Observed Survival Interval: A Supplement to TCGA Pan-Cancer Clinical Data Resource

A deep learning approach based on multi-omics data integration to construct a risk stratification prediction model for skin cutaneous melanoma

PURPOSE

Skin cutaneous melanoma (SKCM) is a highly aggressive melanocytic carcinoma whose high heterogeneity and complex etiology make its prognosis difficult to predict. This study aimed to construct a risk subtype typing model for SKCM.

METHODS

The study proposes a deep learning framework combining early fusion feature autoencoder (AE) and late fusion feature AE for risk subtype prediction of SKCM. The deep learning framework integrates mRNA, miRNA, and DNA methylation data of SKCM patients from The Cancer Genome Atlas (TCGA), and clusters the screened multi-omics features associated with survival prognosis to identify risk subtypes. Differential expression analysis and functional enrichment analysis were performed between risk subtypes, while SVM classifiers were constructed between differentially expressed genes (DEGs) obtained by Least Absolute Shrinkage and Selection Operator (LASSO) logistic regression screening and risk subtype labels inferred from multi-omics data, and the predictive robustness of risk subtypes inferred from the risk subtype classification prediction model was validated using two independent datasets.

RESULTS

The deep learning framework that combined early fusion feature AE with late fusion feature AE distinguished the two best risk subtypes compared to the multi-omics integration approach with single strategy AE or PCA. A promising C-index (C-index = 0.748) and a significant difference in survival (log-rank P value = 4.61 × 10-9) were found between the identified risk subtypes. The DEGs with the top significance values together with differentially expressed miRNAs provided the biological interpretation of risk subtypes on SKCM. Finally, the framework was applied to predict risk subtypes in two independent test datasets of SKCM patients, all of which showed good predictive power (C-index > 0.680) and significant survival differences (log-rank P value < 0.01).

CONCLUSION

The SKCM risk subtypes identified by integrating multi-omics data based on deep learning can not only improve the understanding of the molecular mechanisms of SKCM, but also provide clinicians with assistance in treatment decisions.

FAM3 family genes are associated with prognostic value of human cancer: a pan-cancer analysis

Family with sequence similarity three member (FAM3) plays a crucial role in the malignant development of various cancers of human. However, there remains doubtful what specific role of FAM3 family genes in pan-cancer. Our study aimed to investigate the role of FAM3 family genes in prognosis, immune subtype, tumor immune microenvironment, stemness score, and anticancer drug sensitivity of pan-cancer. We obtained data from UCSC Xena GDC and CellMiner databases, and used them to study the correlation of the expression, survival, immune subtype, tumor microenvironment, stemness score, and anticancer drug sensitivity between FAM3 family genes with pan-cancer. Furthermore, we investigated the tumor cellular functions and clinical prognostic value FAMC3 in pancreatic cancer (PAAD) using cellular experiments and tissue microarray. Cell Counting Kit-8 (CCK-8), transwell invasion, wound-healing and apoptosis assays were performed to study the effect of FAM3C on SW1990 cells' proliferation, migration, invasion and apoptosis. Immunohistochemical staining was used to study the relationship between FAM3C expression and clinical characteristics of pancreatic cancer patients. The results revealed that FAM3 family genes are significantly differential expression in tumor and adjacent normal tissues in 7 cancers (CHOL, HNSC, KICH, LUAD, LUSC, READ, and STAD). The expression of FAM3 family genes were negatively related with the RNAss, and robust correlated with immune type, tumor immune microenvironment and drug sensitivity. The expression of FAM3 family genes in pan-cancers were significantly different in immune type C1 (wound healing), C2 (IFN-gamma dominant), C3 (inflammatory), C4 (lymphocyte depleted), C5 (immunologically quiet), and C6 (TGF-beta dominant). Meanwhile, overexpression FAM3C promoted SW1990 cells proliferation, migration, invasion and suppressed SW1990 cells apoptosis. While knockdown of FAM3C triggered opposite results. High FAM3C expression was associated with duodenal invasion, differentiation and liver metastasis. In summary, this study provided a new perspective on the potential therapeutic role of FAM3 family genes in pan-cancer. In particular, FAM3C may play an important role in the occurrence and progression of PAAD.

A General Framework for Identifying Hierarchical Interactions and Its Application to Genomics Data

The analysis of hierarchical interactions has long been a challenging problem due to the large number of candidate main effects and interaction effects, and the need for accommodating the "main effects, interactions" hierarchy. The two-stage analysis methods enjoy simplicity and low computational cost, but contradict the fact that the outcome of interest is attributable to the joint effects of multiple main factors and their interactions. The existing joint analysis methods can accurately describe the underlying data generating process, but suffer from prohibitively high computational cost. And it is not straightforward to extend their optimization algorithms to general loss functions. To address this need, we develop a new computational method that is much faster than the existing joint analysis methods and rivals the runtimes of two-stage analysis. The proposed method, HierFabs, adopts the framework of the forward and backward stagewise algorithm and enjoys computational efficiency and broad applicability. To accommodate hierarchy without imposing additional constraints, it has newly developed forward and backward steps. It naturally accommodates the strong and weak hierarchy, and makes optimization much simpler and faster than in the existing studies. Optimality of HierFabs sequences is investigated theoretically. Simulations show that it outperforms the existing methods. The analysis of TCGA data on melanoma demonstrates its competitive practical performance.

M6A-Related Bioinformatics Analysis Reveals a New Prognostic Risk Signature in Cutaneous Malignant Melanoma

Cutaneous malignant melanoma (CMM) is the most deadly skin cancer worldwide. Despite advances in the treatments of CMM, its incidence and mortality rates are still increasing. N6-methyladenosine (m6A) is the most common form of RNA modification and has attracted increasing interest in cancer initiation and progression. However, the role of m6A regulators in CMM and their correlation with prognosis remain elusive. Here, we demonstrated that by applying consensus clustering, all CMM patient cases can be divided into two clusters based on overall expression levels of 25 m6A genes. We systematically analyzed the prognostic value of the 25 m6A RNA methylation regulators in CMM and found that ELAVL1, ABCF1, and IGF2BP1 yield the highest scores for predicting the prognosis of CMM. Accordingly, we derived a risk signature consisting of three selected m6A genes as an independent prognostic marker for CMM and validated our findings with data derived from a different CMM cohort. Next, we determined that CNVs in m6A genes had a significant negative impact on patient survival. The mRNA expression levels of m6A genes were correlated with CNV mutation. Moreover, in the selected three risk signature m6A regulators, GSEA analysis showed that they were closely correlated with inflammation and immune pathways. TME analysis proved that m6A gene expressions were negatively correlated with immune cell infiltration. In conclusion, m6A regulators are vital participants in CMM pathology; and ELAVL1, ABCF1, and IGF2BP1 mRNA levels are valuable factors for prognosis prediction and treatment strategy development.

The Prognostic and Predictive Role of Xeroderma Pigmentosum Gene Expression in Melanoma

Background

Assessment of immune-specific markers is a well-established approach for predicting the response to immune checkpoint inhibitors (ICIs). Promising candidates as ICI predictive biomarkers are the DNA damage response pathway genes. One of those pathways, which are mainly responsible for the repair of DNA damage caused by ultraviolet radiation, is the nucleotide excision repair (NER) pathway. Xeroderma pigmentosum (XP) is a hereditary disease caused by mutations of eight different genes of the NER pathway, or POLH, here together named the nine XP genes. Anecdotal evidence indicated that XP patients with melanoma or other skin tumors responded impressively well to anti-PD-1 ICIs. Hence, we analyzed the expression of the nine XP genes as prognostic and anti-PD-1 ICI predictive biomarkers in melanoma.

Methods

We assessed mRNA gene expression in the TCGA-SKCM dataset (n = 445) and two pooled clinical melanoma cohorts of anti-PD-1 ICI (n = 75). In TCGA-SKCM, we applied hierarchical clustering on XP genes to reveal clusters, further utilized as XP cluster scores. In addition, out of 18 predefined genes representative of a T cell inflamed tumor microenvironment, the TIS score was calculated. Besides these scores, the XP genes, immune-specific single genes (CD8A, CXCL9, CD274, and CXCL13) and tumor mutational burden (TMB) were cross-correlated. Survival analysis in TCGA-SKCM was conducted for the selected parameters. Lastly, the XP response prediction value was calculated for the two pooled anti-PD-1 cohorts by classification models.

Results

In TCGA-SKCM, expression of the XP genes was divided into two clusters, inversely correlated with immune-specific markers. A higher ERCC3 expression was associated with improved survival, particularly in younger patients. The constructed models utilizing XP genes, and the XP cluster scores outperformed the immune-specific gene-based models in predicting response to anti-PD-1 ICI in the pooled clinical cohorts. However, the best prediction was achieved by combining the immune-specific gene CD274 with three XP genes from both clusters.

Conclusion

Our results suggest pre-therapeutic XP gene expression as a potential marker to improve the prediction of anti-PD-1 response in melanoma.

Revisiting miRNA Association with Melanoma Recurrence and Metastasis from a Machine Learning Point of View

The diagnostic and prognostic value of miRNAs in cutaneous melanoma (CM) has been broadly studied and supported by advanced bioinformatics tools. From early studies using miRNA arrays with several limitations, to the recent NGS-derived miRNA expression profiles, an accurate diagnostic panel of a comprehensive pre-specified set of miRNAs that could aid timely identification of specific cancer stages is still elusive, mainly because of the heterogeneity of the approaches and the samples. Herein, we summarize the existing studies that report several miRNAs as important diagnostic and prognostic biomarkers in CM. Using publicly available NGS data, we analyzed the correlation of specific miRNA expression profiles with the expression signatures of known gene targets. Combining network analytics with machine learning, we developed specific non-linear classification models that could successfully predict CM recurrence and metastasis, based on two newly identified miRNA signatures. Subsequent unbiased analyses and independent test sets (i.e., a dataset not used for training, as a validation cohort) using our prediction models resulted in 73.85% and 82.09% accuracy in predicting CM recurrence and metastasis, respectively. Overall, our approach combines detailed analysis of miRNA profiles with heuristic optimization and machine learning, which facilitates dimensionality reduction and optimization of the prediction models. Our approach provides an improved prediction strategy that could serve as an auxiliary tool towards precision treatment.

Integrative Predictive Modeling of Metastasis in Melanoma Cancer Based on MicroRNA, mRNA, and DNA Methylation Data

Introduction: Despite the significant progress in understanding cancer biology, the deduction of metastasis is still a challenge in the clinic. Transcriptional regulation is one of the critical mechanisms underlying cancer development. Even though mRNA, microRNA, and DNA methylation mechanisms have a crucial impact on the metastatic outcome, there are no comprehensive data mining models that combine all transcriptional regulation aspects for metastasis prediction. This study focused on identifying the regulatory impact of genetic biomarkers for monitoring metastatic molecular signatures of melanoma by investigating the consolidated effect of miRNA, mRNA, and DNA methylation.

Method: We developed multiple machine learning models to distinguish the metastasis by integrating miRNA, mRNA, and DNA methylation markers. We used the TCGA melanoma dataset to differentiate between metastatic melanoma samples by assessing a set of predictive models. For this purpose, machine learning models using a support vector machine with different kernels, artificial neural networks, random forests, AdaBoost, and Naïve Bayes are compared. An iterative combination of differentially expressed miRNA, mRNA, and methylation signatures is used as a candidate marker to reveal each new biomarker category’s impact. In each iteration, the performances of the combined models are calculated. During all comparisons, the choice of the feature selection method and under and oversampling approaches are analyzed. Selected biomarkers of the highest performing models are further analyzed for the biological interpretation of functional enrichment.

Results: In the initial model, miRNA biomarkers can identify metastatic melanoma with an 81% F-score. The addition of mRNA markers upon miRNA increased the F-score to 92%. In the final integrated model, the addition of the methylation data resulted in a similar F-score of 92% but produced a stable model with low variance across multiple trials.

Conclusion: Our results support the role of miRNA regulation in metastatic melanoma as miRNA markers model metastasis outcomes with high accuracy. Moreover, the integrated evaluation of miRNA with mRNA and methylation biomarkers increases the model’s power. It populates selected biomarkers on the metastasis-associated pathways of melanoma, such as the “osteoclast”, “Rap1 signaling”, and “chemokine signaling” pathways.

Source Code:https://github.com/aysegul-kt/MelonomaMetastasisPrediction/

Zic Family Member 2 (ZIC2): a Potential Diagnostic and Prognostic Biomarker for Pan-Cancer

Background: As a transcription factor, Zinc finger protein ZIC2 can interact with various DNAs and proteins. Current studies have shown that ZIC2 plays an oncogene role in various cancers. In this study, we systematically characterize the prevalence and predictive value of ZIC2 expression across multiple cancer types. Methods: We mined several public databases, including Oncomine, the Cancer Genome Atlas (TCGA), cBioPortal, Kaplan-Meier Plotter and PrognoScan to evaluated the differentially expressed ZIC2 between tumor samples and normal control samples in pan-cancner, and then explored the association between ZIC2 expression and patient survival, prognosis and clinicopathologic stage. We also analyzed the relationship between tumor mutation burden (TMB), microsatellite instability (MSI), tumor microenvironment, tumor- and immune-related genes and ZIC2 expression. Finally, we explored the potential signaling pathway mechanism through gene set enrichment analysis (GSEA). Results: ZIC2 expression was higher in most cancer tissues compared with adjacent normal tissues. High ZIC2 expression was associated with worse prognosis and a higher clinicopathologic stage. ZIC2 expression was strongly associated with the TMB, MSI, tumor microenvironment and tumor- and immune-related genes. The GSEA revealed that multiple tumor- and immune-related pathways were differentially enriched in ZIC2 high or low expression phenotype. Conclusion: ZIC2 expression may be a potential prognostic molecular biomarker of poor survival in pan-cancer and may act as an oncogene with a strong effect in the processes of tumorigenesis and progression.

LRIG1 is a conserved EGFR regulator involved in melanoma development, survival and treatment resistance

Leucine-rich repeats and immunoglobulin-like domains 1 (LRIG1) is a pan-negative regulator of receptor tyrosine kinase (RTK) signaling and a tumor suppressor in several cancers, but its involvement in melanoma is largely unexplored. Here, we aim to determine the role of LRIG1 in melanoma tumorigenesis, RTK signaling, and BRAF inhibitor resistance. We find that LRIG1 is downregulated during early tumorigenesis and that LRIG1 affects activation of the epidermal growth factor receptor (EGFR) in melanoma cells. LRIG1-dependent regulation of EGFR signaling is evolutionary conserved to the roundworm C. elegans, where negative regulation of the EGFR-Ras-Raf pathway by sma-10/LRIG completely depends on presence of the receptor let-23/EGFR. In a cohort of metastatic melanoma patients, we observe an association between LRIG1 and survival in the triple wild-type subtype and in tumors with high EGFR expression. During in vitro development of BRAF inhibitor resistance, LRIG1 expression decreases; and mimics LRIG1 knockout cells for increased EGFR expression. Treating resistant cells with recombinant LRIG1 suppresses AKT activation and proliferation. Together, our results show that sma-10/LRIG is a conserved regulator of RTK signaling, add to our understanding of LRIG1 in melanoma and identifies recombinant LRIG1 as a potential therapeutic against BRAF inhibitor-resistant melanoma.

Targeted Stat2 deletion in conventional dendritic cells impairs CTL responses but does not affect antibody production

STAT2 is a central component of the ISGF3 transcriptional complex downstream of type I interferon (IFN-I) signaling. The significance of in vivo IFN-I/STAT1 signals in cDCs is well-established in the generation of antitumor cytotoxic T cell (CTL) responses. However, the role of STAT2 has remained elusive. Here, we report a clinical correlation between cDC markers and STAT2 associated with better survival in human metastatic melanoma. In a murine tumor transplantation model, targeted Stat2 deletion in CD11c+cDCs enhanced tumor growth unaffected by IFNβ therapy. Furthermore, STAT2 was essential for both, the activation of CD8a+cDCs and CD11b+cDCs and antigen cross-presentation in vivo for the generation of robust T cell killing response. In contrast, STAT2 in CD11c+cDCs was dispensable for stimulating an antigen-specific humoral response, which was impaired in global Stat2 deficient mice. Thus, our studies indicate that STAT2 in cDCs is critical in host IFN-I signals by sculpting CTL responses against tumors.

A Novel Signature of 23 Immunity-Related Gene Pairs Is Prognostic of Cutaneous Melanoma

In this study, we aimed to identify an immune-related signature for predicting prognosis in cutaneous melanoma (CM). Sample data from The Cancer Genome Atlas (TCGA; n = 460) were used to develop a prognostic signature with 23 immune-related gene pairs (23 IRGPs) for CM. Patients were divided into high- and low-risk groups using the TCGA and validation datasets GSE65904 (n = 214), GSE59455 (n = 141), and GSE22153 (n = 79). The ability of the 23-IRGP signature to predict CM was precise, with the stratified high-risk groups showing a poor prognosis, and it had a significant predictive power when used for immune microenvironment and biological analyses. We subsequently established a novel promising prognostic model in CM to determine the association between the immune microenvironment and CM patient results. This approach may be used to discover signatures in other diseases while avoiding the technical biases associated with other platforms.

Mining database for the expression and gene regulation network of JAK2 in skin cutaneous melanoma

BACKGROUND

Skin cutaneous melanoma (SKCM) is the most common subtype of skin malignancy, with ever-increasing incidence, mortality, and disease burden. Dysregulation of JAK-STATs signaling pathway is involved in the pathogenesis and progression of cancers, thus affecting the prognosis of cancer patients. The function of JAKs in SKCM is still not clarified.

METHODS

A total of five online portal (GEPIA, TIMER, GeneMANIA, LinkedOmics, and GSCALite) is used to mine the expression and gene regulation network JAK2 in SKCM.

RESULTS

JAK2 expression was downregulated in SKCM and significantly associated with pathological stage and the prognosis of patients. The functions of JAK2 and associated genes were primarily involved in the DNA recombination, cell cycle checkpoint, metabolic process, NOD-like receptor signaling pathways, p53 signaling pathway and apoptosis. JAK2 level was significantly correlated with the abundance of immune cells and the level of immune biomarkers. Low expression of JAK2 were resistant to QL-VIII-58, TL-1-85, Ruxolitinib, TG101348 and Sunitinib.

CONCLUSIONS

Our results reveal the expression and gene regulation network of JAK2 in skin cutaneous melanoma, providing more evidences about the role of JAK2 in carcinogenesis.

Identification of CD38 as a potential biomarker in skin cutaneous melanoma using bioinformatics analysis

Skin cutaneous melanoma (SKCM) is the most aggressive type of skin cancer, with a high rate of metastasis and mortality; however, identification of biomarkers for the treatment of SKCM is required. Cluster of differentiation (CD)38 has emerged as an effective target for therapeutic drugs in several types of cancer, such as chronic lymphocytic leukemia and multiple myeloma. In the present study, to determine the contribution of CD38 to the diagnosis of SKCM, Gene Expression Profiling Interactive Analysis 2 and University of Alabama Cancer Database online tools were used to analyze The Cancer Genome Atlas-SKCM dataset. Moreover, Search Tool for the Retrieval of Interacting Genes/Proteins and GeneMANIA databases were used to determine protein-protein interaction networks and potential functions. To the best of our knowledge, the results of the present study indicated for the first time that high expression levels of CD38 were a favorable diagnostic factor for SKCM. Moreover, a correlation between CD38 expression levels and the survival probability of patients with SKCM was identified. Integrative analysis predicted that nine genes were correlated with CD38 in SKCM, and the similarity of these genes in SKCM expression and a survival heatmap was verified. Gene ontology enrichment analysis using the Metascape tool revealed that CD38 and its correlated genes were significantly enriched in lymphocyte activation and T cell differentiation regulation. Collectively, the bioinformatics analysis revealed that CD38 might serve as a potential diagnostic predictor for SKCM.

Predicting the Risk of Melanoma Metastasis Using an Immune Risk Score in the Melanoma Cohort

Melanoma is a highly aggressive cancer, attracting increasing attention worldwide. The 5-year survival rate of patients with metastatic melanoma is low. Therefore, it is critical to identify potential effective biomarkers for diagnosis of melanoma metastasis. In the present study, the melanoma cohort and immune genes were obtained from the Cancer Genome Atlas (TCGA) database and the ImmPort database, respectively. Then, we constructed the immune risk score (IRS) using univariate and multivariate logistic analysis. The area under the curve (AUC) of IRS in sequencing samples and the initial diagnosis patients was 0.90 and 0.80, respectively. Besides, IRS could add benefits for metastasis diagnosis. For sequencing samples, IRS (OR = 16.35, 95% CI = 8.74–30.59) increased the odds for melanoma metastasis. Similar results were obtained in the initial diagnosis patients (OR = 8.93, 95% CI = 3.53–22.61). A composite nomogram was built based on IRS and clinical information with well-fitted calibration curves. We further used other independent melanoma cohorts from Gene Expression Omnibus (GEO) databases to confirm the reliability and validity of the IRS (AUC > 0.75, OR > 1.04, and P value < 0.01 in all cohorts). In conclusion, IRS is significantly associated with melanoma metastasis and can be a novel effective signature for predicting the metastasis risk.

Poor clinical outcome in metastatic melanoma is associated with a microRNA-modulated immunosuppressive tumor microenvironment

Background

Interaction between malignant cells and immune cells that reside within the tumor microenvironment (TME) modulate different aspects of tumor development and progression. Recent works showed the importance of miRNA-containing extracellular vesicles in this crosstalk.

Methods

Interested in understanding the interplay between melanoma and immune-related TME cells, we characterized the TCGA’s metastatic melanoma samples according to their tumor microenvironment profiles, HLA-I neoepitopes, transcriptome profile and classified them into three groups. Moreover, we combined our results with melanoma single-cell gene expression and public miRNA data to better characterize the regulatory network of circulating miRNAs and their targets related to immune evasion and microenvironment response.

Results

The group associated with a worse prognosis showed phenotypic characteristics that favor immune evasion, including a strong signature of suppressor cells and less stable neoantigen:HLA-I complexes. Conversely, the group with better prognosis was marked by enrichment in lymphocyte and MHC signatures. By analyzing publicly available melanoma single-cell RNA and microvesicle microRNAs sequencing data we identified circulating microRNAs potentially involved in the crosstalk between tumor and TME cells. Candidate miRNA/target gene pairs with previously reported roles in tumor progression and immune escape mechanisms were further investigated and demonstrated to impact patient’s overall survival not only in melanoma but across different tumor types.

Conclusion

Our results underscore the impact of tumor-microenvironment interactions on disease outcomes and reveal potential non-invasive biomarkers of prognosis and treatment response.

Survival between synchronous and non-synchronous multiple primary cutaneous melanomas-a SEER database analysis

Background

There is no criterion to distinguish synchronous and non-synchronous multiple primary cutaneous melanomas (MPMs). This study aimed to distinguish synchronous and non-synchronous MPMs and compare the survivals of them using the Surveillance, Epidemiology, and End Results database.

Methods

Synchronous and non-synchronous MPMs were distinguished by fitting the double log transformed distribution of the time interval between the first and second primary cutaneous melanomas (TIFtS) through a piecewise linear regression. The overall and melanoma-specific survivals were compared by the Kaplan-Meier method and Cox proportional hazard model through modeling the occurrence of synchronous MPMs as a time-dependent variable.

Results

The distribution of TIFtS was composed by three power-law distributions. According to its first inflection point, synchronous MPMs were defined as tumors that occurred within 2 months. The Kaplain-Meier plot revealed a significant inferior survival for synchronous MPMs than non-synchronous MPMs (P < 0.0001), and the occurrence of synchronous MPM was a risk factor for overall survival of cutaneous melanoma (CM) (hazard ratio: 2.213; (95% CI [2.087-2.346]); P < 0.0001).

Conclusions

This study provided data analysis evidences for using 2 months to distinguish synchronous MPMs and non-synchronous MPMs. Furthermore, the occurrence of synchronous MPM was a risk factor for prognosis of patients with CM.

A six-gene prognostic model predicts overall survival in bladder cancer patients

Background

The fatality and recurrence rates of bladder cancer (BC) have progressively increased. DNA methylation is an influential regulator associated with gene transcription in the pathogenesis of BC. We describe a comprehensive epigenetic study performed to analyse DNA methylation-driven genes in BC.

Methods

Data related to DNA methylation, the gene transcriptome and survival in BC were downloaded from The Cancer Genome Atlas (TCGA). MethylMix was used to detect BC-specific hyper-/hypo-methylated genes. Metascape was used to carry out gene ontology (GO) enrichment and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses. A least absolute shrinkage and selection operator (LASSO)-penalized Cox regression was conducted to identify the characteristic dimension decrease and distinguish prognosis-related methylation-driven genes. Subsequently, we developed a six-gene risk evaluation model and a novel prognosis-related nomogram to predict overall survival (OS). A survival analysis was carried out to explore the individual prognostic significance of the six genes.

Results

In total, 167 methylation-driven genes were identified. Based on the LASSO Cox regression, six genes, i.e., ARHGDIB, LINC00526, IDH2, ARL14, GSTM2, and LURAP1, were selected for the development of a risk evaluation model. The Kaplan–Meier curve indicated that patients in the low-risk group had considerably better OS (P = 1.679e−05). The area under the curve (AUC) of this model was 0.698 at 3 years of OS. The verification performed in subgroups demonstrated the validity of the model. Then, we designed an OS-associated nomogram that included the risk score and clinical factors. The concordance index of the nomogram was 0.694. The methylation levels of IDH2 and ARL14 were appreciably related to the survival results. In addition, the methylation and gene expression-matched survival analysis revealed that ARHGDIB and ARL14 could be used as independent prognostic indicators. Among the six genes, 6 methylation sites in ARHGDIB, 3 in GSTM2, 1 in ARL14, 2 in LINC00526 and 2 in LURAP1 were meaningfully associated with BC prognosis. In addition, several abnormal methylated sites were identified as linked to gene expression.

Conclusion

We discovered differential methylation in BC patients with better and worse survival and provided a risk evaluation model by merging six gene markers with clinical characteristics.