Drebrin: A new oncofetal biomarker associated with prognosis of lung adenocarcinoma

Clinical Implications and Molecular Characterization of Drebrin-Positive, Tumor-Infiltrating Exhausted T Cells in Lung Cancer

T cells express an actin-binding protein, drebrin, which is recruited to the contact site between the T cells and antigen-presenting cells during the formation of immunological synapses. However, little is known about the clinical implications of drebrin-expressing, tumor-infiltrating lymphocytes (TILs). To address this issue, we evaluated 34 surgical specimens of pathological stage I-IIIA squamous cell lung cancer. The immune context of primary tumors was investigated using fluorescent multiplex immunohistochemistry. The high-speed scanning of whole-slide images was performed, and the tissue localization of TILs in the tumor cell nest and surrounding stroma was automatically profiled and quantified. Drebrin-expressing T cells were characterized using drebrin⁺ T cells induced in vitro and publicly available single-cell RNA sequence (scRNA-seq) database. Survival analysis using the propensity scores revealed that a high infiltration of drebrin⁺ TILs within the tumor cell nest was independently associated with short relapse-free survival and overall survival. Drebrin⁺ T cells induced in vitro co-expressed multiple exhaustion-associated molecules. The scRNA-seq analyses confirmed that the exhausted tumor-infiltrating CD8⁺ T cells specifically expressed drebrin. Our study suggests that drebrin-expressing T cells present an exhausted phenotype and that tumor-infiltrating drebrin⁺ T cells affect clinical outcomes in patients with resectable squamous cell lung cancer.

Drebrin promotes lung adenocarcinoma cell migration through inducing integrin β1 endocytosis

Lung adenocarcinoma (LUAD) is the most common type of lung cancers, which remains the leading cause of cancer-related death worldwide. Drebrin can promote cell migration and invasion with poor prognosis, but its roes in LUAD tumor progression remains unknown. We showed that the expression of Drebrin was upregulated in clinical LUAD samples. A Kaplan-Meier survival analysis showed that a high expression of Drebrin predicated poor prognosis in LUAD. In vitro, Drebrin promoted anchorage-independent growth and migration of LUAD cells. Drebrin interacted with dynamin through CT domain, and served as an adaptor to promote LUAD cell migration through inducing integrin β1 endocytosis. Thus, this study demonstrated the critical role of Drebrin in LUAD and associated mechanism.

Coexpressed Genes That Promote the Infiltration of M2 Macrophages in Melanoma Can Evaluate the Prognosis and Immunotherapy Outcome

Purpose

To improve immunotherapy efficacy for melanoma, a coexpression network and key genes of M2 macrophages in melanoma were explored. A prognostic risk assessment model was established for M2-related coexpressed genes, and the role of M2 macrophages in the immune microenvironment of melanoma was elucidated.

Method

We obtained mRNA data from melanoma and peritumor tissue samples from The Cancer Genome Atlas-skin cutaneous melanoma (TCGA-SKCM). Then, we used CIBERSORT to calculate the proportion of M2 macrophage cells. A coexpression module most related to M2 macrophages in TCGA-SKCM was determined by analyzing the weighted gene coexpression network, and a coexpression network was established. After survival analysis, factors with significant results were incorporated into a Cox regression analysis to establish a model. The model's essential genes were analyzed using functional enrichment, GSEA, and subgroup and total carcinoma. Finally, external datasets GSE65904 and GSE78220 were used to verify the prognostic risk model.

Results

The yellow-green module was the coexpression module most related to M2 macrophages in TCGA-SKCM; NOTCH3, DBN1, KDELC2, and STAB1 were identified as the essential genes that promoted the infiltration of M2 macrophages in melanoma. These genes are concentrated in antigen treatment and presentation, chemokine, cytokine, the T cell receptor pathway, and the IFN-γ pathway. These factors were analyzed for survival, and factors with significant results were included in a Cox regression analysis. According to the methods, a model related to M2-TAM coexpressed gene was established, and the formula was risk score = 0.25^∗NOTCH3 + 0.008^∗ DBN1 − 0.031^∗KDELC2 − 0.032^∗STAB1. The new model was used to perform subgroup evaluation and external queue validation. The results showed good prognostic ability.

Conclusion

We proposed a Cox proportional hazards regression model associated with coexpression genes of melanoma M2 macrophages that may provide a measurement method for generating prognosis scores in patients with melanoma. Four genes coexpressed with M2 macrophages were associated with high levels of infiltration of M2 macrophages. Our findings may provide significant candidate biomarkers for the treatment and monitoring of melanoma.

Simplified ARCHITECT microfluidic chip through a dual-flip strategy enables stable and versatile tumoroid formation combined with label-free quantitative proteomic analysis

Recent years, microfluidic three-dimensional(3D) tumor culture technique has made great progress in tumor microenvironment simulation and drug screening. Meanwhile, as their functionality and complexity increase, it is more difficult for current chip models to selectively collect specific-layer cells from tumoroids for further analysis. Moreover, a simplified and robust method for tumoroid formation with highly consistent size and repeatable 3D morphology is relatively ncessary. Here, we report an ARCHITECT (ARtificial CHIp for Tumor Enables Confocal Topography observation) chip, through a dual-flip strategy to implement straightforward tumoroid establishment. This platform guarantees stable batch-to-batch tumoroids formation and allows high resolution confocal imaging. Moreover, an initial cell density as low as 65 cells per chamber is efficient to deliver a tumoroid. With this ARCHITECT chip, different-layer cells of interest could be collected from tumoroid for label-free quantitative(LFQ) proteomic analysis. For application demonstration, we mainly verified this platform for lung carcinoma (A549) tumoroid construction and proteomic analysis at out layer. Our data indicate that the out-layer cells of A549 tumoroid show extensively distinct proteomic expressions compared to two-dimensional cultured A549 cells. The up-regulated proteins are mainly related to tumorigenicity, proliferation and metastasis. And the differentially expressed proteins are mainly relevant to lipid metabolism pathway which is essential to tumor progression and proliferation. This platform provides a simplified yet robust technique to connect microfluidic tumoroid construction and LFQ proteomic analysis. The simplicity of this technique should open the way to numerous applications such as discovering the innovative targets for cancer treatment, and studying the mophological and proteomic heterogeneity of different-layer cells across the tumoroid.

Integrated Analysis of Key Differentially Expressed Genes Identifies DBN1 as a Predictive Marker of Response to Endocrine Therapy in Luminal Breast Cancer

Endocrine therapy is the mainstay of adjuvant treatment for patients with luminal breast cancer. Despite ongoing advances in endocrine therapy to date, a proportion of patients ultimately develop endocrine resistance, resulting in failure of therapy and poor prognosis. Therefore, as part of the growing concept of personalised medicine, the need for identification of predictive markers of endocrine therapy response at an early stage, is recognised. The METABRIC series was used to identify differentially expressed genes (DEGs) in term of response to adjuvant endocrine therapy. Drebrin 1 (DBN1) was identified as a key DEG associated with response to hormone treatment. Next, large, well-characterised cohorts of primary luminal breast cancer with long-term follow-up were assessed at the mRNA and protein levels for the value of DBN1 as a prognostic marker in luminal breast cancer, as well as its potential for predicting the benefit of endocrine therapy. DBN1 positivity was associated with aggressive clinicopathological variables and poor patient outcomes. Importantly, high DBN1 expression predicted relapse patients who were subject to adjuvant endocrine treatment. Our results further demonstrate that DBN1 is an independent prognostic marker in luminal breast cancer. Its association with the response to endocrine therapy and outcome provides evidence for DBN1 as a potential biomarker in luminal breast cancer, particularly for the benefit of endocrine treatment. Further functional investigations into the mechanisms underlying sensitivity to endocrine therapy is required.

Prognostic values of a novel multi-mRNA signature for predicting relapse of cholangiocarcinoma

Cholangiocarcinoma (CCA) is an epithelial cancer and has high death and recurrence rates, current methods cannot satisfy the need for predicting cancer relapse effectively. So, we aimed at conducting a multi-mRNA signature to improve the relapse prediction of CCA. We analyzed mRNA expression profiling in large CCA cohorts from the Gene Expression Omnibus (GEO) database (GSE76297, GSE32879, GSE26566, GSE31370, and GSE45001) and The Cancer Genome Atlas (TCGA) database. The Least absolute shrinkage and selection operator (LASSO) regression model was used to establish a 7-mRNA-based signature that was significantly related to the recurrence-free survival (RFS) in two test series. Based on the 7-mRNA signature, the cohort TCGA patients could be divided into high-risk or low-risk subgroups with significantly different RFS [p < 0.001, hazard ratio (HR): 48.886, 95% confidence interval (CI): 6.226-3.837E+02]. Simultaneously, the prognostic value of the 7-mRNA signature was confirmed in clinical samples of Ren Ji hospital (p < 0.001, HR: 4.558, 95% CI: 1.829-11.357). Further analysis including multivariable and sub-group analyses revealed that the 7-mRNA signature was an independent prognostic value for recurrence of patients with CCA. In conclusion, our results might provide an efficient tool for relapse prediction and were beneficial to individualized management for CCA patients.

Simultaneous Interrogation of Cancer Omics to Identify Subtypes With Significant Clinical Differences

Recent advances in high-throughput sequencing have accelerated the accumulation of omics data on the same tumor tissue from multiple sources. Intensive study of multi-omics integration on tumor samples can stimulate progress in precision medicine and is promising in detecting potential biomarkers. However, current methods are restricted owing to highly unbalanced dimensions of omics data or difficulty in assigning weights between different data sources. Therefore, the appropriate approximation and constraints of integrated targets remain a major challenge. In this paper, we proposed an omics data integration method, named high-order path elucidated similarity (HOPES). HOPES fuses the similarities derived from various omics data sources to solve the dimensional discrepancy, and progressively elucidate the similarities from each type of omics data into an integrated similarity with various high-order connected paths. Through a series of incremental constraints for commonality, HOPES can take both specificity of single data and consistency between different data types into consideration. The fused similarity matrix gives global insight into patients' correlation and efficiently distinguishes subgroups. We tested the performance of HOPES on both a simulated dataset and several empirical tumor datasets. The test datasets contain three omics types including gene expression, DNA methylation, and microRNA data for five different TCGA cancer projects. Our method was shown to achieve superior accuracy and high robustness compared with several benchmark methods on simulated data. Further experiments on five cancer datasets demonstrated that HOPES achieved superior performances in cancer classification. The stratified subgroups were shown to have statistically significant differences in survival. We further located and identified the key genes, methylation sites, and microRNAs within each subgroup. They were shown to achieve high potential prognostic value and were enriched in many cancer-related biological processes or pathways.

Neuron-specific enolase, histopathological types, and age as risk factors for bone metastases in lung cancer

Lung cancer is a malignant tumor with high metastatic ability and bone is the most common site of distant metastasis of it. However, the independent risk factors for bone metastases of lung cancer remain largely to be elucidated. Here, we conducted a retrospective study to evaluate the correlation between clinical-pathological parameters, serum levels of neuron-specific enolase and CYFRA21-1, and bone metastases in lung cancer patients. The results revealed that patients with bone metastases were younger than those without metastases. Adenocarcinoma was the most frequent type of histopathology in patients with bone metastases. And the incidence of bone metastasis in patients with adenocarcinoma was significantly higher than those with other histopathological subtypes ( p < 0.001). Furthermore, the serum concentration of neuron-specific enolase was significantly higher in patients with bone lesions than those without bone metastases. Multivariate logistic regression analysis showed that patients' age (odds ratio = 1.024, p < 0.001), concentrations of neuron-specific enolase (odds ratio = 1.212, p = 0.004), and histopathological types (odds ratio = 0.995, p = 0.001) were the independent risk factors for bone metastases in patients with lung cancer. Thus, physicians should pay attention to these factors in order to identify bone metastasis earlier while patient was primarily diagnosed as having lung cancer.