APOB Genotypes and CDH13 Haplotypes in the Cholesterol-Related Pathway Genes Predict Non-Small Cell Lung Cancer Survival

Population-enriched innate immune variants may identify candidate gene targets at the intersection of cancer and cardio-metabolic disease

Both cancer and cardio-metabolic disease disparities exist among specific populations in the US. For example, African Americans experience the highest rates of breast and prostate cancer mortality and the highest incidence of obesity. Native and Hispanic Americans experience the highest rates of liver cancer mortality. At the same time, Pacific Islanders have the highest death rate attributed to type 2 diabetes (T2D), and Asian Americans experience the highest incidence of non-alcoholic fatty liver disease (NAFLD) and cancers induced by infectious agents. Notably, the pathologic progression of both cancer and cardio-metabolic diseases involves innate immunity and mechanisms of inflammation. Innate immunity in individuals is established through genetic inheritance and external stimuli to respond to environmental threats and stresses such as pathogen exposure. Further, individual genomes contain characteristic genetic markers associated with one or more geographic ancestries (ethnic groups), including protective innate immune genetic programming optimized for survival in their corresponding ancestral environment(s). This perspective explores evidence related to our working hypothesis that genetic variations in innate immune genes, particularly those that are commonly found but unevenly distributed between populations, are associated with disparities between populations in both cancer and cardio-metabolic diseases. Identifying conventional and unconventional innate immune genes that fit this profile may provide critical insights into the underlying mechanisms that connect these two families of complex diseases and offer novel targets for precision-based treatment of cancer and/or cardio-metabolic disease.

APOB is a potential prognostic biomarker in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is significantly associated with adverse prognostic outcomes. The development and progression of different types of human tumors are significantly influenced by APOB. Nevertheless, the significance and pathomechanisms of APOB in HCC have not been conclusively determined. We assessed APOB expression levels in HCC using three publicly available databases of TIMER2.0, UALCAN and Human Protein Atlas. To identify the biological function of APOB, we conducted enrichment analysis via LinkedOmics. Moreover, UALCAN was employed to assess the relationship between APOB expression and clinicopathological features among HCC patients. Additionally, the Kaplan-Meier plotter was utilized to investigate the prognostic relevance of APOB in HCC. To explore potential regulatory ncRNAs that could bind to APOB, we utilized StarBase and GEPIA. Furthermore, the correlation between APOB expression and immune cell infiltration, as well as immune checkpoint genes, was investigated using Spearman's correlation analysis in TISIDB, GEPIA, and TIMER2.0. The findings of our investigation showed a notable decrease in the expression levels of APOB among individuals diagnosed with HCC. Moreover, a noteworthy correlation was observed between the expression of APOB and immune checkpoint genes, alongside the occurrence of immune cell infiltration. The levels of APOB expression in HCC tissues also showed correlations with various clinicopathological features. According to Cox regression analysis, decreased APOB expression emerged as a potential autonomous predictor for OS, RFS, DSS, and PFS among HCC patients. Furthermore, we identified six potential pathways associated with non-coding RNA (ncRNA) as the most promising pathway for APOB in HCC. Our results illuminate the possible involvement of APOB in HCC and offer understanding into its governing mechanisms and medical importance.

Identification of genetic susceptibility in preterm newborns with bronchopulmonary dysplasia by whole-exome sequencing: BIVM gene may play a role

Bronchopulmonary dysplasia (BPD) is a common chronic respiratory disease in preterm infants caused by multifactorial etiology. Genetic factors are involved in the occurrence of BPD, but studies have found that candidate genes have poor reproducibility and are influenced by ethnic heterogeneity; therefore, more exploration is still needed. We performed whole-exon sequencing in 34 preterm infants with BPD and 32 non-BPD control neonates. The data were analyzed and interpreted by Fisher difference comparison, PLINK and eQTL association analysis, KEGG and GO enrichment analysis, STRING tool, Cytoscape software, ProtParam tool, HOPE online software, and GEOR2 analysis on NCBI GEO dataset. BPD has a highly heterogeneity in different populations, and we found 35 genes overlapped with previous whole-exon sequencing studies, such as APOB gene. Arterial and epithelial cell development and energy metabolism pathways affect BPD. In this study, 24 key genes were identified, and BIVM rs3825519 mutation leads to prolonged assisted ventilation in patients with BPD. A novel DDAH1 mutation site (NM_012137: exon1: c.89 T > G: p.L30R) was found in 9 BPD patients.

CONCLUSION

BIVM gene rs3825519 mutation may play a role in the pathogenesis of BPD by affecting cilia movement, and the DDAH1 and APOB genes mutations may have a pathogenic role in BPD.

WHAT IS KNOWN

• Genetic factors are involved in the occurrence of bronchopulmonary dysplasia. • The candidate genes have poor reproducibility and are influenced by ethnic heterogeneity, therefore, more exploration is still needed.

WHAT IS NEW

• We identified the role of susceptible SNPs in BPD in Shenzhen, China, and identified 24 key genes that influence the pathogenesis of BPD, and also found 35 genes overlapped with previous whole exon sequencing studies, such as APOB gene. • We found that BIVM and DDAH1 genes may play a pathogenic role in the pathogenesis of BPD.

Glycoproteomics revealed novel N-glycosylation biomarkers for early diagnosis of lung adenocarcinoma cancers

Circulating biomarkers play important roles in diagnosis of malignant tumors. N-glycosylation is an important post-translation patter and obviously affect biological behaviors of malignant tumor cells. However, the role of N-glycosylation sites in early diagnosis of tumors still remains further investigation. In this study, plasma from 20 lung adenocarcinoma (LUAD), which were all classified as stage I, as well as 20 normal controls (NL) were labeled and screened by mass spectrometry (MS). Total 39 differential N-glycosylation sites were detected in LUAD, 17 were up-regulated and 22 were down-regulated. In all differential sites, ITGB3-680 showed highest potential in LUAD which showed 99.2% AUC, 95.0% SP and 95.0% SN. Besides, APOB-1523 (AUC: 89.0%, SP: 95.0%, SN: 70.0%), APOB-2982 (AUC: 86.8%, SP: 95.0%, SN: 45.0%) and LPAL2-101 (AUC: 81.1%, SP: 95.0%, SN: 47.4%) also acted as candidate biomarkers in LUAD. Combination analysis was then performed by random forest model, all samples were divided into training group (16 cases) and testing group (4 cases) and conducted by feature selection, machine learning, integrated model of classifier and model evaluation. And the results indicated that combination of differential sites could reach 100% AUC in both training and testing group. Taken together, our study revealed multiple N-glycosylation sites which could be applied as candidate biomarkers for early diagnosis diagnosis of LUAD.

Analysis of multiple databases identifies crucial genes correlated with prognosis of hepatocellular carcinoma

Despite advancements made in the therapeutic strategies on hepatocellular carcinoma (HCC), the survival rate of HCC patient is not satisfactory enough. Therefore, there is an urgent need for the valuable prognostic biomarkers in HCC therapy. In this study, we aimed to screen hub genes correlated with prognosis of HCC via multiple databases. 117 HCC-related genes were obtained from the intersection of the four databases. We subsequently identify 10 hub genes (JUN, IL10, CD34, MTOR, PTGS2, PTPRC, SELE, CSF1, APOB, MUC1) from PPI network by Cytoscape software analysis. Significant differential expression of hub genes between HCC tissues and adjacent tissues were observed in UALCAN, HCCDB and HPA databases. These hub genes were significantly associated with immune cell infiltrations and immune checkpoints. The hub genes were correlated with clinical parameters and survival probability of HCC patients. 147 potential targeted therapeutic drugs for HCC were identified through the DGIdb database. These hub genes could be used as novel prognostic biomarkers for HCC therapy.

Multi-Omics Analysis of Cancer Cell Lines with High/Low Ferroptosis Scores and Development of a Ferroptosis-Related Model for Multiple Cancer Types

Background: Ferroptosis is a newly identified regulated cell death characterized by iron-dependent lipid peroxidation and subsequent membrane oxidative damage, which has been implicated in multiple types of cancers. The multi-omics differences between cancer cell lines with high/low ferroptosis scores remain to be elucidated.

Methods and Materials: We used RNA-seq gene expression, gene mutation, miRNA expression, metabolites, copy number variation, and drug sensitivity data of cancer cell lines from DEPMAP to detect multi-omics differences associated with ferroptosis. Based on the gene expression data of cancer cell lines, we performed LASSO-Logistic regression analysis to build a ferroptosis-related model. Lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), esophageal cancer (ESCA), bladder cancer (BLCA), cervical cancer (CESC), and head and neck cancer (HNSC) patients from the TCGA database were used as validation cohorts to test the efficacy of this model.

Results: After stratifying the cancer cell lines into high score (HS) and low score (LS) groups according to the median of ferroptosis scores generated by gene set variation analysis, we found that IC50 of 66 agents such as oxaliplatin (p < 0.001) were significantly different, among which 65 were higher in the HS group. 851 genes such as KEAP1 and NRAS were differentially muted between the two groups. Differentially expressed genes, miRNAs and metabolites were also detected—multiple items such as IL17F (logFC = 6.58, p < 0.001) differed between the two groups. Unlike the TCGA data generated by bulk RNA-seq, the gene expression data in DEPMAP are from pure cancer cells, so it could better reflect the traits of tumors in cancer patients. Thus, we built a 15-signature model (AUC = 0.878) based on the gene expression data of cancer cell lines. The validation cohorts demonstrated a higher mutational rate of NFE2L2 and higher expression levels of 12 ferroptosis-related genes in HS groups.

Conclusion: This article systemically analyzed multi-omics differences between cancer cell lines with high/low ferroptosis scores and a ferroptosis-related model was developed for multiple cancer types. Our findings could improve our understanding of the role of ferroptosis in cancer and provide new insight into treatment for malignant tumors.