Difference of copy number variation in blood of patients with lung cancer

Exploring the role of N-acetyltransferases in diseases: a focus on N-acetyltransferase 9 in neurodegeneration

Acetyltransferases, required to transfer an acetyl group on protein are highly conserved proteins that play a crucial role in development and disease. Protein acetylation is a common post-translational modification pivotal to basic cellular processes. Close to 80%-90% of proteins are acetylated during translation, which is an irreversible process that affects protein structure, function, life, and localization. In this review, we have discussed the various N-acetyltransferases present in humans, their function, and how they might play a role in diseases. Furthermore, we have focused on N-acetyltransferase 9 and its role in microtubule stability. We have shed light on how N-acetyltransferase 9 and acetylation of proteins can potentially play a role in neurodegenerative diseases. We have specifically discussed the N-acetyltransferase 9-acetylation independent function and regulation of c-Jun N-terminal kinase signaling and microtubule stability during development and neurodegeneration.

A new insight into the impact of copy number variations on cell cycle deregulation of luminal-type breast cancer

Breast cancer is the most prevalent neoplasm in women. ER+ (Luminal subtype), representing over 70% of breast tumors, is a genetically diverse group. Structural and Numerical-Chromosomal instability initiates tumor development and is recognized as the primary driver of genetic alteration in luminal breast tumors. Genomic instability refers to the increased tendency of cancer cells to accumulate genomic alterations during cell proliferation. The cell cycle check-point response to constant and stable genomic alterations in tumor cells drives this process. The impact of CNV patterns and aneuploidies in cell cycle and proliferation perturbation has recently been highlighted by scientists in Luminal breast tumors. The impact of chromosomal instability on cancer therapy and prognosis is not a new concept. Still, the degree of emerging genomic instability leads to prognosis alteration following cell cycle deregulation by chromosomal instability could be predicted by CNVs-based reclassification of breast tumors. In this review, we try to explain the effect of CIN in the cell cycle that ended with genomic instability and altered prognosis and the impact of CIN in decision-making for a therapy strategy for patients with luminal breast cancer.

HapCNV: A Comprehensive Framework for CNV Detection in Low-input DNA Sequencing Data

Copy number variants (CNVs) are prevalent in both diploid and haploid genomes, with the latter containing a single copy of each gene. Studying CNVs in genomes from single or few cells is significantly advancing our knowledge in human disorders and disease susceptibility. Low-input including low-cell and single-cell sequencing data for haploid and diploid organisms generally displays shallow and highly non-uniform read counts resulting from the whole genome amplification steps that introduce amplification biases. In addition, haploid organisms typically possess relatively short genomes and require a higher degree of DNA amplification compared to diploid organisms. However, most CNV detection methods are specifically developed for diploid genomes without specific consideration of effects on haploid genomes. Challenges also reside in reference samples or normal controls which are used to provide baseline signals for defining copy number losses or gains. In traditional methods, references are usually pre-specified from cells that are assumed to be normal or disease-free. However, the use of pre-defined reference cells can bias results if common CNVs are present. Here, we present the development of a comprehensive statistical framework for data normalization and CNV detection in haploid single- or low-cell DNA sequencing data called HapCNV. The prominent advancement is the construction of a novel genomic location specific pseudo-reference that selects unbiased references using a preliminary cell clustering method. This approach effectively preserves common CNVs. Using simulations, we demonstrated that HapCNV outperformed existing methods by generating more accurate CNV detection, especially for short CNVs. Superior performance of HapCNV was also validated in detecting known CNVs in a real P. falciparum parasite dataset. In conclusion, HapCNV provides a novel and useful approach for CNV detection in haploid low-input sequencing datasets, with easy applicability to diploids.

Building a Risk Scoring Model for ARDS in Lung Adenocarcinoma Patients Using Machine Learning Algorithms

Lung adenocarcinoma (LUAD), the predominant form of non-small-cell lung cancer, is frequently complicated by acute respiratory distress syndrome (ARDS), which increases mortality risks. Investigating the prognostic implications of ARDS-related genes in LUAD is crucial for improving clinical outcomes. Data from TCGA, GEO and GTEx were used to identify 276 ARDS-related genes in LUAD via differential expression analysis. Univariate Cox regression, consensus clustering and machine learning algorithms were used to develop a prognostic risk scoring model. Functional enrichment, immune infiltration analyses, copy number variations and mutational burdens were examined, and the results were validated at the single-cell level. ARDS-related genes significantly impact the prognosis of LUAD patients. A machine learning-based risk scoring model accurately predicted survival rates. Functional enrichment and immune infiltration analyses revealed that these genes are primarily involved in cell cycle regulation and immune cell infiltration. Single-cell expression data supported these findings, and the assessments of copy number variations and mutational burdens highlighted distinct genetic characteristics. This study establishes the prognostic relevance of ARDS-associated genes in LUAD and provides potential biomarkers for personalized therapy and prognosis. Future studies will validate these findings and explore their clinical applications.

Specific Deletions of Chromosomes 3p, 5q, 13q, and 21q among Patients with G2 Grade of Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) leads as a primary cause of cancer-related premature mortality in Western populations. This study leverages cutting-edge gene-expression-profiling technologies to perform an in-depth molecular characterization of NSCLC specimens, with the objective of uncovering tumor-specific genomic alterations. By employing DNA microarray analysis, our research aims to refine the classification of NSCLC for early detection, guide molecular-targeted treatment approaches, enhance prognostication, and broaden the scientific understanding of the disease's biology. We identified widespread genomic abnormalities in our samples, including the recurrent loss of chromosomal regions 3p, 5q, 13q, and 21q and the gain of 12p. Furthermore, utilizing Metascape for bioinformatic analysis revealed critical biological pathways disrupted in NSCLC, offering promising leads for novel therapeutic interventions.

NKX2‑1 copy number alterations are associated with oncogenic, immunological and prognostic remodeling in non‑small cell lung cancer

NK2 homeobox 1 (NKX2-1) copy number alterations (CNAs) are frequently observed in lung cancer. However, little is known about the complete landscape of focal alterations in NKX2-1 copy number (CN), their clinical significance and their therapeutic implications in non-small cell lung cancer (NSCLC). The correlations between NKX2-1 expression and EGFR driver mutations and programmed death ligand 1 (PD-L1) co-expression were studied using immunohistochemistry and PCR from the tumors of recruited Filipino patients (n=45). Clinical features of NSCLC with NKX2-1 CNAs were resolved at the tumor and clonal levels using the molecular profiles of patients with lung adenocarcinoma and lung squamous cell carcinoma from The Cancer Genome Atlas (n=1,130), and deconvoluted single-cell RNA-seq data from the Bivona project (n=1,654), respectively. Despite a significant and positive correlation between expression and CN (r=0.264; P<0.001), NKX2-1 CNAs exerted a stronger influence on the combined EGFR and PD-L1 status of NSCLC tumors than expression. NKX2-1 CN gain was prognostic of favorable survival (P=0.018) and a better response to targeted therapy. NKX2-1 CN loss predicted a worse survival (P=0.041). Mutational architecture in the Y-chromosome differentiated the two prognostic groups. There were 19,941 synonymous mutations and 1,408 genome-wide CN perturbations associated with NKX2-1 CNAs. Tumors with NKX2-1 CN gain expressed lymphocyte markers more heterogeneously than those with CN loss. Higher expression of tumor-infiltrating lymphocyte gene signatures in CN gain was prognostic of longer disease-free survival (P=0.005). Tumors with NKX2-1 CN gain had higher B-cell (P<0.001) and total T-cell estimates (P=0.003). NKX2-1 CN loss was associated with immunologically colder tumors due to higher M2 macrophage infiltrates (P=0.011) and higher expression of immune checkpoint proteins, CD274 (P=0.025), VTCN1 (P<0.001) and LGALS9 (P=0.002). In conclusion, NKX2-1 CNAs are associated with tumors that exhibit clinically diverse characteristics, and with unique oncogenic, immunological and prognostic signatures.

Evaluating the effectiveness of pre-operative diagnosis of ovarian cancer using minimally invasive liquid biopsies by combining serum human epididymis protein 4 and cell-free DNA in patients with an ovarian mass

OBJECTIVE

To assess the feasibility of scalable, objective, and minimally invasive liquid biopsy-derived biomarkers such as cell-free DNA copy number profiles, human epididymis protein 4 (HE4), and cancer antigen 125 (CA125) for pre-operative risk assessment of early-stage ovarian cancer in a clinically representative and diagnostically challenging population and to compare the performance of these biomarkers with the Risk of Malignancy Index (RMI).

METHODS

In this case-control study, we included 100 patients with an ovarian mass clinically suspected to be early-stage ovarian cancer. Of these 100 patients, 50 were confirmed to have a malignant mass (cases) and 50 had a benign mass (controls). Using WisecondorX, an algorithm used extensively in non-invasive prenatal testing, we calculated the benign-calibrated copy number profile abnormality score. This score represents how different a sample is from benign controls based on copy number profiles. We combined this score with HE4 serum concentration to separate cases and controls.

RESULTS

Combining the benign-calibrated copy number profile abnormality score with HE4, we obtained a model with a significantly higher sensitivity (42% vs 0%; p<0.002) at 99% specificity as compared with the RMI that is currently employed in clinical practice. Investigating performance in subgroups, we observed especially large differences in the advanced stage and non-high-grade serous ovarian cancer groups.

CONCLUSION

This study demonstrates that cell-free DNA can be successfully employed to perform pre-operative risk of malignancy assessment for ovarian masses; however, results warrant validation in a more extensive clinical study.

Identification of gene variation feature for targeted therapy of non-small cell lung cancer through combined method of DNA and RNA sequencing

Next generation sequencing (NGS) is typically used to reveal tumor gene variation feature for targeted therapy of various types of human cancers, including non-small cell lung cancer (NSCLC). Here, we report the role and potential applicable value of combining DNA and RNA sequencing in gene variation detection in NSCLC. 386 NSCLC patients with stage II-IV were enrolled and detected using NGS sequencing of DNA and RNA panels that covered all well-documented target driver genes from the Chinese Society of Clinical Oncology (CSCO). The rate of epidermal growth factor receptor (EGFR) single nucleotide variation (SNV)/indel, mesenchymal-epithelial transition factor (MET) copy number variation (CNV) and anaplastic lymphoma kinase (ALK) fusion were 52.1%, 4.1% and 6.0% in the NSCLC cohort. The landscapes of SNV/indel, CNV and gene fusion in the cohort were depicted as well. Meanwhile, we assessed detection efficacy of DNA and RNA sequencing in gene fusion. Detected number and types of gene fusion using the RNA sequencing were better than those using the DNA sequencing. Gene fusion with intergenic region was only detected by DNA sequencing and MET exon 14 skipping (METΔex14) was more easily identified by RNA sequencing. Finally, we investigated clinical correlations of SNV/indel/CNV/fusion with clinicopathologic features in the NSCLC cohort. Taken together, RNA sequencing significantly complements deficiency of DNA sequencing for gene fusion, which cooperatively presents comprehensive and reliable gene variation features and facilitate the identification of potential drug targets for NSCLC patients.

Integration of genomics and transcriptomics highlights the crucial role of chromosome 5 open reading frame 34 in various human malignancies

Although some data suggest that chromosome 5 open reading frame 34 (C5orf34) plays a pivotal part in the onset and disease progression of various cancers, there is no pan-cancer investigation of C5orf34 at present. This study sought to establish the predictive importance of C5orf34 in a variety of human malignancies and to understand its fundamental immunological function. In our research, we applied a combination of several bioinformatics techniques and basic experiments to investigate the differential expression of C5orf34, and its relationship with prognosis, methylation, single nucleotide variant, clinical characteristics, microsatellite instability, tumor mutational burden, copy number variation, and immune cell infiltration of several cancers from the database that is publicly available with the aim of identifying the potential prognostic markers. In this study we found that C5orf34 expression differed significantly among cancers types, according to the findings. The expression level of C5orf34 is markedly increased in the majority of malignancies when compared to normal tissues, which is significantly correlated with an unfavorable prognosis of patients. Immunohistochemical staining confirmed the findings that C5orf34 expression was remarkably up-regulated in a variety of gynecologic cancers. Moreover, C5orf34 expression was shown to be correlated with the clinical features of patients. C5orf34 was also found to be expressed with genes that code for the major immune suppressors, chemokines, immune activators, chemokine receptors, and histocompatibility complex. Finally, our study shows that C5orf34 has the potential to be employed as a prognostic biomarker. Moreover, it might regulate the immune microenvironment in a variety of malignancies.

NATs at a glance

Most proteins receive an acetyl group at the N terminus while in their nascency as the result of modification by co-translationally acting N-terminal acetyltransferases (NATs). The N-terminal acetyl group can influence several aspects of protein functionality. From studies of NAT-lacking cells, it is evident that several cellular processes are affected by this modification. More recently, an increasing number of genetic cases have demonstrated that N-terminal acetylation has crucial roles in human physiology and pathology. In this Cell Science at a Glance and the accompanying poster, we provide an overview of the human NAT enzymes and their properties, substrate coverage, cellular roles and connections to human disease.

Mapping Multi-factor-mediated Chromatin Interactions to Assess Dysregulation of Lung Cancer-related Genes

Studies on the lung cancer genome are indispensable for developing a cure for lung cancer. Whole-genome resequencing, genome-wide association studies, and transcriptome sequencing have greatly improved our understanding of the cancer genome. However, dysregulation of long-range chromatin interactions in lung cancer remains poorly described. To better understand the three-dimensional (3D) genomic interaction features of the lung cancer genome, we used the A549 cell line as a model system and generated high-resolution chromatin interactions associated with RNA polymerase II (RNAPII), CCCTC-binding factor (CTCF), enhancer of zeste homolog 2 (EZH2), and histone 3 lysine 27 trimethylation (H3K27me3) using long-read chromatin interaction analysis by paired-end tag sequencing (ChIA-PET). Analysis showed that EZH2/H3K27me3-mediated interactions further repressed target genes, either through loops or domains, and their distributions along the genome were distinct from and complementary to those associated with RNAPII. Cancer-related genes were highly enriched with chromatin interactions, and chromatin interactions specific to the A549 cell line were associated with oncogenes and tumor suppressor genes, such as additional repressive interactions on FOXO4 and promoter-promoter interactions between NF1 and RNF135. Knockout of an anchor associated with chromatin interactions reversed the dysregulation of cancer-related genes, suggesting that chromatin interactions are essential for proper expression of lung cancer-related genes. These findings demonstrate the 3D landscape and gene regulatory relationships of the lung cancer genome.

Genome-Wide Copy Number Variant and High-Throughput Transcriptomics Analyses of Placental Tissues Underscore Persisting Child Susceptibility in At-Risk Pregnancies Cleared in Standard Genetic Testing

Several studies have shown that children from pregnancies with estimated first-trimester risk based on fetal nuchal translucency thickness and abnormal maternal serum pregnancy protein and hormone levels maintain a higher likelihood of adverse outcomes, even if initial testing for known genetic conditions is negative. We used the Finnish InTraUterine cohort (ITU), which is a comprehensively characterized perinatal cohort consisting of 943 mothers and their babies followed throughout pregnancy and 18 months postnatally, including mothers shortlisted for prenatal genetic testing but cleared for major aneuploidies (cases: n = 544, 57.7%) and control pregnancies (n = 399, 42.3%). Using genome-wide genotyping and RNA sequencing of first-trimester and term placental tissue, combined with medical information from registry data and maternal self-report data, we investigated potential negative medical outcomes and genetic susceptibility to disease and their correlates in placenta gene expression. Case mothers did not present with higher levels of depression, perceived stress, or anxiety during pregnancy. Case children were significantly diagnosed more often with congenital malformations of the circulatory system (4.12 (95% CI [1.22−13.93]) higher hazard) and presented with significantly more copy number duplications as compared to controls (burden analysis, based on all copy number variants (CNVs) with at most 10% frequency, 823 called duplications in 297 cases versus 626 called duplications in 277 controls, p = 0.01). Fifteen genes showed differential gene expression (FDR < 0.1) in association with congenital malformations in first-trimester but not term placenta. These were significantly enriched for genes associated with placental dysfunction. In spite of normal routine follow-up prenatal testing results in early pregnancy, case children presented with an increased likelihood of negative outcomes, which should prompt vigilance in follow-up during pregnancy and after birth.

CRIA: An Interactive Gene Selection Algorithm for Cancers Prediction Based on Copy Number Variations

Genomic copy number variations (CNVs) are among the most important structural variations of genes found to be related to the risk of individual cancer and therefore they can be utilized to provide a clue to the research on the formation and progression of cancer. In this paper, an improved computational gene selection algorithm called CRIA (correlation-redundancy and interaction analysis based on gene selection algorithm) is introduced to screen genes that are closely related to cancer from the whole genome based on the value of gene CNVs. The CRIA algorithm mainly consists of two parts. Firstly, the main effect feature is selected out from the original feature set that has the largest correlation with the class label. Secondly, after the analysis involving correlation, redundancy and interaction for each feature in the candidate feature set, we choose the feature that maximizes the value of the custom selection criterion and add it into the selected feature set and then remove it from the candidate feature set in each selection round. Based on the real datasets, CRIA selects the top 200 genes to predict the type of cancer. The experiments' results of our research show that, compared with the state-of-the-art related methods, the CRIA algorithm can extract the key features of CNVs and a better classification performance can be achieved based on them. In addition, the interpretable genes highly related to cancer can be known, which may provide new clues at the genetic level for the treatment of the cancer.