Potentially functional genetic variants in the complement-related immunity gene-set are associated with non-small cell lung cancer survival

Contribution of Endothelial Dysfunction to Cancer Susceptibility and Progression: A Comprehensive Narrative Review on the Genetic Risk Component

Venous thromboembolism (VTE) is a challenging clinical obstacle in oncological settings, marked by elevated incidence rates and resulting morbidity and mortality. In the context of cancer-associated thrombosis (CAT), endothelial dysfunction (ED) plays a crucial role in promoting a pro-thrombotic environment as endothelial cells lose their ability to regulate blood flow and coagulation. Moreover, emerging research suggests that this disorder may not only contribute to CAT but also impact tumorigenesis itself. Indeed, a dysfunctional endothelium may promote resistance to therapy and favour tumour progression and dissemination. While extensive research has elucidated the multifaceted mechanisms of ED pathogenesis, the genetic component remains a focal point of investigation. This comprehensive narrative review thus delves into the genetic landscape of ED and its potential ramifications on cancer progression. A thorough examination of genetic variants, specifically polymorphisms, within key genes involved in ED pathogenesis, namely eNOS, EDN1, ACE, AGT, F2, SELP, SELE, VWF, ICAM1, and VCAM1, was conducted. Overall, these polymorphisms seem to play a context-dependent role, exerting both oncogenic and tumour suppressor effects depending on the tumour and other environmental factors. In-depth studies are needed to uncover the mechanisms connecting these DNA variations to the pathogenesis of malignant diseases.

Moringa oleifera leaf polysaccharides exert anti-lung cancer effects upon targeting TLR4 to reverse the tumor-associated macrophage phenotype and promote T-cell infiltration

Tumor-associated macrophages (TAMs) participate in tumorigenesis, growth, invasion as well as metastasis by facilitating an immunosuppressive tumor microenvironment. Reversing the pro-tumoral M2 phenotype of TAMs has become a hot spot in advancing cancer immunotherapy. In the current study, the content of Moringa oleifera leaf polysaccharides (MOLP) was determined and characterized, along with the anti-cancer mechanism of MOLP studied in a Lewis lung cancer (LLC) tumor-bearing mouse model and bone marrow-derived macrophages. The monosaccharide composition and gel permeation chromatography analyses show that MOLP are mainly composed of galactose, glucose, and arabinose, with approximately 17.35 kDa average molecular weight (Mw). In vivo studies demonstrate that MOLP convert TAMs from the immunosuppressive M2 phenotype to the antitumor M1 phenotype, thus inducing CXCL9 and CXCL10 expression and increasing T-cell infiltration in the tumor. Furthermore, macrophage depletion and T cell suppression demonstrated that the tumor suppressive effect of MOLP was reliant on reprogramming macrophage polarization and T cell infiltration. In vitro studies revealed that MOLP could induce the phenotypic switch from M2 macrophages to M1 by targeting TLR4. The current study highlights that MOLP are promising anticancer plant-derived polysaccharides with potential in modulating the immune microenvironment and have a bright application prospect in the immunotherapy of lung cancer.

Identification of lung adenocarcinoma subtypes and predictive signature for prognosis, immune features, and immunotherapy based on immune checkpoint genes

Background: Lung adenocarcinoma (LUAD) is the most common variant of non-small cell lung cancer (NSCLC) across the world. Recently, the rapid development of immunotherapy has brought a new dawn for LUAD patients. Closely related to the tumor immune microenvironment and immune cell functions, more and more new immune checkpoints have been discovered, and various cancer treatment studies targeting these novel immune checkpoints are currently in full swing. However, studies on the phenotype and clinical significance of novel immune checkpoints in LUAD are still limited, and only a minority of patients with LUAD can benefit from immunotherapy. Methods: The LUAD datasets were downloaded from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) databases, and the immune checkpoints score of each sample were calculated based on the expression of the 82 immune checkpoints-related genes (ICGs). The weighted gene co-expression network analysis (WGCNA) was used to obtain the gene modules closely related to the score and two different LUAD clusters were identified based on these module genes by the Non-negative Matrix Factorization (NMF) Algorithm. The differentially expressed genes between the two clusters were further used to construct a predictive signature for prognosis, immune features, and the response to immunotherapy for LUAD patients through a series of regression analyses. Results: A new immune checkpoints-related signature was finally established according to the expression of 7 genes (FCER2, CD200R1, RHOV, TNNT2, WT1, AHSG, and KRTAP5-8). This signature can stratify patients into high-risk and low-risk groups with different survival outcomes and sensitivity to immunotherapy, and the signature has been well validated in different clinical subgroups and validation cohorts. Conclusion: We constructed a novel immune checkpoints-related LUAD risk assessment system, which has a good predictive ability and significance for guiding immunotherapy. We believe that these findings will not only aid in the clinical management of LUAD patients but also provide some insights into screening appropriate patients for immunotherapy.

Artificial intelligence applications in pediatric oncology diagnosis

Artificial intelligence (AI) algorithms have been applied in abundant medical tasks with high accuracy and efficiency. Physicians can improve their diagnostic efficiency with the assistance of AI techniques for improving the subsequent personalized treatment and surveillance. AI algorithms fundamentally capture data, identify underlying patterns, achieve preset endpoints, and provide decisions and predictions about real-world events with working principles of machine learning and deep learning. AI algorithms with sufficient graphic processing unit power have been demonstrated to provide timely diagnostic references based on preliminary training of large amounts of clinical and imaging data. The sample size issue is an inevitable challenge for pediatric oncology considering its low morbidity and individual heterogeneity. However, this problem may be solved in the near future considering the exponential advancements of AI algorithms technically to decrease the dependence of AI operation on the amount of data sets and the efficiency of computing power. For instance, it could be a feasible solution by shifting convolutional neural networks (CNNs) from adults and sharing CNN algorithms across multiple institutions besides original data. The present review provides important insights into emerging AI applications for the diagnosis of pediatric oncology by systematically overviewing of up-to-date literature.

Artificial intelligence in pancreatic cancer: diagnosis, limitations, and the future prospects-a narrative review

PURPOSE

This review aims to explore the role of AI in the application of pancreatic cancer management and make recommendations to minimize the impact of the limitations to provide further benefits from AI use in the future.

METHODS

A comprehensive review of the literature was conducted using a combination of MeSH keywords, including "Artificial intelligence", "Pancreatic cancer", "Diagnosis", and "Limitations".

RESULTS

The beneficial implications of AI in the detection of biomarkers, diagnosis, and prognosis of pancreatic cancer have been explored. In addition, current drawbacks of AI use have been divided into subcategories encompassing statistical, training, and knowledge limitations; data handling, ethical and medicolegal aspects; and clinical integration and implementation.

CONCLUSION

Artificial intelligence (AI) refers to computational machine systems that accomplish a set of given tasks by imitating human intelligence in an exponential learning pattern. AI in gastrointestinal oncology has continued to provide significant advancements in the clinical, molecular, and radiological diagnosis and intervention techniques required to improve the prognosis of many gastrointestinal cancer types, particularly pancreatic cancer.

Potentially functional genetic variants of VAV2 and PSMA4 in the immune-activation pathway and non-small cell lung cancer survival

BACKGROUND

Lung cancer ranks the highest mortality among cancers, represented by a low 5-year survival rate. The function of the immune system has a profound influence on the development and progression of lung cancer. Thus genetic variants of the immune-related genes may serve as potential predictors of non-small cell lung cancer (NSCLC) survival.

METHODS

In the present study, we conducted a two-stage survival analysis in 1,531 NSCLC patients and assessed the associations between genetic variants in the immune-activation gene-set and overall survival (OS) of NSCLC. The validated variants were further subjected to functional annotation and in vitro experiments.

RESULTS

We identified 25 SNPs spanning 6 loci associated with NSCLC OS after multiple-testing corrections in all datasets, in which two variants, PSMA4 rs12901682 A>C and VAV2 rs12002767 C>T were shown to potentially affect lung cancer OS by cis-regulating the expression of the corresponding genes [(HR (95% CI) = 0.76 (0.65-0.89) and 1.36 (1.12-1.65), P=4.29E-04 and 0.002, respectively)].

CONCLUSION

Our findings provide new insights into the role of genetic variants in the immune-activation pathway genes in lung cancer progression.

Genetic variants in the human leukocyte antigen region and survival of Chinese patients with non-small cell lung carcinoma

Human leukocyte antigen (HLA) is highly polymorphic, driving antigen presentation, complement cascade and leukocyte maturation against cancer cells. Therefore, we extracted genotyping data in the HLA region from an ongoing Chinese genome-wide association study of non-small cell lung cancer (NSCLC). Using deep sequencing data of 10 689 healthy Han Chinese, we imputed for untyped genetic variants in the HLA region, followed by a two-stage survival analysis of 1531 NSCLC patients. In the discovery stage of 758 patients, we identified 301 out of 15 138 single-nucleotide polymorphisms to be independently associated with overall survival [P < 0.05 and Bayesian false-discovery probability < 0.8]. In further validation of another 773 patients, we confirmed chromosome 6p21, rs241424 (located at intron 3 of TAP2) and rs6457642 as two independent survival predictors. In the combined analysis of 1531 NSCLC patients, rs241424 G>A and rs6457642 C>T were associated with a hazards ratio of 1.26 [95% confidence interval (CI) = 1.14-1.40 and P = 4.04 × 10-6] and 0.76 (95% CI = 0.66-0.87 and P = 1.16 × 10-4), respectively. The analysis of publically available ChIP-sequencing and Hi-C data found that the rs241424 locus was involved in potential cis-regulatory element by a long-range interaction with the HLA-DQA1 promoter. Additional expression quantitative trait loci analysis showed that the rs241424 G>A change decreased HLA-DQA1 mRNA expression. Furthermore, expression levels of HLA-DQA1 were lower in lung cancer tissues than in adjacent normal tissues, and the lower expression was associated with a worse prognosis for patients with lung adenocarcinoma. Collectively, HLA genetic variants may modulate OS of NSCLC patients, possibly via a mechanism of long-range promoter interaction regulating HLA-DQA1 expression.

Novel Gene Signatures Predictive of Patient Recurrence-Free Survival and Castration Resistance in Prostate Cancer

Simple Summary

Molecular signatures predictive of recurrence-free survival (RFS) and castration resistance are critical for treatment decision-making in prostate cancer (PCa), but the robustness of current signatures is limited. This study aims to identify castration-resistant PCa (CRPC)-associated genes and develop robust RFS and CRPC signatures. Among 287 genes differentially expressed between localized CRPC and hormone-sensitive PCa (HSPC) samples, 6 genes constituted a signature (CRPC-derived prognosis signature, CRPCPS) that predicted RFS. Moreover, a 3-gene panel derived from the 6 CRPCPS genes was capable of distinguishing CRPC from HSPC. The CRPCPS predicted RFS in 5/9 cohorts in the multivariate analysis and maintained prognostic in patients stratified by tumor stage, Gleason score, and lymph node metastasis status. It also predicted overall survival and metastasis-free survival. Notably, the signature was validated in another six independent cohorts. These findings suggest that these two signatures could be robust tools for predicting RFS and CRPC in clinical practice.

Abstract

Molecular signatures predictive of recurrence-free survival (RFS) and castration resistance are critical for treatment decision-making in prostate cancer (PCa), but the robustness of current signatures is limited. Here, we applied the Robust Rank Aggregation (RRA) method to PCa transcriptome profiles and identified 287 genes differentially expressed between localized castration-resistant PCa (CRPC) and hormone-sensitive PCa (HSPC). Least absolute shrinkage and selection operator (LASSO) and stepwise Cox regression analyses of the 287 genes developed a 6-gene signature predictive of RFS in PCa. This signature included NPEPL1, VWF, LMO7, ALDH2, NUAK1, and TPT1, and was named CRPC-derived prognosis signature (CRPCPS). Interestingly, three of these 6 genes constituted another signature capable of distinguishing CRPC from HSPC. The CRPCPS predicted RFS in 5/9 cohorts in the multivariate analysis and remained valid in patients stratified by tumor stage, Gleason score, and lymph node status. The signature also predicted overall survival and metastasis-free survival. The signature’s robustness was demonstrated by the C-index (0.55–0.74) and the calibration plot in all nine cohorts and the 3-, 5-, and 8-year area under the receiver operating characteristic curve (0.67–0.77) in three cohorts. The nomogram analyses demonstrated CRPCPS’ clinical applicability. The CRPCPS thus appears useful for RFS prediction in PCa.

Effects of complement gene-set polygenic risk score on brain volume and cortical measures in patients with psychotic disorders and healthy controls

Multiple genome-wide association studies of schizophrenia have reported associations between genetic variants within the MHC region and disease risk, an association that has been partially accounted for by alleles of the complement component 4 (C4) gene. Following on previous findings of association between both C4 and other complement-related variants and memory function, we tested the hypothesis that polygenic scores calculated based on identified schizophrenia risk alleles within the "complement" system would be broadly associated with memory function and associated brain structure. We tested this using a polygenic risk score (PRS) calculated for complement genes, but excluding C4 variants. Higher complement-based PRS scores were observed to be associated with lower memory scores for the sample as a whole (N = 620, F change = 8.25; p = .004). A significant association between higher PRS and lower hippocampal volume was also observed (N = 216, R² change = 0.016, p = .015). However, after correcting for further testing of association with the more general indices of cortical thickness, surface area or total brain volume, none of which were associated with complement, the association with hippocampal volume became non-significant. A post-hoc analysis of hippocampal subfields suggested an association between complement PRS and several hippocampal subfields, findings that appeared to be particularly driven by the patient sample. In conclusion, our study yielded suggestive evidence of association between complement-based schizophrenia PRS and variation in memory function and hippocampal volume.

Novel genetic variants of SYK and ITGA1 related lymphangiogenesis signaling pathway predict non-small cell lung cancer survival

Although lymphangiogenesis is a vital step in lung cancer metastasis, the association between lymphangiogenesis and non-small cell lung cancer (NSCLC) survival remains unclear. Since single-nucleotide polymorphisms (SNPs) have been reported to predict NSCLC survival, we investigated associations between SNPs in lymphangiogenesis-related pathway genes and NSCLC survival in a discovery genotyping dataset of 1,185 patients from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial and validated the findings in another genotyping dataset of 984 patients from the Harvard Lung Cancer Susceptibility Study. We evaluated associations between 34,509 genetic variants (3252 genotyped and 31,257 imputed) in 247 genes involved in lymphangiogenesis-related pathway and NSCLC survival. After validation, we finally identified two independent SNPs (SYK rs11787670 A>G and ITGA1 rs67715745 T>C) to be significantly associated with NSCLC overall survival (OS), with adjusted hazards ratios of 0.77 and 0.83 (95% confidence interval =0.66-0.90, P=7.20×10^-4) and 0.84 (95% confidence interval =0.75-0.92, P=3.50×10^-4), respectively. Moreover, an increasing number of combined protective alleles of these two SNPs was significantly associated with an improved NSCLC OS and disease-specific survival (DSS) in the PLCO dataset (P _trend=0.011 and 0.006, respectively). Furthermore, the addition of these protective alleles to the prediction model for the 5-year survival increased the time-dependent area under the curve both from 87% to 87.67% for OS (P=0.029) and from 88.54% to 89.06% for DSS (P=0.022). Subsequent expression quantitative trait loci (eQTL) functional analysis revealed that the rs11787670 G allele was significantly associated with an elevated SYK mRNA expression in normal tissues. Additional analyses suggested a suppressor role for both SYK and ITGA1 in NSCLC survival. Collectively, these findings indicated that SYK rs11787670 A>G and ITGA1 rs67715745 T>C may be independent prognostic factors for NSCLC survival once further validated.

Novel genetic variants in genes of the Fc gamma receptor-mediated phagocytosis pathway predict non-small cell lung cancer survival

Background

Both antibody-dependent cellular cytotoxicity and phagocytosis activate innate immunity, and the Fc gamma receptor (FCGR)-mediated phagocytosis is an integral part of the process. We assessed associations between single-nucleotide polymorphisms (SNPs) in FCGR-related genes and survival of patients with non-small cell lung cancer (NSCLC).

Methods

We evaluated associations between 24,734 (SNPs) in 97 FCGR-related genes and survival of 1,185 patients with NSCLC using a published genome-wide association study (GWAS) dataset from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial and validated the results in another independent dataset of 894 NSCLC patients.

Results

In the single-locus analysis with Bayesian false discovery probability (BFDP) for multiple testing correction, we found 1,084 SNPs to be significantly associated overall survival (OS) (P<0.050 and BFDP ≤0.80), of which two independent SNPs (PLCG2 rs9673682 T>G and PLPP1 rs115613985 T>A) were further validated in another GWAS dataset of 894 patients from the Harvard Lung Cancer Susceptibility (HLCS) Study, with combined allelic hazards ratios for OS of 0.87 [95% confidence interval (CI): 0.81-0.94 and P=5.90×10^-4] and 1.18 (95% CI: 1.08-1.29 and 1.32×10^-4, respectively). Expression quantitative trait loci analysis showed that the rs9673682 G allele was significantly correlated with increased mRNA expression levels of PLCG2 in 373 transformed lymphoblastoid cell-lines (P=7.20×10^-5). Additional evidence from differential expression analysis further supported a tumor-suppressive effect of PLCG2 on OS of patients with lung cancer, with lower mRNA expression levels in both lung squamous carcinoma and adenocarcinoma than in adjacent normal tissues.

Conclusions

Genetic variants in PLCG2 of the FCGR-mediated phagocytosis pathway may be promising predictors of NSCLC survival, possibly through modulating gene expression, but additional investigation of the molecular mechanisms of PLPP1 rs115613985 is warranted.

Artificial intelligence in cancer diagnosis and prognosis: Opportunities and challenges

Cancer is an aggressive disease with a low median survival rate. Ironically, the treatment process is long and very costly due to its high recurrence and mortality rates. Accurate early diagnosis and prognosis prediction of cancer are essential to enhance the patient's survival rate. Developments in statistics and computer engineering over the years have encouraged many scientists to apply computational methods such as multivariate statistical analysis to analyze the prognosis of the disease, and the accuracy of such analyses is significantly higher than that of empirical predictions. Furthermore, as artificial intelligence (AI), especially machine learning and deep learning, has found popular applications in clinical cancer research in recent years, cancer prediction performance has reached new heights. This article reviews the literature on the application of AI to cancer diagnosis and prognosis, and summarizes its advantages. We explore how AI assists cancer diagnosis and prognosis, specifically with regard to its unprecedented accuracy, which is even higher than that of general statistical applications in oncology. We also demonstrate ways in which these methods are advancing the field. Finally, opportunities and challenges in the clinical implementation of AI are discussed. Hence, this article provides a new perspective on how AI technology can help improve cancer diagnosis and prognosis, and continue improving human health in the future.

Mining Prognosis Index of Brain Metastases Using Artificial Intelligence

This study is to identify the optimum prognosis index for brain metastases by machine learning. Seven hundred cancer patients with brain metastases were enrolled and divided into 446 training and 254 testing cohorts. Seven features and seven prediction methods were selected to evaluate the performance of cancer prognosis for each patient. We used mutual information and rough set with particle swarm optimization (MIRSPSO) methods to predict patient’s prognosis with the highest accuracy at area under the curve (AUC) = 0.978 ± 0.06. The improvement by MIRSPSO in terms of AUC was at 1.72%, 1.29%, and 1.83% higher than that of the traditional statistical method, sequential feature selection (SFS), mutual information with particle swarm optimization(MIPSO), and mutual information with sequential feature selection (MISFS), respectively. Furthermore, the clinical performance of the best prognosis was superior to conventional statistic method in accuracy, sensitivity, and specificity. In conclusion, identifying optimal machine-learning methods for the prediction of overall survival in brain metastases is essential for clinical applications. The accuracy rate by machine-learning is far higher than that of conventional statistic methods.