Use of four genes in exosomes as biomarkers for the identification of lung adenocarcinoma and lung squamous cell carcinoma

The emerging roles of CEACAM6 in human cancer (Review)

Carcinoembryonic antigen (CEA)‑related cell adhesion molecule 6 (CEACAM6) is a cell adhesion protein of the CEA family of glycosyl phosphatidyl inositol anchored cell surface glycoproteins. A wealth of research has demonstrated that CEACAM6 is generally upregulated in pancreatic adenocarcinoma, breast cancer, non‑small cell lung cancer, gastric cancer, colon cancer and other cancers and promotes tumor progression, invasion and metastasis. The transcriptional expression of CEACAM6 is regulated by various factors, including the CD151/TGF‑β1/Smad3 axis, microRNA (miR)‑146, miR‑26a, miR‑29a/b/c, miR‑128, miR‑1256 and DNA methylation. In addition, the N‑glycosylation of CEACAM6 protein at Asn256 is mediated by α‑1,6‑mannosylglycoptotein 6‑β‑N‑acetylglucosaminyltransferase. In terms of downstream signaling pathways, CEACAM6 promotes tumor proliferation by increasing levels of cyclin D1 and cyclin‑dependent kinase 4 proteins. CEACAM6 can activate the ERK1/2/MAPK or SRC/focal adhesion kinase/PI3K/AKT pathways directly or through EGFR, leading to stimulation of tumor proliferation, invasion, migration, resistance to anoikis and chemotherapy, as well as angiogenesis. This article provides a review of the expression pattern, biological function and relationship with prognosis of CEACAM6 in cancer. In summary, CEACAM6 may be a valuable diagnostic biomarker and potential therapeutic target for human cancers exhibiting overexpression of CEACAM6.

Exosomes in lung cancer metastasis, diagnosis, and immunologically relevant advances

Lung cancer is a chronic wasting disease with insidious onset and long treatment cycle. Exosomes are specialized extracellular vesicles, at first exosomes were considered as a transporter of cellular metabolic wastes, but recently many studies have identified exosomes which contain a variety of biologically active substances that play a role in the regulation of cellular communication and physiological functions. Exosomes play an important role in the development of lung cancer and can promote metastasis through a variety of mechanisms. However, at the same time, researchers have also discovered that immune cells can also inhibit lung cancer through exosomes. In addition, researchers have discovered that some specific miRNAs in exosomes can be used as markers for early diagnosis of lung cancer. Engineering exosomes may be one of the strategies to enhance the clinical translational application of exosomes in the future, for example, strategies such as modifying exosomes to enhance targeting or utilizing exosomes as carriers for drug delivery have been explored. but more studies are needed to verify the safety and efficacy. This article reviews the latest research on exosomes in the field of lung cancer, from the mechanism of lung cancer development, the functions of immune cell-derived exosomes and tumor-derived exosomes, to the early diagnosis of lung cancer.

Emerging role of exosomes in cancer progression and tumor microenvironment remodeling

Cancer is one of the leading causes of death worldwide, and the factors responsible for its progression need to be elucidated. Exosomes are structures with an average size of 100 nm that can transport proteins, lipids, and nucleic acids. This review focuses on the role of exosomes in cancer progression and therapy. We discuss how exosomes are able to modulate components of the tumor microenvironment and influence proliferation and migration rates of cancer cells. We also highlight that, depending on their cargo, exosomes can suppress or promote tumor cell progression and can enhance or reduce cancer cell response to radio- and chemo-therapies. In addition, we describe how exosomes can trigger chronic inflammation and lead to immune evasion and tumor progression by focusing on their ability to transfer non-coding RNAs between cells and modulate other molecular signaling pathways such as PTEN and PI3K/Akt in cancer. Subsequently, we discuss the use of exosomes as carriers of anti-tumor agents and genetic tools to control cancer progression. We then discuss the role of tumor-derived exosomes in carcinogenesis. Finally, we devote a section to the study of exosomes as diagnostic and prognostic tools in clinical courses that is important for the treatment of cancer patients. This review provides a comprehensive understanding of the role of exosomes in cancer therapy, focusing on their therapeutic value in cancer progression and remodeling of the tumor microenvironment.

Xprediction: Explainable EGFR-TKIs response prediction based on drug sensitivity specific gene networks

In recent years, drug sensitivity prediction has garnered a great deal of attention due to the growing interest in precision medicine. Several computational methods have been developed for drug sensitivity prediction and the identification of related markers. However, most previous studies have ignored genetic interaction, although complex diseases (e.g., cancer) involve many genes intricately connected in a molecular network rather than the abnormality of a single gene. To effectively predict drug sensitivity and understand its mechanism, we propose a novel strategy for explainable drug sensitivity prediction based on sample-specific gene regulatory networks, designated Xprediction. Our strategy first estimates sample-specific gene regulatory networks that enable us to identify the molecular interplay underlying varying clinical characteristics of cell lines. We then, predict drug sensitivity based on the estimated sample-specific gene regulatory networks. The predictive models are based on machine learning approaches, i.e., random forest, kernel support vector machine, and deep neural network. Although the machine learning models provide remarkable results for prediction and classification, we cannot understand how the models reach their decisions. In other words, the methods suffer from the black box problem and thus, we cannot identify crucial molecular interactions that involve drug sensitivity-related mechanisms. To address this issue, we propose a method that describes the importance of each molecular interaction for the drug sensitivity prediction result. The proposed method enables us to identify crucial gene-gene interactions and thereby, interpret the prediction results based on the identified markers. To evaluate our strategy, we applied Xprediction to EGFR-TKIs prediction based on drug sensitivity specific gene regulatory networks and identified important molecular interactions for EGFR-TKIs prediction. Our strategy effectively performed drug sensitivity prediction compared with prediction based on the expression levels of genes. We also verified through literature, the EGFR-TKIs-related mechanisms of a majority of the identified markers. We expect our strategy to be a useful tool for predicting tasks and uncovering complex mechanisms related to pharmacological profiles, such as mechanisms of acquired drug resistance or sensitivity of cancer cells.

AutoDC: an Automatic Machine Learning Framework for Disease Classification

MOTIVATION

The emergence of next-generation sequencing techniques opens up tremendous opportunities for researchers to uncover the basic mechanisms of disease at the molecular level. Recently, automatic machine learning (AutoML) frameworks have been employed for genomic and epigenomic data analysis. However, to analyze those high-dimensional data, existing AutoML frameworks suffer from the following issues: 1) they could not effectively filter out the redundant features from the original data, and 2) they usually obey the rule of feature engineering first and algorithm hyper-parameter tuning later to build the machine learning pipeline, which could lead to sub-optimal outcomes. Thus, it is an urgent need to design a new AutoML framework for high-dimensional omics data analysis.

RESULTS

We introduce a new method: AutoDC, a tailored automatic machine learning framework, for different disease classification based on gene expression data. AutoDC designs two novel optimization strategies to improve the performance. One is that AutoDC designs a novel two-stage feature selection method to select the features with high gene contribution scores. The other is that AutoDC proposes a novel optimization method, based on a two-layer Multi-Armed Bandit framework, to jointly optimize the feature engineering, algorithm selection, and algorithm hyper-parameter tuning. We apply our framework to two public gene expression datasets. Compared with three state-of-the-art AutoML frameworks, AutoDC could effectively classify diseases with higher predictive accuracy.

AVAILABILITY

The data and codes of AutoDC are available at https://github.com/dingdian110/AutoDC.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Application Value of Serum TK1 and PCDGF, CYFRA21-1, NSE, and CEA plus Enhanced CT Scan in the Diagnosis of Nonsmall Cell Lung Cancer and Chemotherapy Monitoring

Objective

To assess the application value of serum thymidine kinase 1 (TK1) and PC cell-derived growth factor (PCDGF), cytokeratin 19 fragment 21-1 (CYFRA21-1), neuron-specific enolase (NSE), and carcinoembryonic antigen (CEA) plus enhanced CT scan in the diagnosis of nonsmall cell lung cancer (NSCLC) and chemotherapy monitoring.

Methods

Between April 2019 and April 2021, 30 patients with NSCLC assessed for eligibility treated in our institution were included in the experimental group, and 30 healthy individuals screened out from physical examinations were recruited in the control group. The chemotherapy regimens included gemcitabine plus cisplatin, pemetrexed disodium plus cisplatin, and vinorelbine plus cisplatin. The application value of serum TK1, PCDGF, CYFRA21-1, NSE, CEA, and enhanced CT scan in the diagnosis and chemotherapy monitoring of NSCLC was analyzed.

Results

Before treatment, the eligible patients had significantly higher serum levels of TK1, PCDGF, CYFRA21-1, NSE, and CEA than those of the healthy individuals included (P < 0.05). Clinical efficacy was categorized into good and poor, and the good efficacy included complete response and partial response, with the poor efficacy including stable disease and progressive disease. Patients with good clinical efficacy had lower levels of serum TK1, PCDGF, CYFRA21-1, NSE, and CEA than those with poor efficacy (P < 0.05). Joint detection showed a larger area under the curve (AUC) (0.900; 95%CI, 0.812-0.988), a higher sensitivity, and a superior detection outcome to the stand-alone detection (P < 0.05). Diagnostic results were similar between joint detection and pathological examination (P > 0.05).

Conclusion

The application of serum TK1, PCDGF, CYFRA21-1, NSE, and CEA assay plus enhanced CT scan shows high sensitivity and diagnostic accuracy in the diagnosis and chemotherapy monitoring of nonsmall cell lung cancer and thus provides a diagnostic reference basis.

Feature screening for survival trait with application to TCGA high-dimensional genomic data

Background

In high-dimensional survival genomic data, identifying cancer-related genes is a challenging and important subject in the field of bioinformatics. In recent years, many feature screening approaches for survival outcomes with high-dimensional survival genomic data have been developed; however, few studies have systematically compared these methods. The primary purpose of this article is to conduct a series of simulation studies for systematic comparison; the second purpose of this article is to use these feature screening methods to further establish a more accurate prediction model for patient survival based on the survival genomic datasets of The Cancer Genome Atlas (TCGA).

Results

Simulation studies prove that network-adjusted feature screening measurement performs well and outperforms existing popular univariate independent feature screening methods. In the application of real data, we show that the proposed network-adjusted feature screening approach leads to more accurate survival prediction than alternative methods that do not account for gene-gene dependency information. We also use TCGA clinical survival genetic data to identify biomarkers associated with clinical survival outcomes in patients with various cancers including esophageal, pancreatic, head and neck squamous cell, lung, and breast invasive carcinomas.

Conclusions

These applications reveal advantages of the new proposed network-adjusted feature selection method over alternative methods that do not consider gene-gene dependency information. We also identify cancer-related genes that are almost detected in the literature. As a result, the network-based screening method is reliable and credible.

HyperChIP: identification of hypervariable signals across ChIP-seq or ATAC-seq samples

Identifying genomic regions with hypervariable ChIP-seq or ATAC-seq signals across given samples is essential for large-scale epigenetic studies. In particular, the hypervariable regions across tumors from different patients indicate their heterogeneity and can contribute to revealing potential cancer subtypes and the associated epigenetic markers. We present HyperChIP as the first complete statistical tool for the task. HyperChIP uses scaled variances that account for the mean-variance dependence to rank genomic regions, and it increases the statistical power by diminishing the influence of true hypervariable regions on model fitting. A pan-cancer case study illustrates the practical utility of HyperChIP.

Emerging role of exosomes as biomarkers in cancer treatment and diagnosis

Cancer is a leading cause of death worldwide and cancer incidence and mortality are rapidly growing. These massive amounts of cancer patients require rapid diagnosis and efficient treatment strategies. However, the currently utilized methods are invasive and cost-effective. Recently, the effective roles of exosomes as promising diagnostic, prognostic, and predictive biomarkers have been revealed. Exosomes are membrane-bound extracellular vesicles containing RNAs, DNAs, and proteins, and are present in a wide array of body fluids. Exosomal cargos have shown the potential to detect various types of cancers at early stages with high sensitivity and specificity. They can also delivery therapeutic agents efficiently. In this article, an overview of recent advances in the research of exosomal biomarkers and their applications in cancer diagnosis and treatment has been provided. Furthermore, the advantages and challenges of exosomes as liquid biopsy targets are discussed and the clinical implications of using exosomal miRNAs have been revealed.

Exosomes in Lung Cancer: Actors and Heralds of Tumor Development

Lung cancer is a leading cause of cancer-related death worldwide and in most cases, diagnosis is reached when the tumor has already spread and prognosis is quite poor. For that reason, the research for new biomarkers that could improve early diagnosis and its management is essential. Exosomes are microvesicles actively secreted by cells, especially by tumor cells, hauling molecules that mimic molecules of the producing cells. There are multiple methods for exosome isolation and analysis, although not standardized, and cancer exosomes from biological fluids are especially difficult to study. Exosomes' cargo proteins, RNA, and DNA participate in the communication between cells, favoring lung cancer development by delivering signals for growth, metastasis, epithelial mesenchymal transition, angiogenesis, immunosuppression and even drug resistance. Exosome analysis can be useful as a type of liquid biopsy in the diagnosis, prognosis and follow-up of lung cancer. In this review, we will discuss recent advances in the role of exosomes in lung cancer and their utility as liquid biopsy, with special attention to isolating methods.

Construction of a prognostic model for lung squamous cell carcinoma based on seven N6-methylandenosine-related autophagy genes

OBJECTIVE

We aimed to construct a novel prognostic model based on N6-methyladenosine (m6A)-related autophagy genes for predicting the prognosis of lung squamous cell carcinoma (LUSC).

METHODS

Gene expression profiles and clinical information of Patients with LUSC were downloaded from The Cancer Genome Atlas (TCGA) database. In addition, m6A- and autophagy-related gene profiles were obtained from TCGA and Human Autophagy Database, respectively. Pearson correlation analysis was performed to identify the m6A-related autophagy genes, and univariate Cox regression analysis was conducted to screen for genes associated with prognosis. Based on these genes, LASSO Cox regression analysis was used to construct a prognostic model. The corresponding prognostic score (PS) was calculated, and patients with LUSC were assigned to low- and high-risk groups according to the median PS value. An independent dataset (GSE37745) was used to validate the prognostic ability of the model. CIBERSORT was used to calculate the differences in immune cell infiltration between the high- and low-risk groups.

RESULTS

Seven m6A-related autophagy genes were screened to construct a prognostic model: CASP4, CDKN1A, DLC1, ITGB1, PINK1, TP63, and EIF4EBP1. In the training and validation sets, patients in the high-risk group had worse survival times than those in the low-risk group; the areas under the receiver operating characteristic curves were 0.958 and 0.759, respectively. There were differences in m6A levels and immune cell infiltration between the high- and low-risk groups.

CONCLUSIONS

Our prognostic model of the seven m6A-related autophagy genes had significant predictive value for LUSC; thus, these genes may serve as autophagy-related therapeutic targets in clinical practice.

Serum Exosomes and Their miRNA Load-A Potential Biomarker of Lung Cancer

Early detection of lung cancer in screening programs is a rational way to reduce mortality associated with this malignancy. Low-dose computed tomography, a diagnostic tool used in lung cancer screening, generates a relatively large number of false-positive results, and its complementation with molecular biomarkers would greatly improve the effectiveness of such programs. Several biomarkers of lung cancer based on different components of blood, including miRNA signatures, were proposed. However, only a few of them have been positively validated in the context of early cancer detection yet, which imposes a constant need for new biomarker candidates. An emerging source of cancer biomarkers are exosomes and other types of extracellular vesicles circulating in body fluids. Hence, different molecular components of serum/plasma-derived exosomes were tested and showed different levels in lung cancer patients and healthy individuals. Several studies focused on the miRNA component of these vesicles. Proposed signatures of exosome miRNA had promising diagnostic value, though none of them have yet been clinically validated. These signatures involved a few dozen miRNA species overall, including a few species that recurred in different signatures. It is worth noting that all these miRNA species have cancer-related functions and have been associated with lung cancer progression. Moreover, a few of them, including known oncomirs miR-17, miR-19, miR-21, and miR-221, appeared in multiple miRNA signatures of lung cancer based on both the whole serum/plasma and serum/plasma-derived exosomes.