (sHyperMethyl) and hypo-methylation (sHypoMethyl) changes, of which the latter type was more frequently identified in all tissues. Biological pathways interrupted by sHyperMethyl and sHypoMethyl alterations demonstrated significant tissue specificity. DNAme bidirectional chaos indicated by the enrichment of both sHyperMethyl and sHypoMethyl changes in the same pathway was observed in most tissues and was a common phenomenon, particularly in liver lesions. Moreover, for the same enriched pathways, different tissues may be affected by distinct DNAme types. For the PI3K-Akt signalling pathway, sHyperMethyl enrichment was observed in the prostate dataset, but sHypoMethyl enrichment was observed in the colorectum and liver datasets. Nevertheless, they did not show an increased possibility in survival prediction of patients in comparison with other DNAme types. Additionally, our study demonstrated that gene-body DNAme changes of tumour suppressor genes and oncogenes may persist from precancerous lesions to the tumour. Overall, we demonstrate the tissue specificity and commonality of cross-stage alterations in DNA methylation profiles in multi-tissue tumorigenesis.

Systematic analysis reveals distinct roles of USF family proteins in various cancer types

BACKGROUND

Upstream stimulatory factors (USFs) are members of the basic helix-loop-helix leucine zipper transcription factor family, including USF1, USF2, and USF3. The first two members have been well studied compared to the third member, USF3, which has received scarce attention in cancer research to date. Despite a recently reported association of its alteration with thyroid carcinoma, its expression has not been previously analyzed.

METHODS

We comprehensively analyzed differential levels of USFs expression, genomic alteration, DNA methylation, and their prognostic value across different cancer types and the possible correlation with tumor-infiltrating immune cells and drug response by using different bioinformatics tools.

RESULTS

Our findings established that USFs play an important role in cancers related to the urinary system and justify the necessity for further investigation. We implemented and offer a useful ShinyApp to facilitate researchers' efforts to inquire about any other gene of interest and to perform the analysis of drug response in a user-friendly fashion at http://zzdlab.com:3838/Drugdiscovery/.

Integrative Analysis of DNA Methylation and Gene Expression Data Identifies Potential Biomarkers and Functional Epigenetic Modules for SARS-CoV-2

To integrate gene expression and DNA methylation data and find the potential role of DNA methylation in the invasion and replication of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). We first conducted differential expression and methylation analysis between the coronavirus disease of 2019 (COVID-19) and healthy controls. FEM was employed to identify functional epigenetic modules, from which a diagnostic model for COVID-19 was built. SKA1 and WSB1 modules were identified, with SKA1 module enriched in COVID-19 replication and transcription, and WSB1 module related to ubiquitin-protein activity. The differentially expressed or differentially methylated genes in these two modules could be used to distinguish COVID-19 from healthy controls, with AUC reaching 1 and 0.98 for SKA1 and WSB1 modules, respectively. Two epigenetically activated genes (CENPM and KNL1) from the SKA1 module were upregulated in HPV- or HBV-positive tumor samples and were found to be significantly associated with the survival of tumor patients. In conclusion, the identified FEM modules and potential signatures play an essential role in the replication and transcription of coronavirus.

DNA Methylation Signature of Synchronous Endometrioid Endometrial and Ovarian Carcinomas

Next-generation sequencing (NGS) studies have demonstrated that co-occurring sporadic endometrioid endometrial carcinoma (EEC) and endometrioid ovarian carcinoma (EOC) are clonally related, suggesting that they originate from a single primary tumor. Despite clonality, synchronous EEC and EOC when diagnosed at early stage behave indolently, similar to isolated primary EEC or isolated primary EOC. In the present study, we compared the DNA methylation signatures of co-occurring EEC and EOC with those of isolated primary EEC and isolated primary EOC. We also performed targeted NGS to assess the clonal relatedness of 7 co-occurring EEC and EOC (4 synchronous EEC and EOC and 3 metastatic EEC based on pathologic criteria). NGS confirmed a clonal relationship in all co-occurring EEC and EOC. DNA methylation profiling showed distinct epigenetic signatures of isolated primary EEC and isolated primary EOC. Endometrial tumors from co-occurring EEC and EOC clustered with isolated primary EEC while their ovarian counterparts clustered with isolated primary EOC. Three co-occurring EEC and EOC cases with peritoneal lesions showed a closer epigenetic signature and copy number variation profile between the peritoneal lesion and EOC than EEC. In conclusion, synchronous sporadic EEC and EOC are clonally related but demonstrate a shift in DNA methylation signatures between ovarian and endometrial tumors as well as epigenetic overlap between ovarian and peritoneal tumors. Our results suggest that tumor microenvironment in the ovary may play a role in epigenetic modulation of metastatic EEC.

The role of dietary polyphenols in alternating DNA methylation in cancer

Natural products such as curcumin, quercetin, and resveratrol have been shown to have antitumor effectsand several studies have examined their role in treating cancer, either alone or in combination with other chemotherapeutic drugs. These compounds are capable of affecting different cancer-related mechanisms, such as proliferation, inflammation, invasion, and metastasis. Along with all of the benefits of these agents, affecting epigenetic processes is one of the most important aspects of their impact. Epigenetic modifications can be categorized into three main processes that include DNA methylation, histone modification, and regulation of small non-coding RNAs. Therefore, targeting DNA methylation can be used as a cancer treatment strategy by identifying or developing methylation modulators. Herein, we take a look into the studies investigating the role of natural products (e.g. curcumin, resveratrol, epigallocatechin gallate (EGCG), and quercetin) in alternating the DNA methylation status of various cancer cells. We discuss how these compounds reduce the expression of enzymes mediating the methylation of tumor suppressor genes and thereby, increasing the expression of tumor suppressors while reactivating antitumor signaling pathways.

Non-SMC condensin I complex subunit D2 (NCAPD2) reveals its prognostic and immunologic features in human cancers

Non-SMC condensin I complex subunit D2 (NCAPD2) is overexpressed in some malignant tumors. However, there are few studies on the function of NCAPD2 in pan-cancer. We used the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Human Protein Atlas (HPA), and UALCAN to analyze NCAPD2 expression and promoter methylation levels in 33 tumors and normal samples. We performed immunohistochemistry (IHC) on liver cancer and corresponding normal tissues to examine NCAPD2 protein expression in LIHC. Kaplan-Meier survival and univariate regression analyses were performed to explore the pan-cancer clinical significance of NCAPD2. Moreover, correlative analysis between NCAPD2 expression and clinical characteristics, immune cell infiltration, immune checkpoints, immune regulators, tumor mutation burden (TMB), microsatellite instability (MSI), ribonucleic acid (RNA) methylation regulators, and drug sensitivity was conducted using data from TCGA. We also investigated the effects of NCAPD2 expression on immunotherapy efficacy and prognosis. Gene set enrichment analysis (GSEA) was conducted using NCAPD2. Bioinformatic analysis showed that NCAPD2 was overexpressed in most tumors and correlated with the clinical characteristics of some cancers. IHC results demonstrated that NCAPD2 protein expression was higher in LIHC than in normal liver. NCAPD2 expression was linked with T stage, clinical stage, and histologic grade in LIHC. Overexpression of NCAPD2 resulted in poor overall survival, and disease-specific survival in adrenocortical carcinoma, kidney renal papillary cell carcinoma, brain lower grade glioma, liver hepatocellular carcinoma, lung adenocarcinoma, mesothelioma, pancreatic adenocarcinoma, sarcoma, skin cutaneous melanoma, and uterine corpus endometrial carcinoma. NCAPD2 was considered an independent biomarker by Cox regression in LIHC. The time ROC curve demonstrated that the survival rate of 1-, 3-, and 5-year OS and DSS in LIHC was above 0.6. The expression of NCAPD2 was significantly correlated with immune cell infiltration, immune checkpoints, TMB, MSI, and RNA methylation regulators in several tumors. NCAPD2 had a high predictive value for immunotherapy efficiency in certain tumors. In our study, drugs sensitive to NCAPD2 protein were screened by sensitivity analysis. GSEA analysis showed that NCAPD2 mainly participated in the G2M checkpoint, mitotic spindle, and KRAS-signaling. NCAPD2 may act as a prognostic molecular marker in most cancers.

ZNF480 influences the prognosis, pathogenesis, and immune microenvironment in patients with lower-grade glioma

ZNF480 has not yet attracted attention in the study of malignant tumors. Therefore, this study attempts to explain the significance of ZNF480 in the pathological process of lower-grade gliomas (LGG) based on large-scale samples from public database sources and in vitro experiments. Reverse transcription quantitative real-time polymerase chain reaction and immunohistochemistry confirmed that ZNF480 was highly expressed at both the mRNA and protein levels in LGG. Prognostic correlation analysis confirmed that the high expression of ZNF480, as an independent pathogenic gene, significantly correlates with poor survival in patients. Furthermore, the expression level of ZNF480 was significantly inhibited in SHG-44 cells treated with ademetionine disulfate tosylate. Gene set enrichment analysis showed that ZNF480 exists in multiple tumor-related signaling pathways, including the Notch signaling pathway. Immunological correlation analysis showed that ZNF480 can promote the LGG microenvironment to a high immune state and significantly enhance the infiltration of various immune cells, such as M2 macrophages. Finally, Spearman analysis showed a positive correlation of ZNF480 with many immune checkpoints, such as PD-L1. Overall, this study reveals for the first time the adverse effects of ZNF480 on the prognosis of tumor patients, which expands our understanding of the molecular mechanisms behind the regulation of ZNF480. We believe that the high expression of ZNF480 in LGG may be valuable for molecular targeted therapy or combined immunotherapy.

Integrative analysis of DNA methylomes reveals novel cell-free biomarkers in lung adenocarcinoma

Lung cancer is a leading cause of cancer-related deaths worldwide, with a low 5-year survival rate due in part to a lack of clinically useful biomarkers. Recent studies have identified DNA methylation changes as potential cancer biomarkers. The present study identified cancer-specific CpG methylation changes by comparing genome-wide methylation data of cfDNA from lung adenocarcinomas (LUAD) patients and healthy donors in the discovery cohort. A total of 725 cell-free CpGs associated with LUAD risk were identified. Then XGBoost algorithm was performed to identify seven CpGs associated with LUAD risk. In the training phase, the 7-CpGs methylation panel was established to classify two different prognostic subgroups and showed a significant association with overall survival (OS) in LUAD patients. We found that the methylation of cg02261780 was negatively correlated with the expression of its representing gene GNA11. The methylation and expression of GNA11 were significantly associated with LAUD prognosis. Based on bisulfite PCR, the methylation levels of five CpGs (cg02261780, cg09595050, cg20193802, cg15309457, and cg05726109) were further validated in tumor tissues and matched non-malignant tissues from 20 LUAD patients. Finally, validation of the seven CpGs with RRBS data of cfDNA methylation was conducted and further proved the reliability of the 7-CpGs methylation panel. In conclusion, our study identified seven novel methylation markers from cfDNA methylation data which may contribute to better prognosis for LUAD patients.

Exploring the relationship between abnormally high expression of NUP205 and the clinicopathological characteristics, immune microenvironment, and prognostic value of lower-grade glioma

Nuclear pore complex (NPC) is a major transport pivot for nucleocytoplasmic molecule exchange. Nucleoporin 205 (NUP205)-a main component of NPC-plays a key regulatory role in tumor cell proliferation; however, few reports document its effect on the pathological progression of lower-grade glioma (LGG). Therefore, we conducted an integrated analysis using 906 samples from multiple public databases to explore the effects of NUP205 on the prognosis, clinicopathological characteristics, regulatory mechanism, and tumor immune microenvironment (TIME) formation in LGG. First, multiple methods consistently showed that the mRNA and protein expression levels of NUP205 were higher in LGG tumor tissue than in normal brain tissue. This increased expression was mainly noted in the higher WHO Grade, IDH-wild type, and 1p19q non-codeleted type. Second, various survival analysis methods showed that the highly expressed NUP205 was an independent risk indicator that led to reduced survival time of patients with LGG. Third, GSEA analysis showed that NUP205 regulated the pathological progress of LGG via the cell cycle, notch signaling pathway, and aminoacyl-tRNA biosynthesis. Ultimately, immune correlation analysis suggested that high NUP205 expression was positively correlated with the infiltration of multiple immune cells, particularly M2 macrophages, and was positively correlated with eight immune checkpoints, particularly PD-L1. Collectively, this study documented the pathogenicity of NUP205 in LGG for the first time, expanding our understanding of its molecular function. Furthermore, this study highlighted the potential value of NUP205 as a target of anti-LGG immunotherapy.

A DNA methylation signature for the prediction of tumour recurrence in stage II colorectal cancer

BACKGROUND

A major challenge in stage II colorectal carcinoma is to identify patients with increased risk of recurrence. Biomarkers that distinguish patients with poor prognosis from patients without recurrence are currently lacking. This study aims to develop a robust DNA methylation classifier that allows the prediction of recurrence and chemotherapy benefit in patients with stage II colorectal cancer. We performed a genome-wide DNA methylation capture sequencing in 243 stage II colorectal carcinoma samples and identified a relapse-specific DNA methylation signature consisting of eight CpG sites.

METHODS

Two hundred and forty-three patients with stage II CRC were enrolled in this study. In order to select differential methylation sites among recurrence and non-recurrence stage II CRC samples, DNA methylation profiles of 62 tumour samples including 31 recurrence and 31 nonrecurrence samples were analysed using the Agilent SureSelectXT Human Methyl-Seq, a comprehensive target enrichment system to analyse CpG methylation. Pyrosequencing was applied to quantify the methylation level of candidate DNA methylation sites in 243 patients. Least absolute shrinkage and selection operator (LASSO) method was employed to build the disease recurrence prediction classifier.

RESULTS

We identified a relapse-related DNA methylation signature consisting of eight CpG sites in stage II CRC by DNA methylation capture sequencing. The classifier showed significantly higher prognostic accuracy than any clinicopathological risk factors. The Kaplan-Meier survival curve showed an association of high-risk score with poor prognosis. In multivariate analysis, the signature was the most significant prognosis factor, with an HR of 2.80 (95% CI, 1.71-4.58, P < 0.001). The signature could identify patients who are suitable candidates for adjuvant chemotherapy.

CONCLUSIONS

An eight-CpG DNA methylation signature is a reliable prognostic and predictive tool for disease recurrence in patients with stage II CRC.

Prognosis Prediction of Lung Adenocarcinoma Patients Based on Molecular Subgroups of DNA Methylation

Lung adenocarcinoma (LUAD) is a malignant tumor with high mortality. At present, the clinicopathologic feature is the main breakthrough to assess the prognosis of LUAD patients. However, in most cases, the results are less than satisfactory. Cox regression analysis was conducted in this study to obtain methylation sites with significant prognostic relevance based on mRNA expression, DNA methylation data, and clinical data of LUAD from The Cancer Genome Atlas Program database. LUAD patients were grouped into 4 subtypes according to different methylation levels using K-means consensus cluster analysis. By survival analysis, patients were grouped into high-methylation and low-methylation groups. Later, 895 differentially expressed genes (DEGs) were obtained. Eight optimal methylation signature genes associated with prognosis were screened by Cox regression analysis, and a risk assessment model was constructed based on these genes. Samples were then classified into high-risk and low-risk groups depending on the risk assessment model, and prognostic, predictive ability was assessed using survival and receiver operating characteristic (ROC) curves. The results showed that this risk model had a great efficacy in predicting the prognosis of patients, and it was, therefore, able to be an independent prognostic factor. At last, the enrichment analysis demonstrated that the signaling pathways, including cell cycle, homologous recombination, P53 signaling pathway, DNA replication, pentose phosphate pathway, and glycolysis gluconeogenesis were remarkably activated in the high-risk group. In general, we construct an 8-gene model based on DNA methylation molecular subtypes by a series of bioinformatics methods, which can provide new insights for predicting the prognosis of patients with LUAD.

Shifting the Cancer Screening Paradigm: The Rising Potential of Blood-Based Multi-Cancer Early Detection Tests

Cancer remains a leading cause of death worldwide, partly owing to late detection which entails limited and often ineffective therapeutic options. Most cancers lack validated screening procedures, and the ones available disclose several drawbacks, leading to low patient compliance and unnecessary workups, adding up the costs to healthcare systems. Hence, there is a great need for innovative, accurate, and minimally invasive tools for early cancer detection. In recent years, multi-cancer early detection (MCED) tests emerged as a promising screening tool, combining molecular analysis of tumor-related markers present in body fluids with artificial intelligence to simultaneously detect a variety of cancers and further discriminate the underlying cancer type. Herein, we aim to provide a highlight of the variety of strategies currently under development concerning MCED, as well as the major factors which are preventing clinical implementation. Although MCED tests depict great potential for clinical application, large-scale clinical validation studies are still lacking.

Aberrant promoter methylation of Wnt inhibitory factor-1 gene is a potential target for treating psoriasis

Psoriasis is a chronic inflammatory skin disease mediated by immune and complex genetic factors. The wingless-related integration site (Wnt) signaling pathway plays a critical role in psoriasis, but how the Wnt pathway is regulated in psoriatic skin and whether it can be exploited for therapeutic benefits is unclear. By comparing biopsies from healthy and psoriatic skin, we found that Wnt inhibitory factor 1 (WIF1), an inhibitor of Wnt signaling, showed reduced expression at both mRNA and protein levels in psoriatic skin. We then quantified methylation of the WIF1 gene promoter by DNA methylation sequencing and found that the WIF1 promoter region was hypermethylated. We further showed that recombinant WIF1 injection ameliorates the imiquimod (IMQ) mouse model of psoriasis. We also revealed that treatment with the DNA methylation inhibitor, decitabine, inhibited proliferation of immortalized human keratinocytes (HaCaT) in a psoriasis-like inflammatory environment. Finally, we applied decitabine to the IMQ mouse model and demonstrated that treatment of mice with decitabine ameliorates the disease. Therefore, our study reveals that methylation of the WIF1 gene is associated with the pathogenesis of psoriasis, and suggests that pharmacological targeting of DNA methylation is a potential treatment strategy for psoriasis.

Impact of BSG/CD147 gene expression on diagnostic, prognostic and therapeutic strategies towards malignant cancers and possible susceptibility to SARS-CoV-2

BACKGROUND

BSG (CD147) is a member of the immunoglobulin superfamily that shows roles for potential prognostics and therapeutics for metastatic cancers and SARS-CoV-2 invasion for COVID-19. The susceptibility of malignant cancers to SARS-CoV-2 as well as the correlations between disease outcome and BSG expression in tumor tissues have not been studied in depth.

METHODS

In this study, we explored the BSG expression profile, survival correlation, DNA methylation, mutation, diagnostics, prognostics, and tumor-infiltrating lymphocytes (TILs) from different types of cancer tissues with corresponding healthy tissues. In vitro studies for cordycepin (CD), N6-(2-hydroxyethyl) adenosine (HEA), N6, N6-dimethyladenosine (m⁶₂A) and 5'-uridylic acid (UMP) on BSG expression were also conducted.

RESULTS

We revealed that BSG is conserved among different species, and significantly upregulated in seven tumor types, including ACC, ESCA, KICH, LIHC, PAAD, SKCM and THYM, compared with matched normal tissues, highlighting the susceptibility of these cancer patients to SARS-CoV-2 invasion, COVID-19 severity and progression of malignant cancers. High expression in BSG was significantly correlated with a short OS in LGG, LIHC and OV patients, but a long OS in KIRP patients. Methylation statuses in the BSG promoter were significantly higher in BRCA, HNSC, KIRC, KIRP, LUSC, PAAD, and PRAD tumor tissues, but lower in READ. Four CpGs in the BSG genome were identified as potential DNA methylation biomarkers which could be used to predict malignant cancers from normal individuals. Furthermore, a total of 65 mutation types were found, in which SARC showed the highest mutation frequency (7.84%) and THYM the lowest (0.2%). Surprisingly, both for disease-free and progression-free survival in pan-cancers were significantly reduced after BSG mutations. Additionally, a correlation between BSG expression and immune lymphocytes of CD56bright natural killer cell, CD56dim natural killer cell and monocytes, MHC molecules of HLA-A, HLA-B, HLA-C and TAPBP, immunoinhibitor of PVR, PVRL2, and immunostimulators of TNFRSF14, TNFRSF18, TNFRSF25, and TNFSF9, was revealed in most cancer types. Moreover, BSG expression was downregulated by CD, HEA, m⁶₂A or UMP in cancer cell lines, suggesting therapeutic potentials for interfering entry of SARS-CoV-2.

CONCLUSIONS

Altogether, our study highlights the values of targeting BSG for diagnostic, prognostic and therapeutic strategies to fight malignant cancers and COVID-19. Small molecules CD, HEA, m⁶₂A and UMP imply therapeutic potentials in interfering with entry of SARS-CoV-2 and progression of malignant cancers.

DNA Methylation and Gene Expression of the Cysteinyl Leukotriene Receptors as a Prognostic and Metastatic Factor for Colorectal Cancer Patients

Colorectal cancer (CRC), one of the leading causes of cancer-related deaths in the western world, is the third most common cancer for both men and women. As a heterogeneous disease, colon cancer (CC) is caused by both genetic and epigenetic changes. The prognosis for CRC is affected by a variety of features, including late diagnosis, lymph node and distant metastasis. The cysteinyl leukotrienes (CysLT), as leukotriene D₄ and C₄ (LTD₄ and LTC₄), are synthesized from arachidonic acid via the 5-lipoxygenase pathway, and play an important role in several types of diseases such as inflammation and cancer. Their effects are mediated via the two main G-protein-coupled receptors, CysLT₁R and CysLT₂R. Multiple studies from our group observed a significant increase in CysLT₁R expression in the poor prognosis group, whereas CysLT₂R expression was higher in the good prognosis group of CRC patients. Here, we systematically explored and established the role of the CysLTRs, cysteinyl leukotriene receptor 1(CYSLTR1) and cysteinyl leukotriene receptor 2 (CYSLTR2) gene expression and methylation in the progression and metastasis of CRC using three unique in silico cohorts and one clinical CRC cohort. Primary tumor tissues showed significant CYSLTR1 upregulation compared with matched normal tissues, whereas it was the opposite for the CYSLTR2. Univariate Cox proportional-hazards (CoxPH) analysis yielded a high expression of CYSLTR1 and accurately predicted high-risk patients in terms of overall survival (OS; hazard ratio (HR) = 1.87, p = 0.03) and disease-free survival [DFS] Hazard ratio [HR] = 1.54, p = 0.05). Hypomethylation of the CYSLTR1 gene and hypermethylation of the CYSLTR2 gene were found in CRC patients. The M values of the CpG probes for CYSLTR1 are significantly lower in primary tumor and metastasis samples than in matched normal samples, but those for CYSLTR2 are significantly higher. The differentially upregulated genes between tumor and metastatic samples were uniformly expressed in the high-CYSLTR1 group. Two epithelial-mesenchymal transition (EMT) markers, E-cadherin (CDH1) and vimentin (VIM) were significantly downregulated and upregulated in the high-CYSLTR1 group, respectively, but the result was opposite to that of CYSLTR2 expression in CRC. CDH1 expression was high in patients with less methylated CYSLTR1 but low in those with more methylated CYSLTR2. The EMT-associated observations were also validated in CC SW620 cell-derived colonospheres, which showed decreased E-cadherin expression in the LTD₄ stimulated cells, but not in the CysLT₁R knockdown SW620 cells. The methylation profiles of the CpG probes for CysLTRs significantly predicted lymph node (area under the curve [AUC] = 0.76, p < 0.0001) and distant (AUC = 0.83, p < 0.0001) metastasis. Intriguingly, the CpG probes cg26848126 (HR = 1.51, p = 0.03) for CYSLTR1, and cg16299590 (HR = 2.14, p = 0.03) for CYSLTR2 significantly predicted poor prognosis in terms of OS, whereas the CpG probe cg16886259 for CYSLTR2 significantly predicts a poor prognosis group in terms of DFS (HR = 2.88, p = 0.03). The CYSLTR1 and CYSLTR2 gene expression and methylation results were successfully validated in a CC patient cohort. In this study, we have demonstrated that CysLTRs' methylation and gene expression profile are associated with the progression, prognosis, and metastasis of CRC, which might be used for the assessment of high-risk CRC patients after validating the result in a larger CRC cohort.

Machine Learning Quantified Tumor-Stroma Ratio Is an Independent Prognosticator in Muscle-Invasive Bladder Cancer

Although the tumor-stroma ratio (TSR) has prognostic value in many cancers, the traditional semi-quantitative visual assessment method has inter-observer variability, making it impossible for clinical practice. We aimed to develop a machine learning (ML) algorithm for accurately quantifying TSR in hematoxylin-and-eosin (H&E)-stained whole slide images (WSI) and further investigate its prognostic effect in patients with muscle-invasive bladder cancer (MIBC). We used an optimal cell classifier previously built based on QuPath open-source software and ML algorithm for quantitative calculation of TSR. We retrospectively analyzed data from two independent cohorts to verify the prognostic significance of ML-based TSR in MIBC patients. WSIs from 133 MIBC patients were used as the discovery set to identify the optimal association of TSR with patient survival outcomes. Furthermore, we performed validation in an independent external cohort consisting of 261 MIBC patients. We demonstrated a significant prognostic association of ML-based TSR with survival outcomes in MIBC patients (p < 0.001 for all comparisons), with higher TSR associated with better prognosis. Uni- and multivariate Cox regression analyses showed that TSR was independently associated with overall survival (p < 0.001 for all analyses) after adjusting for clinicopathological factors including age, gender, and pathologic stage. TSR was found to be a strong prognostic factor that was not redundant with the existing staging system in different subgroup analyses (p < 0.05 for all analyses). Finally, the expression of six genes (DACH1, DEEND2A, NOTCH4, DTWD1, TAF6L, and MARCHF5) were significantly associated with TSR, revealing possible potential biological relevance. In conclusion, we developed an ML algorithm based on WSIs of MIBC patients to accurately quantify TSR and demonstrated its prognostic validity for MIBC patients in two independent cohorts. This objective quantitative method allows application in clinical practice while reducing the workload of pathologists. Thus, it might be of significant aid in promoting precise pathology services in MIBC.

Role of Artificial Intelligence and Machine Learning in Prediction, Diagnosis, and Prognosis of Cancer

Cancer is one of the most devastating, fatal, dangerous, and unpredictable ailments. To reduce the risk of fatality in this disease, we need some ways to predict the disease, diagnose it faster and precisely, and predict the prognosis accurately. The incorporation of artificial intelligence (AI), machine learning (ML), and deep learning (DL) algorithms into the healthcare system has already proven to work wonders for patients. Artificial intelligence is a simulation of intelligence that uses data, rules, and information programmed in it to make predictions. The science of machine learning (ML) uses data to enhance performance in a variety of activities and tasks. A bigger family of machine learning techniques built on artificial neural networks and representation learning is deep learning (DL). To clarify, we require AI, ML, and DL to predict cancer risk, survival chances, cancer recurrence, cancer diagnosis, and cancer prognosis. All of these are required to improve patient's quality of life, increase their survival rates, decrease anxiety and fear to some extent, and make a proper personalized treatment plan for the suffering patient. The survival rates of people with diffuse large B-cell lymphoma (DLBCL) can be forecasted. Both solid and non-solid tumors can be diagnosed precisely with the help of AI and ML algorithms. The prognosis of the disease can also be forecasted with AI and its approaches like deep learning. This improvement in cancer care is a turning point in advanced healthcare and will deeply impact patient's life for good.

DNA methylation loci identification for pan-cancer early-stage diagnosis and prognosis using a new distributed parallel partial least squares method

Aberrant methylation is one of the early detectable events in many tumors, which is very promising for pan-cancer early-stage diagnosis and prognosis. To efficiently analyze the big pan-cancer methylation data and to overcome the co-methylation phenomenon, a MapReduce-based distributed and parallel-designed partial least squares approach was proposed. The large-scale high-dimensional methylation data were first decomposed into distributed blocks according to their genome locations. A distributed and parallel data processing strategy was proposed based on the framework of MapReduce, and then latent variables were further extracted for each distributed block. A set of pan-cancer signatures through a differential co-expression network followed by statistical tests was further identified based on their gene expression profiles. In total, 15 TCGA and 3 GEO datasets were used as the training and testing data, respectively, to verify our method. As a result, 22,000 potential methylation loci were selected as highly related loci with early-stage pan-cancer diagnosis. Of these, 67 methylation loci were further identified as pan-cancer signatures considering their gene expression as well. The survival analysis as well as pathway enrichment analysis on them shows that not only these loci may serve as potential drug targets, but also the proposed method may serve as a uniform framework for signature identification with big data.

Disease classification for whole-blood DNA methylation: Meta-analysis, missing values imputation, and XAI

BACKGROUND

DNA methylation has a significant effect on gene expression and can be associated with various diseases. Meta-analysis of available DNA methylation datasets requires development of a specific workflow for joint data processing.

RESULTS

We propose a comprehensive approach of combined DNA methylation datasets to classify controls and patients. The solution includes data harmonization, construction of machine learning classification models, dimensionality reduction of models, imputation of missing values, and explanation of model predictions by explainable artificial intelligence (XAI) algorithms. We show that harmonization can improve classification accuracy by up to 20% when preprocessing methods of the training and test datasets are different. The best accuracy results were obtained with tree ensembles, reaching above 95% for Parkinson's disease. Dimensionality reduction can substantially decrease the number of features, without detriment to the classification accuracy. The best imputation methods achieve almost the same classification accuracy for data with missing values as for the original data. XAI approaches have allowed us to explain model predictions from both populational and individual perspectives.

CONCLUSIONS

We propose a methodologically valid and comprehensive approach to the classification of healthy individuals and patients with various diseases based on whole-blood DNA methylation data using Parkinson's disease and schizophrenia as examples. The proposed algorithm works better for the former pathology, characterized by a complex set of symptoms. It allows to solve data harmonization problems for meta-analysis of many different datasets, impute missing values, and build classification models of small dimensionality.

Identification of molecular subtypes of coronary artery disease based on ferroptosis- and necroptosis-related genes

Aim: Coronary artery disease (CAD) is a heterogeneous disorder with high morbidity, mortality, and healthcare costs, representing a major burden on public health. Here, we aimed to improve our understanding of the genetic drivers of ferroptosis and necroptosis and the clustering of gene expression in CAD in order to develop novel personalized therapies to slow disease progression.

Methods: CAD datasets were obtained from the Gene Expression Omnibus. The identification of ferroptosis- and necroptosis-related differentially expressed genes (DEGs) and the consensus clustering method including the classification algorithm used km and distance used spearman were performed to differentiate individuals with CAD into two clusters (cluster A and cluster B) based expression matrix of DEGs. Next, we identified four subgroup-specific genes of significant difference between cluster A and B and again divided individuals with CAD into gene cluster A and gene cluster B with same methods. Additionally, we compared differences in clinical information between the subtypes separately. Finally, principal component analysis algorithms were constructed to calculate the cluster-specific gene score for each sample for quantification of the two clusters.

Results: In total, 25 ferroptosis- and necroptosis-related DEGs were screened. The genes in cluster A were mostly related to the neutrophil pathway, whereas those in cluster B were mostly related to the B-cell receptor signaling pathway. Moreover, the subgroup-specific gene scores and CAD indices were higher in cluster A and gene cluster A than in cluster B and gene cluster B. We also identified and validated two genes showing upregulation between clusters A and B in a validation dataset.

Conclusion: High expression of CBS and TLR4 was related to more severe disease in patients with CAD, whereas LONP1 and HSPB1 expression was associated with delayed CAD progression. The identification of genetic subgroups of patients with CAD may improve clinician knowledge of disease pathogenesis and facilitate the development of methods for disease diagnosis, classification, and prognosis.

DNA Methylation-Specific Analysis of G Protein-Coupled Receptor-Related Genes in Pan-Cancer

Tumor heterogeneity presents challenges for personalized diagnosis and treatment of cancer. The identification method of cancer-specific biomarkers has important applications for the diagnosis and treatment of cancer types. In this study, we analyzed the pan-cancer DNA methylation data from TCGA and GEO, and proposed a computational method to quantify the degree of specificity based on the level of DNA methylation of G protein-coupled receptor-related genes (GPCRs-related genes) and to identify specific GPCRs DNA methylation biomarkers (GRSDMs) in pan-cancer. Then, a ridge regression-based method was used to discover potential drugs through predicting the drug sensitivities of cancer samples. Finally, we predicted and verified 8 GRSDMs in adrenocortical carcinoma (ACC), rectum adenocarcinoma (READ), uveal Melanoma (UVM), thyroid carcinoma (THCA), and predicted 4 GRSDMs (F2RL3, DGKB, GRK5, PIK3R6) which were sensitive to 12 potential drugs. Our research provided a novel approach for the personalized diagnosis of cancer and informed individualized treatment decisions.

Evaluating DNA recovery efficiency following bisulphite modification from plasma samples submitted for cell-free DNA methylation analysis

The detection of methylated templates in cell-free DNA (cfDNA) is increasingly recognized as a valuable, non-invasive tool for diagnosis, monitoring and prognostication in a range of medical contexts. The importance of controlling pre-analytical conditions in laboratory workflows prior to cfDNA quantification is well-established. Significant variations in the recovery of DNA following processes such as cfDNA extraction and sodium bisulphite modification may confound downstream analysis, particularly when accurate quantification of templates is required. Given the wealth of potential applications for this emerging molecular technology, attention has turned to the requirement to recognize and minimize pre-analytical variables prior to cfDNA methylation analysis. We recently described the development of an approach using an exogenous DNA construct to evaluate the recovery efficiency of cfDNA following the extraction and bisulphite modification steps (CEREBIS). Here, we report our experience in the practical application of this technique in 107 consecutive patient plasma samples submitted for quantitative cfDNA methylation analysis. The mean recovery of cfDNA (as estimated using cerebis), following extraction and bisulphite modification, was 37% ± 7%. Nine (8.4%) of the 107 samples were found to be outside of control limits, where the recovery of cerebis indicated significant differences in the efficiency of the pre-analytical processing of these samples. Recognition of these out-of-control samples precluded subsequent molecular analysis. Implementation of data-driven quality control measures, such as the one described, has the potential to improve the quality of liquid biopsy methylation analysis, interpretation and reporting.

Diagnostic classification of cancers using DNA methylation of paracancerous tissues

The potential role of DNA methylation from paracancerous tissues in cancer diagnosis has not been explored until now. In this study, we built classification models using well-known machine learning models based on DNA methylation profiles of paracancerous tissues. We evaluated our methods on nine cancer datasets collected from The Cancer Genome Atlas (TCGA) and utilized fivefold cross-validation to assess the performance of models. Additionally, we performed gene ontology (GO) enrichment analysis on the basis of the significant CpG sites selected by feature importance scores of XGBoost model, aiming to identify biological pathways involved in cancer progression. We also exploited the XGBoost algorithm to classify cancer types using DNA methylation profiles of paracancerous tissues in external validation datasets. Comparative experiments suggested that XGBoost achieved better predictive performance than the other four machine learning methods in predicting cancer stage. GO enrichment analysis revealed key pathways involved, highlighting the importance of paracancerous tissues in cancer progression. Furthermore, XGBoost model can accurately classify nine different cancers from TCGA, and the feature sets selected by XGBoost can also effectively predict seven cancer types on independent GEO datasets. This study provided new insights into cancer diagnosis from an epigenetic perspective and may facilitate the development of personalized diagnosis and treatment strategies.

Prognostic significance of CHAC1 expression in breast cancer

BACKGROUND

An emerging component of Unfolded Protein Response (UPR) pathway, cation transport regulator homolog 1 (CHAC1) has been conferred with the ability to degrade intracellular glutathione and induce apoptosis, however, many reports have suggested a role of CHAC1 in cancer progression. Our study aimed to investigate CHAC1 mRNA levels in large breast cancer datasets using online tools and both mRNA and protein levels in different breast cancer cell lines.

METHODS AND RESULTS

Analysis of clinical information from various online tools (UALCAN, GEPIA2, TIMER2, GENT2, UCSCXena, bcGenExMiner 4.8, Km Plotter, and Enrichr) was done to elucidate the CHAC1 mRNA expression in large breast cancer patient dataset and its correlation with disease progression. Later, in vitro techniques were employed to explore the mRNA and protein expression of CHAC1 in breast cancer cell lines. Evidence from bioinformatics analysis as well as in vitro studies indicated a high overall expression of CHAC1 in breast tumor samples and had a significant impact on the prognosis and survival of patients. Enhanced CHAC1 levels in the aggressive breast tumor subtypes such as Human Epidermal growth factor receptor 2 (HER2) and Triple Negative Breast Cancer (TNBC) were evident. Our findings hint toward the possible role of CHAC1 in facilitating the aggressiveness of breast cancer and the disease outcome.

CONCLUSION

In summary, CHAC1 is constantly up-regulated in breast cancer leading to a poor prognosis. CHAC1, therefore, could be a promising candidate in the analysis of breast cancer diagnosis and prognosis.

A systematic pan-cancer analysis identifies RIOK3 as an immunological and prognostic biomarker

OBJECTIVES

Despite recent research highlighting the critical function of RIO kinase 3 (RIOK3) in a variety of malignancies, a comprehensive evaluation of RIOK3 in human tumors is absent. Our study helps to clarify the molecular mechanism of RIOK3 in carcinogenesis from multiple perspectives.

METHODS

Our research looked into the potential oncogenic role of RIOK3 in 33 cancers using TCGA (The Cancer Genome Atlas), GTEx (Genotype-Tissue Expression Project), GEO (Gene Expression Omnibus) datasets, and several bioinformatics tools.

RESULTS

RIOK3 expression in tumors is disordered compared to normal tissue, and it is highly linked with the level of MMR (Mismatch repair) gene mutations and DNA methyltransferase expression. According to univariate survival analysis, it could be used as an independent prognostic factor. Further investigation demonstrated that RIOK3 expression was correlated with cancer-associated fibroblast, neutrophil, and endothelial infiltration levels in kidney cancer and was positively correlated with the expression of immune checkpoint markers in different cancers. The functional pathways of RIOK3 also included cell-cell adhesion, protein phosphorylation, and innate immune-related functions.

CONCLUSIONS

These findings suggest that RIOK3 could be used as an immunological and prognostic biomarker in various malignant tumors.

Survival-related genes are diversified across cancers but generally enriched in cancer hallmark pathways

Background

Pan-cancer studies have disclosed many commonalities and differences in mutations, copy number variations, and gene expression alterations among cancers. Some of these features are significantly associated with clinical outcomes, and many prognosis-predictive biomarkers or biosignatures have been proposed for specific cancer types. Here, we systematically explored the biological functions and the distribution of survival-related genes (SRGs) across cancers.

Results

We carried out two different statistical survival models on the mRNA expression profiles in 33 cancer types from TCGA. We identified SRGs in each cancer type based on the Cox proportional hazards model and the log-rank test. We found a large difference in the number of SRGs among different cancer types, and most of the identified SRGs were specific to a particular cancer type. While these SRGs were unique to each cancer type, they were found mostly enriched in cancer hallmark pathways, e.g., cell proliferation, cell differentiation, DNA metabolism, and RNA metabolism. We also analyzed the association between cancer driver genes and SRGs and did not find significant over-representation amongst most cancers.

Conclusions

In summary, our work identified all the SRGs for 33 cancer types from TCGA. In addition, the pan-cancer analysis revealed the similarities and the differences in the biological functions of SRGs across cancers. Given the potential of SRGs in clinical utility, our results can serve as a resource for basic research and biotech applications.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12864-022-08581-x.

Pan-cancer methylome analysis for cancer diagnosis and classification of cancer cell of origin

The accurate and early diagnosis and classification of cancer origin from either tissue or liquid biopsy is crucial for selecting the appropriate treatment and reducing cancer-related mortality. Here, we established the CAncer Cell-of-Origin (CACO) methylation panel using the methylation data of the 28 types of cancer in The Cancer Genome Atlas (7950 patients and 707 normal controls) as well as healthy whole blood samples (95 subjects). We showed that the CACO methylation panel had high diagnostic potential with high sensitivity and specificity in the discovery (maximum AUC = 0.998) and validation (maximum AUC = 1.000) cohorts. Moreover, we confirmed that the CACO methylation panel could identify the cancer cell type of origin using the methylation profile from liquid as well as tissue biopsy, including primary, metastatic, and multiregional cancer samples and cancer of unknown primary, independent of the methylation analysis platform and specimen preparation method. Together, the CACO methylation panel can be a powerful tool for the classification and diagnosis of cancer.

The Prognostic Value and Immunological Role of STEAP1 in Pan-Cancer: A Result of Data-Based Analysis

Purpose. This study is aimed at systematically analyzing the expression, function, and prognostic value of six transmembrane epithelial antigen of the prostate 1 (STEAP1) in various cancers. Methods. The expressions of STEAP1 between normal and tumor tissues were analyzed using TCGA and GTEx. Clinicopathologic data was collected from GEPIA and TCGA. Prognostic analysis was conducted by Cox proportional hazard regression and Kaplan-Meier survival. DNA methylation, mutation features, and molecular subtypes of cancers were also investigated. The top-100 coexpressed genes with STEAP1 were involved in functional enrichment analysis. ESTIMATE algorithm was used to analyze the correlation between STEAP1 and immunity value. The relationships of STEAP1 and biomarkers including tumor mutational burden (TMB), microsatellite instability (MSI), and stemness score as well as chemosensitivity were also illustrated. Results. Among 33 cancers, STEAP1 was overexpressed in 19 cancers such as cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), colon adenocarcinoma, and lymphoid neoplasm diffuse large B cell lymphoma while was downregulated in 5 cancers such as adrenocortical carcinoma, breast invasive carcinoma (BRCA), and kidney chromophobe renal cell carcinoma. STEAP1 has significant prognostic relationships in multiple cancers. 15 cancers exhibited differences of DNA methylation including bladder urothelial carcinoma, BRCA, and CESC. STEAP1 expression was positively correlated to immune molecules especially in thyroid carcinoma and negatively especially in uveal melanoma. STEAP1 was associated with TMB and MSI in certain cancers. In addition, STEAP1 was connected with increased chemosensitivity of drugs such as trametinib and pimasertib. Conclusions. STEAP1 was an underlying target for prognostic prediction in different cancer types and a potential biomarker of TMB, MSI, tumor microenvironment, and chemosensitivity.

Site-Specific Hypermethylation of SST 1stExon as a Biomarker for Predicting the Risk of Gastrointestinal Tract Cancers

Background

DNA methylation is an important epigenetic modification in tumorigenesis, and similar epigenetic regulation mechanisms have been found in the gastrointestinal tract (GIT) cancers. Somatostatin (SST) has been confirmed to be expressed throughout the GIT. This study aimed to simultaneously explore the relationships between the SST methylation and the risks of three GIT cancers (esophageal cancer (EC), gastric cancer (GC), and colorectal cancer (CRC)) and to evaluate its diagnostic value.

Methods

Differentially methylated regions (DMRs) of the SST gene, including TSS200, 1stExon, and the gene body, were identified in GIT cancers by The Cancer Genome Atlas (TCGA) database analysis. Further analyses were conducted in tissue samples of EC (n = 50), GC (n = 99), and CRC (n = 80). The SST methylation was detected by bisulfite-sequencing PCR (BSP), and the SST expression was detected by quantitative real-time reverse transcription-polymerase chain reaction (qRT-PCR).

Results

In GIT cancers, DMR-related CpG islands were mainly located in the 1stExon. The methylation status of the SST 1stExon in the tumor tissues was significantly higher than that in the adjacent noncancerous tissues, and the methylation rates of the specific CpG sites were correlated with clinical phenotypes. The average methylation rate (AMR) of the SST 1stExon was negatively correlated with the SST gene expression in GC and CRC (both P < 0.001). For the diagnosis of GIT cancers, the combined detection of methylation at CpG sites +18 and +129 showed the highest area under the curve (AUC 0.698), with a sensitivity of 59.3% and a specificity of 72.8%.

Conclusions

The site-specific hypermethylation of the SST 1stExon increases the risk of GIT cancers and might be a potential predictive marker for pan-GIT cancers.

SCNN1A Overexpression Correlates with Poor Prognosis and Immune Infiltrates in Ovarian Cancer

Introduction

Ovarian cancer (OV) is a common malignancy affecting women globally; recognizing useful biomarkers has been one of the key priorities. Since SCNN1A was reported to be relevant to tumor progression in a variety of cancers, but rarely in ovarian cancer, we explored the roles of SCNN1A in OV.

Methods

RNA sequencing data from TCGA and GEO were utilized to analyze the expression of SCNN1A and related differentially expressed genes (DEGs) in ovarian cancer. We performed GO, GSEA and immune cell infiltration analysis on SCNN1A-associated DEGs. Correlation of SCNN1A methylation levels and its mRNA expression was analyzed by cBioPortal and UCSC Xena databases. To assess the prognostic impact of SCNN1A, Kaplan–Meier plot analysis and Cox regression analysis were performed; ROC curves and nomogram were also plotted.

Results

Compared to normal tissues, SCNN1A was highly expressed in ovarian cancer. The methylation level of SCNN1A negatively correlated with the SCNN1A expression. Moreover, high expression of SCNN1A was correlated with poor prognosis in OV patients and associated with immune infiltrates.

Conclusion

High SCNN1A expression could be a promising biomarker for poor outcomes in OV and correlated with tumor immune cells infiltration. The findings might help illuminate the function of SCNN1A in tumorigenesis and lay a foundation for further research.

The Multi-Omics Analysis of Key Genes Regulating EGFR-TKI Resistance, Immune Infiltration, SCLC Transformation in EGFR-Mutant NSCLC

Background

Lung cancer is a high-risk malignancy worldwide. The harboring of epidermal growth factor receptor (EGFR) mutations in non-small cell lung cancer (NSCLC) makes EGFR-tyrosine kinase inhibitor (EGFR-TKI) an attractive therapeutic option. However, patients usually suffer the primary and secondary resistance to EGFR-TKI. And the molecular alteration is still not fully clear and needs further study.

Methods

The GEO database was utilized to find the differentially expressed genes (DEGs) in NSCLC profiles resistant to the 1st or 2nd generation EGFR-TKI. We analyzed the expression and pathway enrichment of hub genes, and the prognosis of EGFR mutant/wild-type lung adenocarcinoma (LUAD). Moreover, small cell lung cancer (SCLC) and TKI-resistant profiles were used to find common DEGs, and construct miRNA regulatory network. Analysis was performed of hub genes' related immune infiltration, drug sensitivity, and methylation. Further, we analyzed hub gene expression in EGFR-mutant LUAD and paracancerous tissue by qRT-PCR.

Results

A total of 107 DEGs were found related to TKI resistance. Eleven hub genes were obtained after visualization, of which 5 hub genes were co-expressed in SCLC with common miRNAs. Lower expression of SPP1 (hub gene) was associated with better survival in NSCLC. The immune infiltration analysis showed more CD4+ T cells in the resistant group with high expression of SPP1. SPP1 and CD44 promoters’ methylations were independent prognostic factors of LUAD. And the expression level of SPP1 related to the sensitivity of EGFR-TKIs in multiple cancer cell lines. qRT-PCR validated the higher expression of SPP1 in EGFR-mutant LUAD than in normal tissue.

Conclusion

Our study suggested that the upregulation of SPP1 might induce resistance to the 1st and 2nd generation EGFR-TKI, and influence tumor immune infiltration, resulting in poor survival. ZEB1, SPP1, MUC1, CD44, and ESRP1 might be molecular drivers of SCLC transformation of TKI resistance.

Pan-Cancer DNA Methylation Analysis and Tumor Origin Identification of Carcinoma of Unknown Primary Site Based on Multi-Omics

The metastatic cancer of unknown primary (CUP) sites remains a leading cause of cancer death with few therapeutic options. The aberrant DNA methylation (DNAm) is the most important risk factor for cancer, which has certain tissue specificity. However, how DNAm alterations in tumors differ among the regulatory network of multi-omics remains largely unexplored. Therefore, there is room for improvement in our accuracy in the prediction of tumor origin sites and a need for better understanding of the underlying mechanisms. In our study, an integrative analysis based on multi-omics data and molecular regulatory network uncovered genome-wide methylation mechanism and identified 23 epi-driver genes. Apart from the promoter region, we also found that the aberrant methylation within the gene body or intergenic region was significantly associated with gene expression. Significant enrichment analysis of the epi-driver genes indicated that these genes were highly related to cellular mechanisms of tumorigenesis, including T-cell differentiation, cell proliferation, and signal transduction. Based on the ensemble algorithm, six CpG sites located in five epi-driver genes were selected to construct a tissue-specific classifier with a better accuracy (>95%) using TCGA datasets. In the independent datasets and the metastatic cancer datasets from GEO, the accuracy of distinguishing tumor subtypes or original sites was more than 90%, showing better robustness and stability. In summary, the integration analysis of large-scale omics data revealed complex regulation of DNAm across various cancer types and identified the epi-driver genes participating in tumorigenesis. Based on the aberrant methylation status located in epi-driver genes, a classifier that provided the highest accuracy in tracing back to the primary sites of metastatic cancer was established. Our study provides a comprehensive and multi-omics view of DNAm-associated changes across cancer types and has potential for clinical application.

Pan-Cancer Analyses Reveal Oncogenic Role and Prognostic Value of F-Box Only Protein 22

The F-box protein 22 (FBXO22), an F-box E3 ligase, has been identified to be critically involved in carcinogenesis. However, a systematic assessment of the role of FBXO22 across human cancers is lacking. Here, we performed a pan-cancer analysis to explore the role of FBXO22 in 33 cancer types using multiomic data from The Cancer Genome Atlas (TCGA). First, we found that high FBXO22 expression in multiple cancers was closely associated with poor overall survival and relapse-free survival. Next, we identified ten proteins that interact with FBXO22 and 13 of its target substrates using the STRING database and a literature search to explore the regulatory role of FBXO22 in tumorigenesis. Genes encoding these proteins were found to be significantly enriched in cell cycle negative regulation and ubiquitination pathways. This was confirmed in nonsmall cell lung cancer A549 cells, where FBXO22 overexpression enhanced cyclin-dependent kinase 4 (CDK4) protein levels and promoted cell proliferation. Similarly, overexpression or interference of FBXO22 changed the protein level of one of its substrates, PTEN. Additionally, we found that FBXO22 mutations were accompanied by altered substrate expression, especially in uterine corpus endometrial carcinoma and lung adenocarcinoma; endometrial carcinoma patients with FBXO22 genetic alterations also had better overall and relapse-free survival. Notably, FBXO22 methylation levels were also decreased in most tumors, and hypomethylation of FBXO22 was associated with poor overall survival, relapse-free interval, and progression-free interval in pancreatic adenocarcinoma. Finally, we analyzed the correlation between the abundance of tumor infiltrating lymphocytes (TILs) and FBXO22 expression, copy number variation, and methylation. Multiple algorithms revealed that high FBXO22 expression was associated with lower TIL levels, especially in lung adenocarcinoma, lung squamous cell carcinoma, and sarcoma. Taken together, our findings demonstrate that FBXO22 degrades tumor suppressor genes by ubiquitination and inhibits the cell cycle to promote nonsmall cell lung cancer progression. Our study also provides a relatively comprehensive understanding of the oncogenic role of FBXO22 in different tumors.

Effects of ESCO2 or its methylation on the prognosis, clinical characteristics, immune microenvironment, and pathogenesis of low-grade glioma

The establishment of sister chromatid cohesion N-acetyltransferase 2 (ESCO2) has an important regulatory effect on cell proliferation and division, which is closely related to the malignant process of glioma cells. Therefore, this study attempts to provide a target for biologically targeted therapy for low-grade glioma (LGG) by demonstrating the regulatory effect of ESCO2 during the pathological process of LGG. First, the 1064 samples of LGG transcriptomic data and corresponding clinicopathological information obtained from various databases were included in the study. Second, the chi-squared test showed that the expression of ESCO2 was associated with the malignant characteristics of LGG (recurrence and grade), and Kaplan Meier and multivariate analysis suggested that ESCO2 was an independent risk factor, resulting in a significant reduction in the overall duration of survival of patients. Third, co-expression analysis showed that the level of mRNA expression of ESCO2 was negatively regulated by multiple methylation sites (cg04108328, cg12564175, and cg26534677), and the hypermethylation status of cg12564175 could prolong the overall survival of patients. Fourth, the Tumor Immune Estimation Resource (TIMER) database shows that ESCO2 can have a positive regulatory relationship with six different immune cells, such as CD8 + T cells and macrophages, and a positive expression relationship with PD-1 and PD-L1. Finally, Gene Set Enrichment Analysis (GSEA) showed that ESCO2 may play a carcinogenic role by affecting cell replication and DNA repair. In summary, this study confirmed the carcinogenic effect of ESCO2 on LGG for the first time. It is speculated that both the mRNA of ESCO2 and its methylation site (cg12564175) can be useful biological targets for molecular targeted therapy of LGG.

Genome-wide DNA methylation profiling and identification of potential pan-cancer and tumor-specific biomarkers

DNA methylation alterations have already been linked to cancer, and their usefulness for therapy and diagnosis has encouraged research into the human epigenome. Several biomarker studies have focused on identifying cancer types individually, yet common cancer and multi-cancer markers are still underexplored. We used The Cancer Genome Atlas (TCGA) to investigate genome-wide methylation profiles of 14 different cancer types and developed a three-step computational approach to select candidate biomarker CpG sites. In total, 1,991 pan-cancer and between 75 and 1,803 cancer-specific differentially methylated CpG sites were discovered. Differentially methylated blocks and regions were also discovered for the first time on such a large-scale. Through a three-step computational approach, a combination of four pan-cancer CpG markers was identified from these sites and externally validated (AUC = 0.90), maintaining comparable performance across tumor stages. Additionally, 20 tumor-specific CpG markers were identified and made up the final type-specific prediction model, which could accurately differentiate tumor types (AUC = 0.87-0.99). Our study highlights the power of the methylome as a rich source of cancer biomarkers, and the signatures we identified provide a new resource for understanding cancer mechanisms on the wider genomic scale with strong applicability in the context of new minimally invasive cancer detection assays.

The Application of Bayesian Methods in Cancer Prognosis and Prediction

With the development of high-throughput biological techniques, high-dimensional omics data have emerged. These molecular data provide a solid foundation for precision medicine and prognostic prediction of cancer. Bayesian methods contribute to constructing prognostic models with complex relationships in omics and improving performance by introducing different prior distribution, which is suitable for modelling the high-dimensional data involved. Using different omics, several Bayesian hierarchical approaches have been proposed for variable selection and model construction. In particular, the Bayesian methods of multi-omics integration have also been consistently proposed in recent years. Compared with single-omics, multi-omics integration modelling will contribute to improving predictive performance, gaining insights into the underlying mechanisms of tumour occurrence and development, and the discovery of more reliable biomarkers. In this work, we present a review of current proposed Bayesian approaches in prognostic prediction modelling in cancer.

DNA methylation and single-nucleotide polymorphisms in DDX58 are associated with hand, foot and mouth disease caused by enterovirus 71

BACKGROUND

This research aimed to explore the association between the RIG-I-like receptor (RIG-I and MDA5 encoded by DDX58 and IFIH1, respectively) pathways and the risk or severity of hand, foot, and mouth disease caused by enterovirus 71 (EV71-HFMD). In this context, we explored the influence of gene methylation and polymorphism on EV71-HFMD.

METHODOLOGY/PRINCIPAL FINDINGS

60 healthy controls and 120 EV71-HFMD patients, including 60 mild EV71-HFMD and 60 severe EV71-HFMD patients, were enrolled. First, MiSeq was performed to explore the methylation of CpG islands in the DDX58 and IFIH1 promoter regions. Then, DDX58 and IFIH1 expression were detected in PBMCs using RT-qPCR. Finally, imLDR was used to detect DDX58 and IFIH1 single-nucleotide polymorphism (SNP) genotypes. Severe EV71-HFMD patients exhibited higher DDX58 promoter methylation levels than healthy controls and mild EV71-HFMD patients. DDX58 promoter methylation was significantly associated with severe HFMD, sex, vomiting, high fever, neutrophil abundance, and lymphocyte abundance. DDX58 expression levels were significantly lower in mild patients than in healthy controls and lower in severe patients than in mild patients. Binary logistic regression analysis revealed statistically significant differences in the genotype frequencies of DDX58 rs3739674 between the mild and severe groups. GeneMANIA revealed that 19 proteins displayed correlations with DDX58, including DHX58, HERC5, MAVS, RAI14, WRNIP1 and ISG15, and 19 proteins displayed correlations with IFIH1, including TKFC, IDE, MAVS, DHX58, NLRC5, TSPAN6, USP3 and DDX58.

CONCLUSIONS/SIGNIFICANCE

DDX58 expression and promoter methylation were associated with EV71 infection progression, especially in severe EV71-HFMD patients. The effect of DDX58 in EV71-HFMD is worth further attention.

A Novel Gene Prognostic Signature Based on Differential DNA Methylation in Breast Cancer

Background: DNA methylation played essential roles in regulating gene expression. The impact of DNA methylation status on the occurrence and development of cancers has been well demonstrated. However, little is known about its prognostic role in breast cancer (BC).

Materials: The Illumina Human Methylation450 array (450k array) data of BC was downloaded from the UCSC xena database. Transcriptomic data of BC was downloaded from the Cancer Genome Atlas (TCGA) database. Firstly, we used univariate and multivariate Cox regression analysis to screen out independent prognostic CpGs, and then we identified methylation-associated prognosis subgroups by consensus clustering. Next, a methylation prognostic model was developed using multivariate Cox analysis and was validated with the Illumina Human Methylation27 array (27k array) dataset of BC. We then screened out differentially expressed genes (DEGs) between methylation high-risk and low-risk groups and constructed a methylation-based gene prognostic signature. Further, we validated the gene signature with three subgroups of the TCGA-BRCA dataset and an external dataset GSE146558 from the Gene Expression Omnibus (GEO) database.

Results: We established a methylation prognostic signature and a methylation-based gene prognostic signature, and there was a close positive correlation between them. The gene prognostic signature involved six genes: IRF2, KCNJ11, ZDHHC9, LRP11, PCMT1, and TMEM70. We verified their expression in mRNA and protein levels in BC. Both methylation and methylation-based gene prognostic signatures showed good prognostic stratification ability. The AUC values of 3-years, 5-years overall survival (OS) were 0.737, 0.744 in the methylation signature and 0.725, 0.715 in the gene signature, respectively. In the validation groups, high-risk patients were confirmed to have poorer OS. The AUC values of 3 years were 0.757, 0.735, 0.733 in the three subgroups of TCGA dataset and 0.635 in GSE146558 dataset.

Conclusion: This study revealed the DNA methylation landscape and established promising methylation and methylation-based gene prognostic signatures that could serve as potential prognostic biomarkers and therapeutic targets.

Integral Analyses of Competing Endogenous RNA Mechanisms and DNA Methylation Reveal Regulatory Mechanisms in Osteosarcoma

Background: Osteosarcoma (OS) is the most common primary malignant bone tumour in children and adolescents, with rapid growth, frequent metastasis, and a poor prognosis, but its pathogenesis has not been fully elucidated. Exploring the pathogenesis of OS is of great significance for improving diagnoses and finding new therapeutic targets.

Methods: Differentially expressed circRNAs (DECs), miRNAs (DEMs), methylated DNA sites (DMSs), and mRNAs (DEGs) were identified between OS and control cell lines. GSEA of DEGs and functional enrichment analysis of methylated DEGs were carried out to further identify potential biological processes. Online tools were used to predict the miRNA binding sites of DECs and the mRNA binding sites of DEMs, and then construct a circRNA-miRNA-mRNA network. Next, an analysis of the interaction between methylated DEGs was performed with a protein-protein interaction (PPI) network, and hub gene identification and survival analysis were carried out. The expression pattern of circRNA-miRNA-mRNA was validated by real-time PCR.

Results: GSEA and functional enrichment analysis indicated that DEGs and methylated DEGs are involved in important biological processes in cancer. Hsa_circ_0001753/has_miR_760/CD74 network was constructed and validated in cell lines. Low expression levels of CD74 are associated with poor overall survival times and show good diagnostic ability.

Conclusion: Methylated DEGs may be involved in the development of OS, and the hsa_circ_0001753/has_miR_760/CD74 network may serve as a target for the early diagnosis of and targeted therapy for OS.