EGLN2 DNA methylation and expression interact with HIF1A to affect survival of early-stage NSCLC

Development and experimental validation of a machine learning-based disulfidptosis-related ferroptosis score for hepatocellular carcinoma

Disulfidoptosis and ferroptosis are two distinct programmed cell death pathways that have garnered considerable attention due to their potential as therapeutic targets. However, despite their significance of these pathways, the role of disulfidoptosis-related ferroptosis genes in hepatocellular carcinoma (HCC) remains unclear. In this study, we employed a comprehensive approach that utilized various sophisticated techniques such as Pearson analysis, differential analysis, uniCox regression, lasso, ranger, and multivariable Cox regression to develop the disulfidoptosis-related ferroptosis (DRF) score. We then classified patients with HCC into high- and low-score groups to examine the association between the DRF score and various outcomes, including prognosis, functional enrichment, immune infiltration, immunotherapy, TACE sensitivity, drug sensitivity, and single-cell level function. Finally, we conducted in vitro experiments to validate the function of KIF20A. Our analysis revealed that KIF20A, G6PD, SLC7A11, and SLC2A1 were integral to constructing the DRF score. Our findings showed that patients with low DRF scores had significantly better prognoses and were more responsive to immunotherapy, TACE, and chemotherapy than those with high DRF scores. Based on our results obtained from bulk RNA-seq, single-cell RNA-seq, and in vitro experiments, we identified the cell cycle pathway as the primary distinguished factor between high-score and low-score groups. This study sheds light on the contribution of disulfidoptosis-related ferroptosis genes to the development and progression of HCC. The information gleaned from this study can be leveraged to improve our understanding of their potential as therapeutic targets for HCC treatment.

SARS-CoV-2 Infection to Premature Neuronal Aging and Neurodegenerative Diseases: Is there any Connection with Hypoxia?

The pandemic of coronavirus disease-2019 (COVID-19), caused by SARS-CoV-2, has become a global concern as it leads to a spectrum of mild to severe symptoms and increases death tolls around the world. Severe COVID-19 results in acute respiratory distress syndrome, hypoxia, and multi- organ dysfunction. However, the long-term effects of post-COVID-19 infection are still unknown. Based on the emerging evidence, there is a high possibility that COVID-19 infection accelerates premature neuronal aging and increases the risk of age-related neurodegenerative diseases in mild to severely infected patients during the post-COVID period. Several studies correlate COVID-19 infection with neuronal effects, though the mechanism through which they contribute to the aggravation of neuroinflammation and neurodegeneration is still under investigation. SARS-CoV-2 predominantly targets pulmonary tissues and interferes with gas exchange, leading to systemic hypoxia. The neurons in the brain require a constant supply of oxygen for their proper functioning, suggesting that they are more vulnerable to any alteration in oxygen saturation level that results in neuronal injury with or without neuroinflammation. We hypothesize that hypoxia is one of the major clinical manifestations of severe SARS-CoV-2 infection; it directly or indirectly contributes to premature neuronal aging, neuroinflammation, and neurodegeneration by altering the expression of various genes responsible for the survival of the cells. This review focuses on the interplay between COVID-19 infection, hypoxia, premature neuronal aging, and neurodegenerative diseases and provides a novel insight into the molecular mechanisms of neurodegeneration.

Identifying Macrophage-Related Genes in Ulcerative Colitis Using Weighted Coexpression Network Analysis and Machine Learning

Ulcerative colitis (UC) is an inflammatory bowel disease of unknown cause that typically affects the colon and rectum. Innate intestinal immunity, including macrophages, plays a significant role in the pathological development of UC. Using the CIBERSORT algorithm, we observed elevated levels of 22 types of immune cell infiltrates, as well as increased M1 and decreased M2 macrophages in UC compared to normal colonic mucosa. Weighted gene coexpression network analysis (WGCNA) was used to identify modules associated with macrophages and UC, resulting in the identification of 52 macrophage-related genes (MRGs) that were enriched in macrophages at single-cell resolution. Consensus clustering based on these 52 MRGs divided the integrated UC cohorts into three subtypes. Machine learning algorithms were used to identify ectonucleotide pyrophosphatase/phosphodiesterase 1 (ENPP1), sodium- and chloride-dependent neutral and basic amino acid transporter B(0+) (SLC6A14), and 3-hydroxy-3-methylglutaryl-CoA synthase 2 (HMGCS2) in the training set, and their diagnostic value was validated in independent validation sets. Gene set enrichment analysis (GSEA) and gene set variation analysis (GSVA) revealed the main biological effects, and that interleukin-17 was one of several signaling pathways enriched by the three genes. We also constructed a competitive endogenous RNA (CeRNA) network reflecting a potential posttranscriptional regulatory mechanism. Expression of diagnostic markers was validated in vivo and in biospecimens, and our immunohistochemistry (IHC) results confirmed that HMGCS2 gradually decreased during the transformation of UC to colorectal cancer. In conclusion, ENPP1, SLC6A14, and HMGCS2 are associated with macrophages and the progression of UC pathogenesis and have good diagnostic value for patients with UC.

COVPRIG robustly predicts the overall survival of IDH wild-type glioblastoma and highlights METTL1+ neural-progenitor-like tumor cell in driving unfavorable outcome

BACKGROUND

Accurately predicting the outcome of isocitrate dehydrogenase (IDH) wild-type glioblastoma (GBM) remains hitherto challenging. This study aims to Construct and Validate a Robust Prognostic Model for IDH wild-type GBM (COVPRIG) for the prediction of overall survival using a novel metric, gene-gene (G × G) interaction, and explore molecular and cellular underpinnings.

METHODS

Univariate and multivariate Cox regression of four independent trans-ethnic cohorts containing a total of 800 samples. Prediction efficacy was comprehensively evaluated and compared with previous models by a systematic literature review. The molecular underpinnings of COVPRIG were elucidated by integrated analysis of bulk-tumor and single-cell based datasets.

RESULTS

Using a Cox-ph model-based method, six of the 93,961 G × G interactions were screened to form an optimal combination which, together with age, comprised the COVPRIG model. COVPRIG was designed for RNA-seq and microarray, respectively, and effectively identified patients at high risk of mortality. The predictive performance of COVPRIG was satisfactory, with area under the curve (AUC) ranging from 0.56 (CGGA693, RNA-seq, 6-month survival) to 0.79 (TCGA RNAseq, 18-month survival), which can be further validated by decision curves. Nomograms were constructed for individual risk prediction for RNA-seq and microarray-based cohorts, respectively. Besides, the prognostic significance of COVPRIG was also validated in GBM including the IDH mutant samples. Notably, COVPRIG was comprehensively evaluated and externally validated, and a systemic review disclosed that COVPRIG outperformed current validated models with an integrated discrimination improvement (IDI) of 6-16%. Moreover, integrative bioinformatics analysis predicted an essential role of METTL1+ neural-progenitor-like (NPC-like) malignant cell in driving unfavorable outcome.

CONCLUSION

This study provided a powerful tool for the outcome prediction for IDH wild-type GBM, and preliminary molecular underpinnings for future research.

ATHENA: an independently validated autophagy-related epigenetic prognostic prediction model of head and neck squamous cell carcinoma

The majority of these existing prognostic models of head and neck squamous cell carcinoma (HNSCC) have unsatisfactory prediction accuracy since they solely utilize demographic and clinical information. Leveraged by autophagy-related epigenetic biomarkers, we aim to develop a better prognostic prediction model of HNSCC incorporating CpG probes with either main effects or gene-gene interactions. Based on DNA methylation data from three independent cohorts, we applied a 3-D analysis strategy to develop An independently validated auTophagy-related epigenetic prognostic prediction model of HEad and Neck squamous cell carcinomA (ATHENA). Compared to prediction models with only demographic and clinical information, ATHENA has substantially improved discriminative ability, prediction accuracy and more clinical net benefits, and shows robustness in different subpopulations, as well as external populations. Besides, epigenetic score of ATHENA is significantly associated with tumor immune microenvironment, tumor-infiltrating immune cell abundances, immune checkpoints, somatic mutation and immunity-related drugs. Taken together these results, ATHENA has the demonstrated feasibility and utility of predicting HNSCC survival ( http://bigdata.njmu.edu.cn/ATHENA/ ).

Redox-associated messenger RNAs identify novel prognostic values and influence the tumor immune microenvironment of lung adenocarcinoma

Background: An imbalance of redox homeostasis participates in tumorigenesis, proliferation, and metastasis, which results from the production of reactive oxygen species (ROS). However, the biological mechanism and prognostic significance of redox-associated messenger RNAs (ramRNAs) in lung adenocarcinoma (LUAD) still remain unclear. Methods: Transcriptional profiles and clinicopathological information were retrieved from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) of LUAD patients. A total of 31 overlapped ramRNAs were determined, and patients were separated into three subtypes by unsupervised consensus clustering. Biological functions and tumor immune-infiltrating levels were analyzed, and then, differentially expressed genes (DEGs) were identified. The TCGA cohort was divided into a training set and an internal validation set at a ratio of 6:4. Least absolute shrinkage and selection operator regression were used to compute the risk score and determine the risk cutoff in the training set. Both TCGA and GEO cohort were distinguished into a high-risk or low-risk group at the median cutoff, and then, relationships of mutation characteristics, tumor stemness, immune differences, and drug sensitivity were investigated. Results: Five optimal signatures (ANLN, HLA-DQA1, RHOV, TLR2, and TYMS) were selected. Patients in the high-risk group had poorer prognosis, higher tumor mutational burden, overexpression of PD-L1, and lower immune dysfunction and exclusion score compared with the low-risk group. Cisplatin, docetaxel, and gemcitabine had significantly lower IC₅₀ in the high-risk group. Conclusion: This study constructed a novel predictive signature of LUAD based on redox-associated genes. Risk score based on ramRNAs served as a promising biomarker for prognosis, TME, and anti-cancer therapies of LUAD.

A trans-omics assessment of gene-gene interaction in early-stage NSCLC

Epigenome-wide gene-gene (G × G) interactions associated with non-small-cell lung cancer (NSCLC) survival may provide insights into molecular mechanisms and therapeutic targets. Hence, we proposed a three-step analytic strategy to identify significant and robust G × G interactions that are relevant to NSCLC survival. In the first step, among 49 billion pairs of DNA methylation probes, we identified 175 775 G × G interactions with P_Bonferroni  ≤ 0.05 in the discovery phase of epigenomic analysis; among them, 15 534 were confirmed with P ≤ 0.05 in the validation phase. In the second step, we further performed a functional validation for these G × G interactions at the gene expression level by way of a two-phase (discovery and validation) transcriptomic analysis, and confirmed 25 significant G × G interactions enriched in the 6p21.33 and 6p22.1 regions. In the third step, we identified two G × G interactions using the trans-omics analysis, which had significant (P ≤ 0.05) epigenetic cis-regulation of transcription and robust G × G interactions at both the epigenetic and transcriptional levels. These interactions were cg14391855 × cg23937960 (β_interaction  = 0.018, P = 1.87 × 10^-12 ), which mapped to RELA × HLA-G (β_interaction  = 0.218, P = 8.82 × 10^-11 ) and cg08872738 × cg27077312 (β_interaction  = -0.010, P = 1.16 × 10^-11 ), which mapped to TUBA1B × TOMM40 (β_interaction =-0.250, P = 3.83 × 10^-10 ). A trans-omics mediation analysis revealed that 20.3% of epigenetic effects on NSCLC survival were significantly (P = 0.034) mediated through transcriptional expression. These statistically significant trans-omics G × G interactions can also discriminate patients with high risk of mortality. In summary, we identified two G × G interactions at both the epigenetic and transcriptional levels, and our findings may provide potential clues for precision treatment of NSCLC.

Differential methylation in EGLN1 associates with blood oxygen saturation and plasma protein levels in high-altitude pulmonary edema

BACKGROUND

High-altitude (HA, 2500 m) hypoxic exposure evokes a multitude of physiological processes. The hypoxia-sensing genes though influence transcriptional output in disease susceptibility; the exact regulatory mechanisms remain undetermined in high-altitude pulmonary edema (HAPE). Here, we investigated the differential DNA methylation distribution in the two genes encoding the oxygen-sensing HIF-prolyl hydroxylases, prolyl hydroxylase domain protein 2 (PHD2) and factor inhibiting HIF-1α and the consequent contributions to the HAPE pathophysiology.

METHODS

Deep sequencing of the sodium bisulfite converted DNA segments of the two genes, Egl nine homolog 1 (EGLN1) and Hypoxia Inducible Factor 1 Subunit Alpha Inhibitor (HIF1AN), was conducted to analyze the differential methylation distribution in three study groups, namely HAPE-patients (HAPE-p), HAPE-free sojourners (HAPE-f) and healthy HA natives (HLs). HAPE-p and HAPE-f were permanent residents of low altitude (< 200 m) of North India who traveled to Leh (3500 m), India, and were recruited through Sonam Norboo Memorial (SNM) hospital, Leh. HLs were permanent residents of altitudes at and above 3500 m. In addition to the high resolution, bisulfite converted DNA sequencing, gene expression of EGLN1 and HIF1AN and their plasma protein levels were estimated.

RESULTS

A significantly lower methylation distribution of CpG sites was observed in EGLN1 and higher in HIF1AN (P < 0.01) in HAPE-p compared to the two control groups, HAPE-f and HLs. Of note, differential methylation distribution of a few CpG sites, 231,556,748, 231,556,804, 231,556,881, 231,557,317 and 231,557,329, in EGLN1 were significantly associated with the risk of HAPE (OR = 4.79-10.29; P = 0.048-004). Overall, the methylation percentage in EGLN1 correlated with upregulated plasma PHD2 levels (R = - 0.36, P = 0.002) and decreased peripheral blood oxygen saturation (SpO₂) levels (R = 0.34, P = 0.004). We also identified a few regulatory SNPs in the DNA methylation region of EGLN1 covering chr1:231,556,683-231,558,443 suggestive of the functional role of differential methylation distribution of these CpG sites in the regulation of the genes and consequently in the HIF-1α signaling.

CONCLUSIONS

Significantly lower methylation distribution in EGLN1 and the consequent physiological influences annotated its functional epigenetic relevance in the HAPE pathophysiology.

A Novel Hypoxia-Related Gene Signature with Strong Predicting Ability in Non-Small-Cell Lung Cancer Identified by Comprehensive Profiling

Background

Non-small-cell lung cancer (NSCLC) is the most common malignant tumor among males and females worldwide. Hypoxia is a typical feature of the tumor microenvironment, and it affects cancer development. Circular RNAs (circRNAs) have been reported to sponge miRNAs to regulate target gene expression and play an essential role in tumorigenesis and progression. This study is aimed at identifying whether circRNAs could be used as the diagnostic biomarkers for NSCLC.

Methods

The heterogeneity of samples in this study was assessed by principal component analysis (PCA). Furthermore, the Gene Expression Omnibus (GEO) database was normalized by the affy R package. We further screened the differentially expressed genes (DEGs) and differentially expressed circular RNAs (DEcircRNAs) using the DEseq2 R package. Moreover, we analyzed the Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment of DEGs using the cluster profile R package. Besides, the Gene Set Enrichment Analysis (GSEA) was used to identify the biological function of DEGs. The interaction between DEGs and the competing endogenous RNAs (ceRNA) network was detected using STRING and visualized using Cytoscape. Starbase predicted the miRNAs of target hub genes, and miRanda predicted the target miRNAs of circRNAs. The RNA-seq profiler and clinical information were downloaded from The Cancer Genome Atlas (TCGA) database. Then, the variables were assessed by the univariate and multivariate Cox proportional hazard regression models. Significant variables in the univariate Cox proportional hazard regression model were included in the multivariate Cox proportional hazard regression model to analyze the association between the variables of clinical features. Furthermore, the overall survival of variables was determined by the Kaplan-Meier survival curve, and the time-dependent receiver operating characteristic (ROC) curve analysis was used to calculate and validate the risk score in NSCLC patients. Moreover, predictive nomograms were constructed and used to predict the prognostic features between the high-risk and low-risk score groups.

Results

We screened a total of 2039 DEGs, including 1293 upregulated DEGs and 746 downregulated DEGs in hypoxia-treated A549 cells. A549 cells treated with hypoxia had a total of 70 DEcircRNAs, including 21 upregulated and 49 downregulated DEcircRNAs, compared to A549 cells treated with normoxia. The upregulated genes were significantly enriched in 284 GO terms and 42 KEGG pathways, while the downregulated genes were significantly enriched in 184 GO terms and 25 KEGG pathways. Moreover, the function analysis by GSEA showed enrichment in the enzyme-linked receptor protein signaling pathway, hypoxia-inducible factor- (HIF-) 1 signaling pathway, and G protein-coupled receptor (GPCR) downstream signaling. Furthermore, six hub modules and 10 hub genes, CDC45, EXO1, PLK1, RFC4, CCNB1, CDC6, MCM10, DLGAP5, AURKA, and POLE2, were identified. The ceRNA network was constructed, and it consisted of 4 circRNAs, 14 miRNAs, and 38 mRNAs. The ROC curve was constructed and calculated. The area under the curve (AUC) value was 0.62, and the optimal threshold was 0.28. Based on the optimal threshold, the patients were divided into the high-risk score and low-risk score groups. The survival rate in the high-risk score group was lower than that in the low-risk score group. The expression of SERPINE1, STC2, and LPCAT1; clinical stage; and age of the patient were significantly correlated with the high-risk score. Moreover, nomograms were established based on the risk factors in multivariate analysis, and the median survival time, 3-year survival probability, and 5-year survival were possibly predicted according to nomograms.

Conclusion

The ceRNA network associated with NSCLC was identified, and the hub genes, circRNAs, might act as the potential biomarkers for NSCLC.

APOLLO: An accurate and independently validated prediction model of lower-grade gliomas overall survival and a comparative study of model performance

BACKGROUND

Virtually few accurate and robust prediction models of lower-grade gliomas (LGG) survival exist that may aid physicians in making clinical decisions. We aimed to develop a prognostic prediction model of LGG by incorporating demographic, clinical and transcriptional biomarkers with either main effects or gene-gene interactions.

METHODS

Based on gene expression profiles of 1,420 LGG patients from six independent cohorts comprising both European and Asian populations, we proposed a 3-D analysis strategy to develop and validate an Accurate Prediction mOdel of Lower-grade gLiomas Overall survival (APOLLO). We further conducted decision curve analysis to assess the net benefit (NB) of identifying true positives and the net reduction (NR) of unnecessary interventions. Finally, we compared the performance of APOLLO and the existing prediction models by the first systematic review.

FINDINGS

APOLLO possessed an excellent discriminative ability to identify patients at high mortality risk. Compared to those with less than the 20th percentile of APOLLO risk score, patients with more than the 90th percentile of APOLLO risk score had significantly worse overall survival (HR=54·18, 95% CI: 34·73-84·52, P=2·66 × 10-69). Further, APOLLO can accurately predict both 36- and 60-month survival in six independent cohorts with a pooled AUC36-month=0·901 (95% CI: 0·879-0·923), AUC60-month=0·843 (95% CI: 0·815-0·871) and C-index=0·818 (95% CI: 0·800-0·835). Moreover, APOLLO offered an effective screening strategy for detecting LGG patients susceptible to death (NB36-month=0·166, NR36-month=40·1% and NB60-month=0·258, NR60-month=19·2%). The systematic comparisons revealed APOLLO outperformed the existing models in accuracy and robustness.

INTERPRETATION

APOLLO has the demonstrated feasibility and utility of predicting LGG survival (http://bigdata.njmu.edu.cn/APOLLO).

FUNDING

National Key Research and Development Program of China (2016YFE0204900); Natural Science Foundation of Jiangsu Province (BK20191354); National Natural Science Foundation of China (81973142 and 82103946); China Postdoctoral Science Foundation (2020M681671); National Institutes of Health (CA209414, CA249096, CA092824 and ES000002).

A two-phase comprehensive NSCLC prognostic study identifies lncRNAs with significant main effect and interaction

Long noncoding RNA (lncRNA) are involved in regulating physiological behaviors for various malignant tumors, including non-small-cell lung cancer (NSCLC). However, few studies comprehensively evaluated both lncRNA-lncRNA interaction effects and main effects of lncRNA on overall survival of NSCLC. Hence, we performed a two-phase designed study of lncRNA expression in tumor tissues using 604 NSCLC patients from The Cancer Genome Atlas as the discovery phase and 839 patients from Gene Expression Omnibus as the validation phase. In the discovery phase, we adopted a two-step strategy, Screening before Testing, for dimension reduction and signal detection. These candidate lncRNAs first screened out by the weighted random forest (Ranger), were then tested through the Cox proportional hazards model adjusted for covariates. Significant lncRNAs with either type of effects aforementioned were carried forward into the validation phase to confirm their significances again. As a result, in the discovery phase, 19 lncRNAs were identified by Ranger, among which five lncRNAs and one pair of lncRNA-lncRNA interaction exhibited significant effects (FDR-q ≤ 0.05) main and interaction effects on NSCLC survival, respectively, through Cox model. After the independent validation, we finally observed that one lncRNA (ENSG00000227403.1) with main effect was robustly associated with NSCLC prognosis (HR_discovery = 0.90, P = 1.20 × 10^-3; HR_validation = 0.94, P = 4.11 × 10^-3) and one pair of lncRNAs (ENSG00000267121.4 and ENSG00000272369.1) had significant interaction effect on NSCLC survival (HR_discovery = 1.12, P = 3.07 × 10^-4; HR_validation = 1.11, P = 0.0397). Our comprehensive NSCLC prognostic study of lncRNA provided population-level evidence for further functional study.

Epigenome-wide three-way interaction study identifies a complex pattern between TRIM27, KIAA0226, and smoking associated with overall survival of early-stage NSCLC

The interaction between DNA methylation of tripartite motif containing 27 (cg05293407_TRIM27 ) and smoking has previously been identified to reveal histologically heterogeneous effects of TRIM27 DNA methylation on early-stage non-small-cell lung cancer (NSCLC) survival. However, to understand the complex mechanisms underlying NSCLC progression, we searched three-way interactions. A two-phase study was adopted to identify three-way interactions in the form of pack-year of smoking (number of cigarettes smoked per day × number of years smoked) × cg05293407_TRIM27  × epigenome-wide DNA methylation CpG probe. Two CpG probes were identified with FDR-q ≤ 0.05 in the discovery phase and P ≤ 0.05 in the validation phase: cg00060500_KIAA0226 and cg17479956_EXT2 . Compared to a prediction model with only clinical information, the model added 42 significant three-way interactions using a looser criterion (discovery: FDR-q ≤ 0.10, validation: P ≤ 0.05) had substantially improved the area under the receiver operating characteristic curve (AUC) of the prognostic prediction model for both 3-year and 5-year survival. Our research identified the complex interaction effects among multiple environment and epigenetic factors, and provided therapeutic target for NSCLC patients.

Identification of Prognostic Biomarkers for Bladder Cancer Based on DNA Methylation Profile

Background: DNA methylation is an important epigenetic modification, which plays an important role in regulating gene expression at the transcriptional level. In tumor research, it has been found that the change of DNA methylation leads to the abnormality of gene structure and function, which can provide early warning for tumorigenesis. Our study aims to explore the relationship between the occurrence and development of tumor and the level of DNA methylation. Moreover, this study will provide a set of prognostic biomarkers, which can more accurately predict the survival and health of patients after treatment.

Methods: Datasets of bladder cancer patients and control samples were collected from TCGA database, differential analysis was employed to obtain genes with differential DNA methylation levels between tumor samples and normal samples. Then the protein-protein interaction network was constructed, and the potential tumor markers were further obtained by extracting Hub genes from subnet. Cox proportional hazard regression model and survival analysis were used to construct the prognostic model and screen out the prognostic markers of bladder cancer, so as to provide reference for tumor prognosis monitoring and improvement of treatment plan.

Results: In this study, we found that DNA methylation was indeed related with the occurrence of bladder cancer. Genes with differential DNA methylation could serve as potential biomarkers for bladder cancer. Through univariate and multivariate Cox proportional hazard regression analysis, we concluded that FASLG and PRKCZ can be used as prognostic biomarkers for bladder cancer. Patients can be classified into high or low risk group by using this two-gene prognostic model. By detecting the methylation status of these genes, we can evaluate the survival of patients.

Conclusion: The analysis in our study indicates that the methylation status of tumor-related genes can be used as prognostic biomarkers of bladder cancer.

Construction of diagnostic and subtyping models for renal cell carcinoma by genome-wide DNA methylation profiles

Background

Renal cell carcinoma (RCC) is one of the most common urological cancers and has a poor prognosis. RCC is classified into several subtypes, among which kidney renal clear cell carcinoma (KIRC) and kidney renal papillary cell carcinoma (KIRP) are the two most common subtypes. Due to the lack of adequate screening and comparative analysis of RCC subtypes, effective diagnosis and treatment strategies have not yet been achieved.

Methods

In this study, 450K methylation array data were collected from The Cancer Genome Atlas (TCGA). The ‘limma moderated t-test’ and LASSO were used to construct diagnostic and subtyping models, and survival analysis was conducted online by GEPIA.

Results

We built a model with 15 methylation sites, which showed high diagnostic and subtyping performance in specificity and sensitivity. At the same time, for potential clinical usability, we calculated the diagnostic and subtyping scores to classify RCC from normal tissue and distinguish the different RCC subtypes. Additionally, the CpG sites were mapped to their corresponding genes, which could also be used to predict the prognosis of RCC.

Conclusions

Different methylation sites can be used as diagnostic and subtyping markers that are specific to RCC and RCC subtypes (KIRC and KIRP) with high sensitivity and accuracy.

Genome-wide analysis of methylation CpG sites in gene promoters identified four pairs of CpGs-mRNAs associated with lung adenocarcinoma prognosis

BACKGROUND

Activation of oncogenes through promoter hypomethylation and silencing of tumor suppressor genes induced by promoter hypermethylation played essential roles in the progression of lung adenocarcinoma (LUAD). This study aimed to identify the LUAD prognostic CpG sites and the regulated genes which contributed to LUAD progression.

METHODS

Methylation profiles from TCGA and GSE60645 were used to screen the differentially methylated CpGs. Then, the Log-rank test was adopted to identify LUAD prognosis-associated CpGs. Differential gene expression and survival analyses were further performed to suggest the roles of methylation-driven genes in LUAD prognosis. Finally, models and nomograms were constructed to predict the prognosis of LUAD.

RESULTS

A total of 1891 CpGs at gene promoters were differentially methylated. Among them, 54 CpGs were significantly associated with LUAD prognosis. Nine of them showed significant correlations with the expression of four genes (CCDC181, CFTR, PPP1R16B, MYEOV). CCDC181, CFTR and PPP1R16B were aberrantly down-regulated in LUAD, while MYEOV was up-regulated. All of them were significantly associated with LUAD prognosis. The LASSO regression analysis indicated that tumor stages, cg09181792, cg16998150, cg22779330 and PPP1R16B were promising prognostic factors. The AUC (area under the curve) of the model containing the clinical predictors was 0.643. The combination of CpGs and PPP1R16B with clinical variables significantly improved the predictive efficiency with an AUC of 0.714 (P = 0.036).

CONCLUSION

This study identified four pairs of promoter CpGs and genes that were significantly associated with LUAD prognosis. The integration of CpGs methylation and gene expression showed better predictive ability for LUAD prognosis.

Comparative transcriptomic analysis of the brain in Takifugu rubripes shows its tolerance to acute hypoxia

Hypoxia in water that caused by reduced levels of oxygen occurred frequently, due to the complex aquatic environment. Hypoxia tolerance for fish depends on a complete set of coping mechanisms such as oxygen perception and gene-protein interaction regulation. The present study examined the short-term effects of hypoxia on the brain in Takifugu rubripes. We sequenced the transcriptomes of the brain in T. rubripes to study their response mechanism to acute hypoxia. A total of 167 genes were differentially expressed in the brain of T. rubripes after exposed to acute hypoxia. Gene ontology and KEGG enrichment analysis indicated that hypoxia could cause metabolic and neurological changes, showing the clues of their adaptation to acute hypoxia. As the most complex and important organ, the brain of T. rubripes might be able to create a self-protection mechanism to resist or reduce damage caused by acute hypoxia stress.

Can Targeting Hypoxia-Mediated Acidification of the Bone Marrow Microenvironment Kill Myeloma Tumor Cells?

Multiple myeloma (MM) is an incurable cancer arising from malignant plasma cells that engraft in the bone marrow (BM). The physiology of these cancer cells within the BM microenvironment (TME) plays a critical role in MM development. These processes may be similar to what has been observed in the TME of other (non-hematological) solid tumors. It has been long reported that within the BM, vascular endothelial growth factor (VEGF), increased angiogenesis and microvessel density, and activation of hypoxia-induced transcription factors (HIF) are correlated with MM progression but despite a great deal of effort and some modest preclinical success the overall clinical efficacy of using anti-angiogenic and hypoxia-targeting strategies, has been limited. This review will explore the hypothesis that the TME of MM engrafted in the BM is distinctly different from non-hematological-derived solid tumors calling into question how effective these strategies may be against MM. We further identify other hypoxia-mediated effectors, such as hypoxia-mediated acidification of the TME, oxygen-dependent metabolic changes, and the generation of reactive oxygen species (ROS), that may prove to be more effective targets against MM.

Hypoxia in Lung Cancer Management: A Translational Approach

Simple Summary

Hypoxia is a common feature of lung cancers. Nonetheless, no guidelines have been established to integrate hypoxia-associated biomarkers in patient management. Here, we discuss the current knowledge and provide translational novel considerations regarding its clinical detection and targeting to improve the outcome of patients with non-small-cell lung carcinoma of all stages.

Abstract

Lung cancer represents the first cause of death by cancer worldwide and remains a challenging public health issue. Hypoxia, as a relevant biomarker, has raised high expectations for clinical practice. Here, we review clinical and pathological features related to hypoxic lung tumours. Secondly, we expound on the main current techniques to evaluate hypoxic status in NSCLC focusing on positive emission tomography. We present existing alternative experimental approaches such as the examination of circulating markers and highlight the interest in non-invasive markers. Finally, we evaluate the relevance of investigating hypoxia in lung cancer management as a companion biomarker at various lung cancer stages. Hypoxia could support the identification of patients with higher risks of NSCLC. Moreover, the presence of hypoxia in treated tumours could help clinicians predict a worse prognosis for patients with resected NSCLC and may help identify patients who would benefit potentially from adjuvant therapies. Globally, the large quantity of translational data incites experimental and clinical studies to implement the characterisation of hypoxia in clinical NSCLC management.

YKT6, as a potential predictor of prognosis and immunotherapy response for oral squamous cell carcinoma, is related to cell invasion, metastasis, and CD8+ T cell infiltration

Metastasis and immune suppression account for the poor prognosis of oral squamous cell carcinoma (OSCC). YKT6 is a member of the soluble NSF attachment protein receptor (SNARE) family, and the effect of YKT6 in OSCC remains elusive. The purpose of this study was to explore promising prognostic and immune therapeutic candidate biomarkers for OSCC and to understand the expression pattern, prognostic value, immune effects, and biological functions of YKT6. Genes correlated with tumor metastasis and CD8 + T cell levels were identified by weighted gene coexpression network analysis (WGCNA). Next, YKT6 was analyzed through differential expression, prognostic and machine learning analyses. The molecular and immune characteristics of YKT6 were analyzed in independent cohorts, clinical specimens, and in vitro. In addition, we investigated the role of YKT6 at the pan-cancer level. The results suggested that the red module in WGCNA, as a hub module, was associated with lymph node (LN) metastasis and CD8 + T cell infiltration. Upregulation of YKT6 was found in OSCC and linked to adverse prognosis. A nomogram model containing YKT6 expression and tumor stage was constructed for clinical practice. The aggressive and immune-inhibitory phenotypes showed YKT6 overexpression, and the effect of YKT6 on OSCC cell invasion and metastasis in vitro was observed. Moreover, the low expression of YKT6 was correlated with high CD8 + T cell levels and potential immunotherapy response in OSCC. Similar results were found at the pan-cancer level. In total, YKT6 is a promising candidate biomarker for prognosis, molecular, and immune characteristics in OSCC.

Epigenetic-smoking interaction reveals histologically heterogeneous effects of TRIM27 DNA methylation on overall survival among early-stage NSCLC patients

Tripartite motif containing 27 (TRIM27) is highly expressed in lung cancer, including non-small-cell lung cancer (NSCLC). Here, we profiled DNA methylation of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) tumours from 613 early-stage NSCLC patients and evaluated associations between CpG methylation of TRIM27 and overall survival. Significant CpG probes were confirmed in 617 samples from The Cancer Genome Atlas. The methylation of the CpG probe cg05293407_TRIM27 was significantly associated with overall survival in patients with LUSC (HR = 1.65, 95% CI: 1.30-2.09, P = 4.52 × 10^-5), but not in patients with LUAD (HR = 1.08, 95% CI: 0.87-1.33, P = 0.493). As incidence of LUSC is associated with higher smoking intensity compared to LUAD, we investigated whether smoking intensity impacted on the prognostic effect of cg05293407_TRIM27 methylation in NSCLC. LUSC patients had a higher average pack-year of smoking (37.49_LUAD vs 54.79_LUSC, P = 1.03 × 10^-19) and included a higher proportion of current smokers than LUAD patients (28.24%_LUAD vs 34.09%_LUSC, P = 0.037). cg05293407_TRIM27 was significantly associated with overall survival only in NSCLC patients with medium-high pack-year of smoking (HR = 1.58, 95% CI: 1.26-1.96, P = 5.25 × 10^-5). We conclude that cg05293407_TRIM27 methylation is a potential predictor of LUSC prognosis, and smoking intensity may impact on its prognostic value across the various types of NSCLC.

Epigenome-wide gene-age interaction analysis reveals reversed effects of PRODH DNA methylation on survival between young and elderly early-stage NSCLC patients

DNA methylation changes during aging, but it remains unclear whether the effect of DNA methylation on lung cancer survival varies with age. Such an effect could decrease prediction accuracy and treatment efficacy. We performed a methylation–age interaction analysis using 1,230 early-stage lung adenocarcinoma patients from five cohorts. A Cox proportional hazards model was used to investigate lung adenocarcinoma and squamous cell carcinoma patients for methylation–age interactions, which were further confirmed in a validation phase. We identified one adenocarcinoma-specific CpG probe, cg14326354_PRODH, with effects significantly modified by age (HR_interaction = 0.989; 95% CI: 0.986–0.994; P = 9.18×10–7). The effect of low methylation was reversed for young and elderly patients categorized by the boundary of 95% CI standard (HR_young = 2.44; 95% CI: 1.26–4.72; P = 8.34×10-3; HR_elderly = 0.58; 95% CI: 0.42–0.82; P = 1.67×10-3). Moreover, there was an antagonistic interaction between low cg14326354_PRODH methylation and elderly age (HR_interaction = 0.21; 95% CI: 0.11–0.40; P = 2.20×10−6). In summary, low methylation of cg14326354PRODH might benefit survival of elderly lung adenocarcinoma patients, providing new insight to age-specific prediction and potential drug targeting.

Prognostic value of AKAP13 methylation and expression in lung squamous cell carcinoma

Aim: This study aimed to analyze the prognostic value and clinical significance of AKAP13 mRNA expression and AKAP13 methylation in lung squamous cell carcinoma (LUSC). Materials & methods: The mRNA expression and methylation of AKAP13 data of LUSC patients were downloaded from the Broad GDAC Firehose database and analyzed. Results: AKAP13 mRNA expression was downregulated and methylation was upregulated in LUSC tissue. Three CpG sites of AKAP13 were associated with overall survival. Combination of AKAP13 mRNA and methylation revealed 11 CpG sites associated with overall survival of LUSC patients. AKAP13 mRNA expression was associated with distant metastasis of LUSC, no associations were found between methylation status of CpG sites and clinical features. Conclusion: AKAP13 mRNA and its methylated CpG sites are potential prognostic indicators in LUSC patients.

Independent Validation of Early-Stage Non-Small Cell Lung Cancer Prognostic Scores Incorporating Epigenetic and Transcriptional Biomarkers With Gene-Gene Interactions and Main Effects

Background

DNA methylation and gene expression are promising biomarkers of various cancers, including non-small cell lung cancer (NSCLC). Besides the main effects of biomarkers, the progression of complex diseases is also influenced by gene-gene (G×G) interactions.

Research Question

Would screening the functional capacity of biomarkers on the basis of main effects or interactions, using multiomics data, improve the accuracy of cancer prognosis?

Study Design and Methods

Biomarker screening and model validation were used to construct and validate a prognostic prediction model. NSCLC prognosis-associated biomarkers were identified on the basis of either their main effects or interactions with two types of omics data. A prognostic score incorporating epigenetic and transcriptional biomarkers, as well as clinical information, was independently validated.

Results

Twenty-six pairs of biomarkers with G×G interactions and two biomarkers with main effects were significantly associated with NSCLC survival. Compared with a model using clinical information only, the accuracy of the epigenetic and transcriptional biomarker-based prognostic model, measured by area under the receiver operating characteristic curve (AUC), increased by 35.38% (95% CI, 27.09%-42.17%; P = 5.10 × 10^–17) and 34.85% (95% CI, 26.33%-41.87%; P = 2.52 × 10^–18) for 3- and 5-year survival, respectively, which exhibited a superior predictive ability for NSCLC survival (AUC_{3 year}, 0.88 [95% CI, 0.83-0.93]; and AUC_{5 year}, 0.89 [95% CI, 0.83-0.93]) in an independent Cancer Genome Atlas population. G×G interactions contributed a 65.2% and 91.3% increase in prediction accuracy for 3- and 5-year survival, respectively.

Interpretation

The integration of epigenetic and transcriptional biomarkers with main effects and G×G interactions significantly improves the accuracy of prognostic prediction of early-stage NSCLC survival.

Methylation in Lung Cancer: A Brief Review

Despite the introduction of low-dose computed tomography (LDCT) and implementation of lung cancer screening programs, lung cancer still maintains the leading cause of cancer-specific death all around the world in terms of morbidity and mortality. Many studies demonstrated that the methylation status of selected genes may act as prognostic biomarkers for lung cancer patients. Recently, the development of high-throughput sequencing for methylation would help researchers better understand the role of methylation in the tumorigenicity or metastasis of lung cancer. This chapter reviews the progress of DNA methylation in lung cancer.

Trans-omics biomarker model improves prognostic prediction accuracy for early-stage lung adenocarcinoma

Limited studies have focused on developing prognostic models with trans-omics biomarkers for early-stage lung adenocarcinoma (LUAD). We performed integrative analysis of clinical information, DNA methylation, and gene expression data using 825 early-stage LUAD patients from 5 cohorts. Ranger algorithm was used to screen prognosis-associated biomarkers, which were confirmed with a validation phase. Clinical and biomarker information was fused using an iCluster plus algorithm, which significantly distinguished patients into high- and low-mortality risk groups (Pdiscovery = 0.01 and Pvalidation = 2.71×10-3). Further, potential functional DNA methylation-gene expression-overall survival pathways were evaluated by causal mediation analysis. The effect of DNA methylation level on LUAD survival was significantly mediated through gene expression level. By adding DNA methylation and gene expression biomarkers to a model of only clinical data, the AUCs of the trans-omics model improved by 18.3% (to 87.2%) and 16.4% (to 85.3%) in discovery and validation phases, respectively. Further, concordance index of the nomogram was 0.81 and 0.77 in discovery and validation phases, respectively. Based on systematic review of published literatures, our model was superior to all existing models for early-stage LUAD. In summary, our trans-omics model may help physicians accurately identify patients with high mortality risk.

SIPA1L3 methylation modifies the benefit of smoking cessation on lung adenocarcinoma survival: an epigenomic-smoking interaction analysis

Smoking cessation prolongs survival and decreases mortality of patients with non-small-cell lung cancer (NSCLC). In addition, epigenetic alterations of some genes are associated with survival. However, potential interactions between smoking cessation and epigenetics have not been assessed. Here, we conducted an epigenome-wide interaction analysis between DNA methylation and smoking cessation on NSCLC survival. We used a two-stage study design to identify DNA methylation-smoking cessation interactions that affect overall survival for early-stage NSCLC. The discovery phase contained NSCLC patients from Harvard, Spain, Norway, and Sweden. A histology-stratified Cox proportional hazards model adjusted for age, sex, clinical stage, and study center was used to test DNA methylation-smoking cessation interaction terms. Interactions with false discovery rate-q ≤ 0.05 were further confirmed in a validation phase using The Cancer Genome Atlas database. Histology-specific interactions were identified by stratification analysis in lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) patients. We identified one CpG probe (cg02268510SIPA1L3 ) that significantly and exclusively modified the effect of smoking cessation on survival in LUAD patients [hazard ratio (HR)interaction  = 1.12; 95% confidence interval (CI): 1.07-1.16; P = 4.30 × 10-7 ]. Further, the effect of smoking cessation on early-stage LUAD survival varied across patients with different methylation levels of cg02268510SIPA1L3 . Smoking cessation only benefited LUAD patients with low methylation (HR = 0.53; 95% CI: 0.34-0.82; P = 4.61 × 10-3 ) rather than medium or high methylation (HR = 1.21; 95% CI: 0.86-1.70; P = 0.266) of cg02268510SIPA1L3 . Moreover, there was an antagonistic interaction between elevated methylation of cg02268510SIPA1L3 and smoking cessation (HRinteraction  = 2.1835; 95% CI: 1.27-3.74; P = 4.46 × 10-3 ). In summary, smoking cessation benefited survival of LUAD patients with low methylation at cg02268510SIPA1L3 . The results have implications for not only smoking cessation after diagnosis, but also possible methylation-specific drug targeting.