Identification of LBX2 as a novel causal gene of lung adenocarcinoma

Histone modification-linked prognostic model for ovarian cancer reveals LBX2 as a novel growth promoter

Ovarian cancer (OC) is a deadly disease with limited treatment options and poor overall survival rates. This study aimed to investigate the role of histone modification-related genes in predicting the prognosis of OC patients. Transcriptome data from multiple cohorts, including bulk RNA-Seq data and single-cell scRNA-Seq data, were collected. Gene set enrichment analysis was used to identify enriched gene sets in the histone modification pathway. Differentially expressed genes (DEGs) between histone modification-high and histone modification-low groups were identified using Lasso regression. A prognostic model was constructed using five selected prognostic genes from the DEGs in the TCGA-OV cohort. The study found enrichment of gene sets in the histone modification pathway and identified five prognostic genes associated with OC prognosis. The constructed risk score model based on histone modification-related genes was correlated with immune infiltration of T cells and M1 macrophages. Mutations are more prevalent in the high-risk group compared to the low-risk group. Several drugs were screened against the model genes. Through in vitro experiments, we confirmed the expression patterns of the model genes. LBX2 facilitates the proliferation of OC. Histone modification-related genes have the potential to serve as biomarkers for predicting OC prognosis. Targeting these genes may lead to the development of more effective therapies for OC. Additionally, LBX2 represents a novel cell proliferation promoter in OC carcinogenesis.

Dynamic Network Construction for Identifying Early Warning Signals Based On a Data-Driven Approach: Early Diagnosis Biomarker Discovery for Gastric Cancer

During the development of complex diseases, there is a critical transition from one status to another at a tipping point, which can be an early indicator of disease deterioration. To effectively enhance the performance of early risk identification, a novel dynamic network construction algorithm for identifying early warning signals based on a data-driven approach (EWS-DDA) was proposed. In EWS-DDA, the shrunken centroid was introduced to measure dynamic expression changes in assumed pathway reactions during the progression of complex disease for network construction and to define early warning signals by means of a data-driven approach. We applied EWS-DDA to perform a comprehensive analysis of gene expression profiles of gastric cancer (GC) from The Cancer Genome Atlas database and the Gene Expression Omnibus database. Six crucial genes were selected as potential biomarkers for the early diagnosis of GC. The experimental results of statistical analysis and biological analysis suggested that the six genes play important roles in GC occurrence and development. Then, EWS-DDA was compared with other state-of-the-art network methods to validate its performance. The theoretical analysis and comparison results suggested that EWS-DDA has great potential for a more complete presentation of disease deterioration and effective extraction of early warning information.

A network-based dynamic criterion for identifying prediction and early diagnosis biomarkers of complex diseases

Lung adenocarcinoma (LUAD) seriously threatens human health and generally results from dysfunction of relevant module molecules, which dynamically change with time and conditions, rather than that of an individual molecule. In this study, a novel network construction algorithm for identifying early warning network signals (IEWNS) is proposed for improving the performance of LUAD early diagnosis. To this end, we theoretically derived a dynamic criterion, namely, the relationship of variation (RV), to construct dynamic networks. RV infers correlation [Formula: see text] statistics to measure dynamic changes in molecular relationships during the process of disease development. Based on the dynamic networks constructed by IEWNS, network warning signals used to represent the occurrence of LUAD deterioration can be defined without human intervention. IEWNS was employed to perform a comprehensive analysis of gene expression profiles of LUAD from The Cancer Genome Atlas (TCGA) database and the Gene Expression Omnibus (GEO) database. The experimental results suggest that the potential biomarkers selected by IEWNS can facilitate a better understanding of pathogenetic mechanisms and help to achieve effective early diagnosis of LUAD. In conclusion, IEWNS provides novel insight into the initiation and progression of LUAD and helps to define prospective biomarkers for assessing disease deterioration.

Identification of a New Prediction Model for Bladder Cancer Related to Immune Functions and Chemotherapy Using Gene Sets of Biological Processes

Background. Biological processes serve crucial functions in the initiation and development of cancer. Therefore, we constructed and validated a model for bladder cancer (BLCA) with good predictive power for immunity, prognosis, and therapy. Methods. Using the expression of the gene sets based on biological processes, BLCA patients were divided into three clusters by consensus cluster analysis. By performing LASSO regression analysis twice, key genes were selected, and the biological processes-related genes’ (BPRG) score was calculated. Differences in immune infiltration, tumor microenvironment, tumor mutation burden, immunotherapy, and sensitivity towards chemotherapy were analyzed between two groups divided by BPRG score. Results. Good accuracy was observed for the three clusters. They showed different prognoses and levels of immune cell infiltration. The selected key genes were mainly enriched in immune-related pathways. The high-BPRG score group was related to poor prognosis, higher immune cell infiltration, interstitial scores, and increased tumor mutation. Moreover, the effects of immunotherapy were good, and those of chemotherapy were poor. Conclusion. Overall, key genes may be involved in various complex immune regulation processes. Therefore, the quantification and verification of the BPRG score are expected to facilitate the understanding of the immunosuppressive microenvironment in BLCA and guide the choice of chemotherapeutic drugs and immunotherapeutic regimens and help predict the prognoses of patients with BLCA.

Genetic Variants of CLPP and M1AP Are Associated With Risk of Non-Small Cell Lung Cancer

Background

Single nucleotide polymorphisms (SNPs) are often associated with distinct phenotypes in cancer. The present study investigated associations of cancer risk and outcomes with SNPs discovered by whole exome sequencing of normal lung tissue DNA of 15 non-small cell lung cancer (NSCLC) patients, 10 early stage and 5 advanced stage.

Methods

DNA extracted from normal lung tissue of the 15 NSCLC patients was subjected to whole genome amplification and sequencing and analyzed for the occurrence of SNPs. The association of SNPs with the risk of lung cancer and survival was surveyed using the OncoArray study dataset of 85,716 patients (29,266 cases and 56,450 cancer-free controls) and the Prostate, Lung, Colorectal and Ovarian study subset of 1,175 lung cancer patients.

Results

We identified 4 SNPs exclusive to the 5 patients with advanced stage NSCLC: rs10420388 and rs10418574 in the CLPP gene, and rs11126435 and rs2021725 in the M1AP gene. The variant alleles G of SNP rs10420388 and A of SNP rs10418574 in the CLPP gene were associated with increased risk of squamous cell carcinoma (OR = 1.07 and 1.07; P = 0.013 and 0.016, respectively). The variant allele T of SNP rs11126435 in the M1AP gene was associated with decreased risk of adenocarcinoma (OR = 0.95; P = 0.027). There was no significant association of these SNPs with the overall survival of lung cancer patients (P > 0.05).

Conclusions

SNPs identified in the CLPP and M1AP genes may be useful in risk prediction models for lung cancer. The previously established association of the CLPP gene with cancer progression lends relevance to our findings.