Multiplatform discovery and regulatory function analysis of structural variations in non-small cell lung carcinoma

Deciphering the Significance of Platelet-Derived Chloride Ion Channel Gene (BEST3) Through Platelet-Related Subtypes Mining for Non-Small Cell Lung Cancer

This study investigates platelet-related subtypes in non-small cell lung cancer (NSCLC) and seeks to identify genes associated with prognosis, focusing on the clinical significance of the chloride ion channel gene BEST3. We utilised sequencing and clinical data from GEO, TCGA and the Xena platform, building a risk model based on genetic features. TCGA and GSE37745 served as training cohorts, while GSE50081, GSE13213, GSE30129 and GSE42127 were validation cohorts. Immunotherapy datasets (GSE135222, TCGA-SKCM) were also analysed. Differentially expressed genes (DEGs) were identified using Limma, subtypes through ConsensusClusterPlus and key prognostic genes using COX regression, Random Forest and LASSO-COX. BEST3 expression was validated by flow cytometry (FCM) and functional assays in A549 cells with lentiviral overexpression evaluated its impact on apoptosis, proliferation and migration. Three platelet-related subtypes were identified, with ten key prognostic genes (including BEST3). Gene Ontology (GO) analysis showed six genes involved in platelet pathways. BEST3 was highly expressed in the platelet subtype 1. Flow cytometry confirmed elevated BEST3 levels in NSCLC (35.9% vs. 27.3% in healthy individuals). Overexpression of BEST3 in NSCLC cells suppressed apoptosis and promoted proliferation and migration. The discovery of three platelet subtypes and the role of BEST3 in promoting tumour growth and migration highlights its potential as a therapeutic target and prognostic marker in NSCLC.

Modelling of miRNA-mRNA Network to Identify Gene Signatures with Diagnostic and Prognostic Value in Gastric Cancer: Evidence from In-Silico and In-Vitro Studies

Background

Gastric cancer (GC) is a prevalent malignancy with high recurrence. Advances in systems biology have identified molecular pathways and biomarkers. This study focuses on discovering gene and miRNA biomarkers for diagnosing and predicting survival in GC patients.

Methods

Three sets of genes (GSE19826, GSE81948, and GSE112369) and two sets of miRNA expression (GSE26595, GSE78775) were obtained from the Gene Expression Omnibus (GEO), and subsequently, differentially expressed genes (DEGs) and miRNAs (DEMs) were identified. Functional pathway enrichment, DEG-miR-TF-protein-protein interaction network, DEM-mRNA network, ROC curve, and survival analyses were performed. Finally, qRT-PCR was applied to validate our results.

Results

From the high-throughput profiling studies of GC, we investigated 10 candidate mRNA and 7 candidate miRNAs as potential biomarkers. Expression analysis of these hubs revealed that 5 miRNAs (including miR-141-3p, miR-204-5p, miR-338-3p, miR-609, and miR-369-5p) were significantly upregulated compared to the controls. The genes with the highest degree included 6 upregulated and 4 downregulated genes in tumor samples compared to controls. The expression of miR-141-3p, miR-204-5p, SESTD1, and ANTXR1 were verified in vitro from these hub DEMs and DEGs. The findings indicated a decrease in the expression of miR-141-3p and miR-204-5p and increased expression of SESTD1 and ANTXR1 in GC cell lines compared to the GES-1 cell line.

Conclusions

The current investigation successfully recognized a set of prospective miRNAs and genes that may serve as potential biomarkers for GC's early diagnosis and prognosis.

DNA damage response and neoantigens: A favorable target for triple-negative breast cancer immunotherapy and vaccine development

Triple-negative breast cancer (TNBC) poses a significant clinical challenge due to its aggressive nature and limited therapeutic options. The interplay between DNA damage response (DDR) mechanisms and the emergence of neoantigens represents a promising avenue for developing targeted immunotherapeutic strategies and vaccines for TNBC. The DDR is a complex network of cellular mechanisms designed to maintain genomic integrity. In TNBC, where genetic instability is a hallmark, dysregulation of DDR components plays a pivotal role in tumorigenesis and progression. This review explores the intricate relationship between DDR and neoantigens, shedding light on the potential vulnerabilities of TNBC cells. Neoantigens, arising from somatic mutations in cancer cells, represent unique antigens that can be recognized by the immune system. TNBC's propensity for genomic instability leads to an increased mutational burden, consequently yielding a rich repertoire of neoantigens. The convergence of DDR and neoantigens in TNBC offers a distinctive opportunity for immunotherapeutic targeting. Immunotherapy has revolutionized cancer treatment by harnessing the immune system to selectively target cancer cells. The unique immunogenicity conferred by DDR-related neoantigens in TNBC positions them as ideal targets for immunotherapeutic interventions. This review also explores various immunotherapeutic modalities, including immune checkpoint inhibitors (ICIs), adoptive cell therapies, and cancer vaccines, that leverage the DDR and neoantigen interplay to enhance anti-tumor immune responses. Moreover, the potential for developing vaccines targeting DDR-related neoantigens opens new frontiers in preventive and therapeutic strategies for TNBC. The rational design of vaccines tailored to the individual mutational landscape of TNBC holds promise for precision medicine approaches. In conclusion, the convergence of DDR and neoantigens in TNBC presents a compelling rationale for the development of innovative immunotherapies and vaccines. Understanding and targeting these interconnected processes may pave the way for personalized and effective interventions, offering new hope for patients grappling with the challenges posed by TNBCs.

Construction of a mortality risk prediction model for elderly people at risk of lobectomy for NSCLC

Background

An increasing number of lung cancer patients are opting for lobectomy for oncological treatment. However, due to the unique organismal condition of elderly patients, their short-term postoperative mortality is significantly higher than that of non-elderly patients. Therefore, there is a need to develop a personalised predictive tool to assess the risk of postoperative mortality in elderly patients.

Methods

Information on the diagnosis and survival of 35,411 older patients with confirmed lobectomy NSCLC from 2009 to 2019 was screened from the SEER database. The surgical group was divided into a high-risk mortality population group (≤90 days) and a non-high-risk mortality population group using a 90-day criterion. Survival curves were plotted using the Kaplan-Meier method to compare the differences in overall survival (OS) and lung cancer-specific survival (LCSS) between the two groups. The data set was split into modelling and validation groups in a ratio of 7.5:2.5, and model risk predictors of postoperative death in elderly patients with NSCLC were screened using univariate and multifactorial logistic regression. Columnar plots were constructed for model visualisation, and the area under the subject operating characteristic curve (AUC), DCA decision curve and clinical impact curve were used to assess model predictiveness and clinical utility.

Results

Multi-factor logistic regression results showed that sex, age, race, histology and grade were independent predictors of the risk of postoperative death in elderly patients with NSCLC. The above factors were imported into R software to construct a line graph model for predicting the risk of postoperative death in elderly patients with NSCLC. The AUCs of the modelling and validation groups were 0.711 and 0.713 respectively, indicating that the model performed well in terms of predictive performance. The DCA decision curve and clinical impact curve showed that the model had a high net clinical benefit and was of clinical application.

Conclusion

The construction and validation of a predictive model for death within 90 days of lobectomy in elderly patients with lung cancer will help the clinic to identify high-risk groups and give timely intervention or adjust treatment decisions.

Neoantigens: promising targets for cancer therapy

Recent advances in neoantigen research have accelerated the development and regulatory approval of tumor immunotherapies, including cancer vaccines, adoptive cell therapy and antibody-based therapies, especially for solid tumors. Neoantigens are newly formed antigens generated by tumor cells as a result of various tumor-specific alterations, such as genomic mutation, dysregulated RNA splicing, disordered post-translational modification, and integrated viral open reading frames. Neoantigens are recognized as non-self and trigger an immune response that is not subject to central and peripheral tolerance. The quick identification and prediction of tumor-specific neoantigens have been made possible by the advanced development of next-generation sequencing and bioinformatic technologies. Compared to tumor-associated antigens, the highly immunogenic and tumor-specific neoantigens provide emerging targets for personalized cancer immunotherapies, and serve as prospective predictors for tumor survival prognosis and immune checkpoint blockade responses. The development of cancer therapies will be aided by understanding the mechanism underlying neoantigen-induced anti-tumor immune response and by streamlining the process of neoantigen-based immunotherapies. This review provides an overview on the identification and characterization of neoantigens and outlines the clinical applications of prospective immunotherapeutic strategies based on neoantigens. We also explore their current status, inherent challenges, and clinical translation potential.

African-specific molecular taxonomy of prostate cancer

Prostate cancer is characterized by considerable geo-ethnic disparity. African ancestry is a significant risk factor, with mortality rates across sub-Saharan Africa of 2.7-fold higher than global averages1. The contributing genetic and non-genetic factors, and associated mutational processes, are unknown2,3. Here, through whole-genome sequencing of treatment-naive prostate cancer samples from 183 ancestrally (African versus European) and globally distinct patients, we generate a large cancer genomics resource for sub-Saharan Africa, identifying around 2 million somatic variants. Significant African-ancestry-specific findings include an elevated tumour mutational burden, increased percentage of genome alteration, a greater number of predicted damaging mutations and a higher total of mutational signatures, and the driver genes NCOA2, STK19, DDX11L1, PCAT1 and SETBP1. Examining all somatic mutational types, we describe a molecular taxonomy for prostate cancer differentiated by ancestry and defined as global mutational subtypes (GMS). By further including Chinese Asian data, we confirm that GMS-B (copy-number gain) and GMS-D (mutationally noisy) are specific to African populations, GMS-A (mutationally quiet) is universal (all ethnicities) and the African-European-restricted subtype GMS-C (copy-number losses) predicts poor clinical outcomes. In addition to the clinical benefit of including individuals of African ancestry, our GMS subtypes reveal different evolutionary trajectories and mutational processes suggesting that both common genetic and environmental factors contribute to the disparity between ethnicities. Analogous to gene-environment interaction-defined here as a different effect of an environmental surrounding in people with different ancestries or vice versa-we anticipate that GMS subtypes act as a proxy for intrinsic and extrinsic mutational processes in cancers, promoting global inclusion in landmark studies.