Tissue Proteogenomic Landscape Reveals the Role of Uncharacterized SEL1L3 in Progression and Immunotherapy Response in Lung Adenocarcinoma

Identification of Candidate Genes and eQTLs Related to Porcine Reproductive Function

Expression quantitative trait locus (eQTL) mapping is an effective tool for identifying genetic variations that regulate gene expression. An increasing number of studies suggested that SNPs associated with complex traits in farm animals are considered as expression quantitative trait loci. Identifying eQTLs associated with gene expression levels in the endometrium helps to unravel the regulatory mechanisms of genes related to reproductive functions in this tissue and provides molecular markers for the genetic improvement of high-fertility sow breeding. In this study, 218 RNA-seq data from pig endometrial tissue were used for eQTL analysis to identify genetic variants regulating gene expression. Additionally, weighted gene co-expression network analysis (WGCNA) was performed to identify hub genes involved in reproductive functions. The eQTL analysis identified 34,876 significant cis-eQTLs regulating the expression of 5632 genes (FDR ≤ 0.05), and 90 hub genes were identified by WGCNA analysis. By integrating eQTL and WGCNA results, 14 candidate genes and 16 fine-mapped cis-eQTLs were identified, including FRK, ARMC3, SLC35F3, TMEM72, FFAR4, SOWAHA, PSPH, FMO5, HPN, FUT2, RAP1GAP, C6orf52, SEL1L3, and CLGN, which were involved in the physiological processes of reproduction in sows through hormone regulation, cell adhesion, and amino acid and lipid metabolism. These eQTLs regulate the high expression of candidate genes in the endometrium, thereby affecting reproductive-related physiological functions. These findings enhance our understanding of the genetic basis of reproductive traits and provide valuable genetic markers for marker-assisted selection (MAS), which can be applied to improve sow fecundity and optimize breeding strategies for high reproductive performance.

Excavation of gene markers associated with pancreatic ductal adenocarcinoma based on interrelationships of gene expression

Pancreatic ductal adenocarcinoma (PDAC) accounts for 95% of all pancreatic cancer cases, posing grave challenges to its diagnosis and treatment. Timely diagnosis is pivotal for improving patient survival, necessitating the discovery of precise biomarkers. An innovative approach was introduced to identify gene markers for precision PDAC detection. The core idea of our method is to discover gene pairs that display consistent opposite relative expression and differential co-expression patterns between PDAC and normal samples. Reversal gene pair analysis and differential partial correlation analysis were performed to determine reversal differential partial correlation (RDC) gene pairs. Using incremental feature selection, the authors refined the selected gene set and constructed a machine-learning model for PDAC recognition. As a result, the approach identified 10 RDC gene pairs. And the model could achieve a remarkable accuracy of 96.1% during cross-validation, surpassing gene expression-based models. The experiment on independent validation data confirmed the model's performance. Enrichment analysis revealed the involvement of these genes in essential biological processes and shed light on their potential roles in PDAC pathogenesis. Overall, the findings highlight the potential of these 10 RDC gene pairs as effective diagnostic markers for early PDAC detection, bringing hope for improving patient prognosis and survival.

Prediction of Treatment Recommendations Via Ensemble Machine Learning Algorithms for Non-Small Cell Lung Cancer Patients in Personalized Medicine

Objectives

The primary goal of this research is to develop treatment-related genomic predictive markers for non-small cell lung cancer by integrating various machine learning algorithms that recommends near-optimal individualized patient treatment for chemotherapy in an effort to maximize efficacy or minimize treatment-related toxicity. This research can contribute toward developing a more refined, accurate and effective therapy accounting for specific patient needs.

Methods

To accomplish our research goal, we implement ensemble learning algorithms, bagging with regularized Cox regression models and nonparametric tree-based models via Random Survival Forests. A comprehensive meta-database was compiled from the NCBI Gene Expression Omnibus data repository for lung cancer patients to capture and utilize complex genomic patterns that can predict treatment outcomes more accurately.

Results

The developed novel prediction algorithm demonstrates the ability to support complex clinical decision-making processes in the treatment of NSCLC. It effectively addresses patient heterogeneity, offering predictions that are both refined and personalized in improving the precision of chemotherapy regimens prescribed to the eligible patients.

Conclusion

This research should contribute substantial advancement of cancer treatments by improving the accuracy and efficacy of chemotherapy treatments for a targeted group of patients who need the right treatment. The integration of complex machine learning techniques with genomic data holds substantial potential to transform current cancer treatment paradigms by providing robust support in clinical decision-making.

Hyper-N-glycosylated SEL1L3 as auto-antigenic B-cell receptor target of primary vitreoretinal lymphomas

Primary vitreoretinal lymphoma (PVRL) is a rare subtype of DLBCL and can progress into primary central nervous system lymphoma (PCNSL). To investigate the role of chronic antigenic stimulation in PVRL, we cloned and expressed B-cell receptors (BCR) from PVRL patients and tested for binding against human auto-antigens. SEL1L3, a protein with multiple glycosylation sites, was identified as the BCR target in 3/20 PVRL cases. SEL1L3 induces proliferation and BCR pathway activation in aggressive lymphoma cell lines. Moreover, SEL1L3 conjugated to a toxin killed exclusively lymphoma cells with respective BCR-reactivity. Western Blot analysis indicates the occurrence of hyper-N-glycosylation of SEL1L3 at aa 527 in PVRL patients with SEL1L3-reactive BCRs. The BCR of a PVRL patient with serum antibodies against SEL1L3 was cloned from a vitreous body biopsy at diagnosis and of a systemic manifestation at relapse. VH4-04*07 was used in both lymphoma manifestations with highly conserved CDR3 regions. Both BCRs showed binding to SEL1L3, suggesting continued dependence of lymphoma cells on antigen stimulation. These results indicate an important role of antigenic stimulation by post-translationally modified auto-antigens in the genesis of PVRL. They also provide the basis for a new treatment approach targeting unique lymphoma BCRs with ultimate specificity.

Multi-omics and artificial intelligence predict clinical outcomes of immunotherapy in non-small cell lung cancer patients

In recent years, various types of immunotherapy, particularly the use of immune checkpoint inhibitors targeting programmed cell death 1 or programmed death ligand 1 (PD-L1), have revolutionized the management and prognosis of non-small cell lung cancer. PD-L1 is frequently used as a biomarker for predicting the likely benefit of immunotherapy for patients. However, some patients receiving immunotherapy have high response rates despite having low levels of PD-L1. Therefore, the identification of this group of patients is extremely important to improve prognosis. The tumor microenvironment contains tumor, stromal, and infiltrating immune cells with its composition differing significantly within tumors, between tumors, and between individuals. The omics approach aims to provide a comprehensive assessment of each patient through high-throughput extracted features, promising a more comprehensive characterization of this complex ecosystem. However, features identified by high-throughput methods are complex and present analytical challenges to clinicians and data scientists. It is thus feasible that artificial intelligence could assist in the identification of features that are beyond human discernment as well as in the performance of repetitive tasks. In this paper, we review the prediction of immunotherapy efficacy by different biomarkers (genomic, transcriptomic, proteomic, microbiomic, and radiomic), together with the use of artificial intelligence and the challenges and future directions of these fields.

SEL1L3 as a link molecular between renal cell carcinoma and atherosclerosis based on bioinformatics analysis and experimental verification

BACKGROUND

Renal cancer, the most common type of kidney cancer, develops in the renal tubular epithelium. Atherosclerosis of the aorta is the primary cause of atherosclerosis. However, the underlying mechanisms remain unclear.

METHODS

The renal clear cell carcinoma RNA sequence profile was obtained from The Cancer Genome Atlas (TCGA) database, and the atherosclerosis datasets GSE28829 and GSE43292 based on GPL570 and GPL6244 was obtained from the Gene Expression Omnibus (GEO) database. The difference and hub genes were identified by the Limma protein-protein interaction (PPI) network in R software. Functional enrichment, survival, and immunoinfiltration analyses were performed. The role of SEL1L3 in the ErbB/PI3K/mTOR signaling pathway, apoptosis, invasion, cell cycle, and inflammation was analyzed using western blotting.

RESULTS

764 DEGs were identified from TCGA Kidney Renal Clear Cell Carcinoma (KIRC) dataset. A total of 344 and 117 DEGs were screened from the GSE14762 and GSE53757 datasets, respectively. Functional enrichment analysis results primarily indicated enrichment in the transporter complex, DNA-binding transcription activator activity, morphogenesis of the embryonic epithelium, stem cell proliferation, adrenal overactivity and so on. Fifteen common DEGs overlapped among the three datasets. The PPI network revealed that SEL1L3 was the core gene. Survival analysis showed that lower SEL1L3 expression levels led to a worse prognosis. Immune cell infiltration analysis showed that SEL1L3 expression was significantly correlated with antibody-drug conjugates (aDC), B cells, eosinophils, interstitial dendritic cells (iDC), macrophages, and more.

CONCLUSIONS

SEL1L3 plays an important role in renal clear cell carcinoma and atherosclerosis and may be a potential link between them.