A gene expression signature-based nomogram model in prediction of breast cancer bone metastases

A novel super-enhancer-related gene signature predicts prognosis and immune microenvironment for breast cancer

BACKGROUND

This study targeted at developing a robust, prognostic signature based on super-enhancer-related genes (SERGs) to reveal survival prognosis and immune microenvironment of breast cancer.

METHODS

RNA-sequencing data of breast cancer were retrieved from The Cancer Genome Atlas (TCGA), 1069 patients of which were randomly assigned into training or testing set in 1:1 ratio. SERGs were downloaded from Super-Enhancer Database (SEdb). After which, a SERGs signature was established based on the training set, with its prognostic value further validated in the testing set. Subsequently, we identified the potential function enrichment and tumor immune infiltration of the model. Moreover, in vitro experiments were completed to further explore the biological functions of ZIC2 gene (one of the risk genes in the prognostic model) in breast cancer.

RESULTS

A risk score system of prognostic value was constructed with 6 SERGs (ZIC2, NFE2, FOXJ1, KLF15, POU3F2 and SPIB) to find patients in high-risk group with significantly worse prognosis in both training and testing sets. In addition, a multivariate regression was established via integrating the 6 genes with age and N stage, indicating well performance by calibration, time-dependent receiver operating characteristic (ROC) analysis and decision curve analysis (DCA). Further analysis demonstrated that tumor-associated pathological processes and pathways were significantly enriched in the high-risk group. In general, the novel SERGs signature could be applied to screen breast cancer with immunosuppressive microenvironment for the risk score was negatively correlated with ESTIMATE score, tumor-infiltration lymphocytes (such as CD4 + and CD8 + T cell), immune checkpoints and chemotactic factors. Furthermore, down-regulation of ZIC2 gene expression inhibited the cell viability, cellular migration and cell cycle of breast cancer cells.

CONCLUSIONS

The novel SERGs signature could predict the prognosis of breast cancer; and SERGs might serve as potential therapeutic targets for breast cancer.

LINC01116 affects patient survival differently and is dissimilarly expressed in ER+ and ER- breast cancer samples

BACKGROUND

Breast cancer is the most commonly detected cancer and one of the leading causes of cancer mortality. Emerging evidence supports that aberrant expression of lncRNAs is correlated with tumor progression and various aspects of tumor development.

AIM

This study aimed to evaluate the expression pattern of LINC01116 in breast cancer tissues and investigate the impact of LINC01116 on patients' survival.

METHODS AND RESULTS

Microarray and qRT-PCR data analysis were performed, and the KM-plotter database was used in this study. In addition, the gain of function approach was performed to examine the effect of LINC01116 on breast cancer cells in-vitro. The results exhibited that LINC01116 is meaningfully upregulated in the ER+ tumor specimens compared to the ER- ones. Also, relative to normal tissues, the expression of LINC01116 in ER+ and ER- tumor tissues significantly increased and decreased, respectively. ROC curve analysis revealed the power of LINC01116 in distinguishing ER+ from ER- samples. Additionally, the Kaplan-Meier survival analysis showed that the LINC01116 expression positively correlates with survival probability in all as well as ER+ patients. However, this correlation was negative in ER- patients. Furthermore, our results showed that the overexpression of LINC01116 induces TGF-β signaling in ER- cells (MDA-MB-231), and microarray data analysis revealed that LINC01116 is significantly upregulated in 17β-Estradiol treated MCF7 cells.

CONCLUSION

In conclusion, our results suggest that LINC01116 can be a potential biomarker in distinguishing ER+ and ER- tissues and has different effects on patients' survival based on ER status by affecting TGF-β and ER signaling.

Identification of cell subpopulations associated with disease phenotypes from scRNA-seq data using PACSI

BACKGROUND

Single-cell RNA sequencing (scRNA-seq) has revolutionized the transcriptomics field by advancing analyses from tissue-level to cell-level resolution. Despite the great advances in the development of computational methods for various steps of scRNA-seq analyses, one major bottleneck of the existing technologies remains in identifying the molecular relationship between disease phenotype and cell subpopulations, where "disease phenotype" refers to the clinical characteristics of each patient sample, and subpopulation refer to groups of single cells, which often do not correspond to clusters identified by standard single-cell clustering analysis. Here, we present PACSI, a method aimed at distinguishing cell subpopulations associated with disease phenotypes at the single-cell level.

RESULTS

PACSI takes advantage of the topological properties of biological networks to introduce a proximity-based measure that quantifies the correlation between each cell and the disease phenotype of interest. Applied to simulated data and four case studies, PACSI accurately identified cells associated with disease phenotypes such as diagnosis, prognosis, and response to immunotherapy. In addition, we demonstrated that PACSI can also be applied to spatial transcriptomics data and successfully label spots that are associated with poor survival of breast carcinoma.

CONCLUSIONS

PACSI is an efficient method to identify cell subpopulations associated with disease phenotypes. Our research shows that it has a broad range of applications in revealing mechanistic and clinical insights of diseases.

Long non-coding RNAs in retinal neovascularization: current research and future directions

PURPOSE

Retinal neovascularization (RNV) is an intractable pathological hallmark of numerous ocular blinding diseases, including diabetic retinopathy, retinal vein occlusion, and retinopathy of prematurity. However, current therapeutic methods have potential side effects and limited efficacy. Thus, further studies on the pathogenesis of RNV-related disorders and novel therapeutic targets are critically required. Long non-coding RNAs (lncRNAs) have various functions and participate in almost all biological processes in living cells, such as translation, transcription, signal transduction, and cell cycle control. In addition, recent research has demonstrated critical modulatory roles of various lncRNAs in RNV. In this review, we summarize current knowledge about the expression and regulatory functions of lncRNAs related to the progression of pathological RNV.

METHODS

We searched databases such as PubMed and Web of Science to gather and review information from the published literature.

CONCLUSIONS

In general, lncRNA MEG3 attenuates RNV, thus protecting the retina from excessive and dysregulated angiogenesis under high glucose stress. In contrast, lncRNAs MALAT1, MIAT, ANRIL, HOTAIR, HOTTIP, and SNHG16, have been identified as causative molecules in the pathological progression of RNV. Comprehensive and in-depth studies of the roles of lncRNAs in RNV indicate that targeting lncRNAs may be an alternative therapeutic approach in the near future, enabling new options for attenuating RNV progression and treating RNV-related retinal diseases.

Establishment and validation of an aging-related risk signature associated with prognosis and tumor immune microenvironment in breast cancer

BACKGROUND

Breast cancer (BC) is a highly malignant and heterogeneous tumor which is currently the cancer with the highest incidence and seriously endangers the survival and prognosis of patients. Aging, as a research hotspot in recent years, is widely considered to be involved in the occurrence and development of a variety of tumors. However, the relationship between aging-related genes (ARGs) and BC has not yet been fully elucidated.

MATERIALS AND METHODS

The expression profiles and clinicopathological data were acquired in the Cancer Genome Atlas (TCGA) and the gene expression omnibus (GEO) database. Firstly, the differentially expressed ARGs in BC and normal breast tissues were investigated. Based on these differential genes, a risk model was constructed composed of 11 ARGs via univariate and multivariate Cox analysis. Subsequently, survival analysis, independent prognostic analysis, time-dependent receiver operating characteristic (ROC) analysis and nomogram were performed to assess its ability to sensitively and specifically predict the survival and prognosis of patients, which was also verified in the validation set. In addition, functional enrichment analysis and immune infiltration analysis were applied to reveal the relationship between the risk scores and tumor immune microenvironment, immune status and immunotherapy. Finally, multiple datasets and real-time polymerase chain reaction (RT-PCR) were utilized to verify the expression level of the key genes.

RESULTS

An 11-gene signature (including FABP7, IGHD, SPIB, CTSW, IGKC, SEZ6, S100B, CXCL1, IGLV6-57, CPLX2 and CCL19) was established to predict the survival of BC patients, which was validated by the GEO cohort. Based on the risk model, the BC patients were divided into high- and low-risk groups, and the high-risk patients showed worse survival. Stepwise ROC analysis and Cox analyses demonstrated the good performance and independence of the model. Moreover, a nomogram combined with the risk score and clinical parameters was built for prognostic prediction. Functional enrichment analysis revealed the robust relationship between the risk model with immune-related functions and pathways. Subsequent immune microenvironment analysis, immunotherapy, etc., indicated that the immune status of patients in the high-risk group decreased, and the anti-tumor immune function was impaired, which was significantly different with those in the low-risk group. Eventually, the expression level of FABP7, IGHD, SPIB, CTSW, IGKC, SEZ6, S100B, CXCL1, IGLV6-57 and CCL19 was identified as down-regulated in tumor cell line, while CPLX2 up-regulated, which was mostly similar with the results in TCGA and Human Protein Atlas (HPA) via RT-PCR.

CONCLUSIONS

In summary, our study constructed a risk model composed of ARGs, which could be used as a solid model for predicting the survival and prognosis of BC patients. Moreover, this model also played an important role in tumor immunity, providing a new direction for patient immune status assessment and immunotherapy selection.

Prognostic Factors for Bone Survival and Functional Outcomes in Patients With Breast Cancer Spine Metastases

According to the Global Cancer Statistics 2020 report, breast cancer is the most commonly diagnosed cancer worldwide. Patients with mammary cancer live longer due to the continuous optimization of chemotherapy, targeted drugs, and hormone therapy, which will inevitably lead to an increase in the prevalence of metastatic bone tumors. Bone metastasis affects approximately 8% of patients with mammary cancer, with the spine being the most common site. Metastatic neoplasms can invade the centrum and its attachments, leading to local pain, spinal instability, vertebral pathological fractures, spinal cord compression, impaired neurological function, and paralysis, ultimately reducing the quality of life. Multidisciplinary and personalized management using analgesic drugs, endocrine therapy, corticosteroid therapy, chemotherapy, bisphosphonates, immunotherapy, targeted drugs, radiotherapy, and surgery has been advocated for the treatment of spinal metastases. Multiple paradigms and systems have been proposed to determine suitable treatments. In the early stages, the occurrence of metastasis indicates a terminal stage of the tumor process in patients with malignant tumors, implying that their lifespan is limited. As a result, the choice of treatment is heavily influenced by longevity. However, with the development of treatment methods, the lifespan of patients with tumors has considerably increased in recent years. This leads to the choice of patient's treatment, which depends not only on the patient's survival, but also on the radiotherapy or postoperative functional outcomes. Nevertheless, they fall short of determining the variables that affect survival and functional outcomes in histology-specific subgroups of breast cancer. To accurately predict the bone survival and functional outcomes of patients with breast cancer spine metastases a review of prognostic factors was performed.

The REEP family of proteins: molecular targets and role in pathophysiology

Receptor expression-enhancing proteins (REEPs) are an evolutionarily conserved protein family that is pivotal to the structure and function of the endoplasmic reticulum (ER). The REEP family can be classified into two major subfamilies in higher species, the REEP1-4 and REEP5-6 subfamilies. Within the REEP1-4 subfamily, REEP1 and REEP2 are closely related, and REEP3 and REEP4 are similarly related. The REEP family is widely distributed in various tissues. Recent studies indicate that the REEP family is involved in many pathological and physiological processes, such as ER morphogenesis and remodeling, microtubule cytoskeleton regulation, and the trafficking and expression of G protein-coupled receptors (GPCRs). Moreover, the REEP family plays crucial roles in the occurrence and development of many diseases, including neurological diseases, diabetes, retinal diseases, cardiac diseases, infertility, obesity, oligoarticular juvenile idiopathic arthritis (OJIA), COVID-19, and cancer. In the present review, we describe the distribution and structure of the REEP family. Furthermore, we summarize the functions and the associated diseases of this family. Based on the pleiotropic actions of the REEP family, the study of its family members is crucial to understanding the relevant pathophysiological processes and developing strategies to modulate and control these related diseases. AVAILABILITY OF DATA AND MATERIAL: The datasets used or analyzed during the current study are available from the corresponding author on reasonable request.

A longitudinal evaluation of alcohol intake throughout adulthood and colorectal cancer risk

BACKGROUND

Alcohol intake is an established risk factor for colorectal cancer (CRC); however, there is limited knowledge on whether changing alcohol drinking habits during adulthood modifies CRC risk.

OBJECTIVE

Leveraging longitudinal exposure assessments on alcohol intake at different ages, we examined the relationship between change in alcohol intake and subsequent CRC risk.

METHODS

Within the European Prospective Investigation into Cancer and Nutrition, changes in alcohol intake comparing follow-up with baseline assessments were investigated in relation to CRC risk. The analysis included 191,180, participants and 1530 incident CRC cases, with exclusion of the first three years of follow-up to minimize reverse causation. Trajectory profiles of alcohol intake, assessed at ages 20, 30, 40, 50 years, at baseline and during follow-up, were estimated using latent class mixed models and related to CRC risk, including 407,605 participants and 5,008 incident CRC cases.

RESULTS

Mean age at baseline was 50.2 years and the follow-up assessment occurred on average 7.1 years later. Compared to stable intake, a 12 g/day increase in alcohol intake during follow-up was positively associated with CRC risk (HR = 1.15, 95%CI 1.04, 1.25), while a 12 g/day reduction was inversely associated with CRC risk (HR = 0.86, 95%CI 0.78, 0.95). Trajectory analysis showed that compared to low alcohol intake, men who increased their alcohol intake from early- to mid- and late-adulthood by up to 30 g/day on average had significantly increased CRC risk (HR = 1.24; 95%CI 1.08, 1.42), while no associations were observed in women. Results were consistent by anatomical subsite.

CONCLUSIONS

Increasing alcohol intake during mid-to-late adulthood raised CRC risk, while reduction lowered risk.

Nomogram Models Based on the Gene Expression in Prediction of Breast Cancer Bone Metastasis

Objective

The aim of this study is to design a weighted co-expression network and build gene expression signature-based nomogram (GESBN) models for predicting the likelihood of bone metastasis in breast cancer (BC) patients.

Methods

Dataset GSE124647 was used as a training set, while GSE16446, GSE45255, and GSE14020 were taken as validation sets. In the training cohort, the limma package in R was adopted to obtain differentially expressed genes (DEGs) between BC nonbone metastasis and bone metastasis patients, which were used for functional enrichment analysis. After weighted co-expression network analysis (WGCNA), univariate Cox regression and Kaplan–Meier plotter analyses were performed to screen potential prognosis-related genes. Then, GESBN models were constructed and evaluated. The prognostic value of the GESBN models was investigated in the GSE124647 dataset, which was validated in GSE16446 and GSE45255 datasets. Further, the expression levels of genes in the models were explored in the training set, which was validated in GSE14020. Finally, the expression and prognostic value of hub genes in BC were explored.

Results

A total of 1858 DEGs were obtained. The WGCNA result showed that the blue module was most significantly related to bone metastasis and prognosis. After survival analyses, GAJ1, SLC24A3, ITGBL1, and SLC44A1 were subjected to construct a GESBN model for overall survival (OS). While GJA1, IGFBP6, MDFI, TGFBI, ANXA2, and SLC24A3 were subjected to build a GESBN model for progression-free survival (PFS). Kaplan–Meier plotter and receiver operating characteristic analyses presented the reliable prediction ability of the models. Cox regression analysis further revealed that GESBN models were independent prognostic predictors for OS and PFS in BC patients. Besides, GJA1, IGFBP6, ITGBL1, SLC44A1, and TGFBI expressions were significantly different between the two groups in GSE124647 and GSE14020. The hub genes had a significant impact on patient prognosis.

Conclusion

Both the four-gene signature and six-gene signature could accurately predict patient prognosis, which may provide novel treatment insights for BC bone metastasis.

Advancing therapy for neuroblastoma

Neuroblastomas are tumours of sympathetic origin, with a heterogeneous clinical course ranging from localized or spontaneously regressing to widely metastatic disease. Neuroblastomas recapitulate many of the features of sympathoadrenal development, which have been directly targeted to improve the survival outcomes in patients with high-risk disease. Over the past few decades, improvements in the 5-year survival of patients with metastatic neuroblastomas, from <20% to >50%, have resulted from clinical trials incorporating high-dose chemotherapy with autologous stem cell transplantation, differentiating agents and immunotherapy with anti-GD2 monoclonal antibodies. The next generation of trials are designed to improve the initial response rates in patients with high-risk neuroblastomas via the addition of immunotherapies, targeted therapies (such as ALK inhibitors) and radiopharmaceuticals to standard induction regimens. Other trials are focused on testing precision medicine strategies for patients with relapsed and/or refractory disease, enhancing the antitumour immune response and improving the effectiveness of maintenance regimens, in order to prolong disease remission. In this Review, we describe advances in delineating the pathogenesis of neuroblastoma and in identifying the drivers of high-risk disease. We then discuss how this knowledge has informed improvements in risk stratification, risk-adapted therapy and the development of novel therapies.

Correlation between amino acid metabolism and self-renewal of cancer stem cells: Perspectives in cancer therapy

Cancer stem cells (CSCs) possess self-renewal and differentiation potential, which may be related to recurrence, metastasis, and radiochemotherapy resistance during tumor treatment. Understanding the mechanisms via which CSCs maintain self-renewal may reveal new therapeutic targets for attenuating CSC resistance and extending patient life-span. Recent studies have shown that amino acid metabolism plays an important role in maintaining the self-renewal of CSCs and is involved in regulating their tumorigenicity characteristics. This review summarizes the relationship between CSCs and amino acid metabolism, and discusses the possible mechanisms by which amino acid metabolism regulates CSC characteristics particularly self-renewal, survival and stemness. The ultimate goal is to identify new targets and research directions for elimination of CSCs.

Development and Validation of Prognostic Nomogram for Elderly Breast Cancer: A Large-Cohort Retrospective Study

Purpose

Our research aims to study the bone metastatic patterns and prognostic outcomes in elderly breast cancer (BC) and to develop elder-specific nomograms.

Methods

We downloaded the data of BC patients between 2010 and 2016 from the Surveillance, Epidemiology, and End Results database. The differences in clinical features and prognosis between young (age < 65) and elderly (age ≥ 65) BC patients were compared. The univariate and multivariate Cox analyses were used to determine the overall survival (OS)- and cancer-specific survival (CSS)-related variables and establish two nomograms of BC patients with bone metastasis (BCBM). The receiver operating characteristic (ROC) curve with area under the curve (AUC), calibration curve, decision curve analysis (DCA), and Kaplan–Meier survival curve were selected to evaluate nomograms.

Results

A total of 230,177 BC patients were enrolled in our research, including 142,025 young and 88,152 elderly patients. The prognosis of elderly BCBM patients was significantly worse than young patients. Age, race, breast subtype, tumor size, tumor grade, brain metastasis, liver metastasis, surgery, and chemotherapy were independent prognostic variables for elderly BCBM patients, including OS and CSS. The AUC values at 12, 18, and 24 months were 0.750, 0.751, and 0.739 for OS nomogram and 0.759, 0.762, and 0.752 for CSS nomogram in the training cohort, which were higher than the AUC values of all single independent prognostic variables. The survival curve showed a distinct prognosis between low-, median- and high-risk groups (p < 0.001). Finally, calibration curves and DCA indicated that both nomograms have favorable performance.

Conclusion

Elderly and young patients presented with different bone metastatic frequencies, clinical features, and prognostic outcomes. Two elder-specific nomograms incorporating nine clinical variables were established and validated to be a valuable predictor for elderly BCBM patients.

Aldehyde dehydrogenase 3B2 promotes the proliferation and invasion of cholangiocarcinoma by increasing Integrin Beta 1 expression

Aldehyde dehydrogenases (ALDHs) play an essential role in regulating malignant tumor progression; however, their role in cholangiocarcinoma (CCA) has not been elucidated. We analyzed the expression of ALDHs in 8 paired tumor and peritumor perihilar cholangiocarcinoma (pCCA) tissues and found that ALDH3B1 and ALDH3B2 were upregulated in tumor tissues. Further survival analysis in intrahepatic cholangiocarcinoma (iCCA, n = 27), pCCA (n = 87) and distal cholangiocarcinoma (dCCA, n = 80) cohorts have revealed that ALDH3B2 was a prognostic factor of CCA and was an independent prognostic factor of iCCA and pCCA. ALDH3B2 expression was associated with serum CEA in iCCA and dCCA, associated with tumor T stage, M stage, neural invasion and serum CA19-9 in pCCA. In two cholangiocarcinoma cell lines, overexpression of ALDH3B2 promoted cell proliferation and clone formation by promoting the G1/S phase transition. Knockdown of ALDH3B2 inhibited cell migration, invasion, and EMT in vitro, and restrained tumor metastasis in vivo. Patients with high expression of ALDH3B2 also have high expression of ITGB1 in iCCA, pCCA, and dCCA at both mRNA and protein levels. Knockdown of ALDH3B2 downregulated the expression of ITGB1 and inhibited the phosphorylation level of c-Jun, p38, and ERK. Meanwhile, knockdown of ITGB1 inhibited the promoting effect of ALDH3B2 overexpression on cell proliferation, migration, and invasion. ITGB1 is also a prognostic factor of iCCA, pCCA, and dCCA and double-positive expression of ITGB1 and ALDH3B2 exhibits better performance in predicting patient prognosis. In conclusion, ALDH3B2 promotes tumor proliferation and metastasis in CCA by regulating the expression of ITGB1 and upregulating its downstream signaling pathway. The double-positive expression of ITGB1 and ALDH3B2 serves as a better prognostic biomarker of CCA.

Incidence, prognosis and nomograms of breast cancer with bone metastases at initial diagnosis: a large population-based study

BACKGROUND

Bone is the most common metastatic site for breast cancer, and patients' condition will deteriorate when it occurs.

METHODS

We performed a retrospective analysis on 6482 breast cancer patients with bone metastases (BCBM), who were selected from the Surveillance, Epidemiology, and End Result (SEER) 18 registry database. The optimal age cut-points were generated by using the X-tile software. By using Cox regression, we selected independent prognostic factors from 21 variables, and plotted a visual nomogram to predict the probability of surviving to the median survival time. We also diagrammed a competing risk nomogram on the basis of competitive risk model.

RESULTS

Compared with other three common metastatic sites, the incidence of bone metastasis was the highest for patients with breast cancer. The incidence of BCBM peaked around the age of 60, and a large majority of patients were between the ages of 50 and 70. The survival rate decreased with age, and the median survival time was about 19 months. Factors of age, race, marital status, grade, human epidermal growth factor receptor-2 (HER2) receptor, hormone receptor, concurrent brain metastasis, concurrent liver metastasis, concurrent lung metastasis, surgery and chemotherapy are strongly related to the prognosis of patients with BCBM. It was revealed that the C-index of the nomogram was 0.72 and the calibration curves showed good agreement between the nomogram prediction and actual observation.

CONCLUSION

Our practical nomograms provide a visual and user-friendly tool in the risk evaluation and prognostic prediction for breast cancer patients with bone metastases.

Screening of genes related to survival prognosis of cervical squamous cell carcinoma and construction of prognosis prediction model

AIM

We aimed to screen for the genes related to survival prognosis of cervical squamous cell carcinoma (CSCC) and then constructed a prognosis prediction model.

METHODS

The GSE63514 dataset was obtained from the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (GEO). The CSCC gene dataset and the GSE44001 dataset were obtained from The Cancer Genome Atlas and NCBI GEO, respectively. The Kaplan-Meier (KM) curve was used to evaluate the association between high and low prognosis that was with the actual survival prognosis information. The Cox proportional hazards model was used to screen out the optimized prognostic-related signature differentially expressed gene (DEG) combinations. Gene set enrichment analysis was used to perform pathway enrichment annotation analysis for DEGs that were related to risk grouping.

RESULTS

In total, 16 399 DEGs were obtained and 23 gene ontology biological processes and 8 Kyoto Encyclopedia of Genes and Genomes pathways were screened. Nine optimized DEG groups related to independent prognosis were selected. The KM curves of pathologic N0 and N1 showed that low-risk group were associated with a better overall survival (p = 1.518e; p = 1.704e-01). The pathways related to risk grouping were cytokine-cytokine receptor interaction, JAK stat signaling pathway, and glycolysis-gluconeogenesis.

CONCLUSION

On the basis of this study, we established a prognostic risk model, which provided a reliable prognostic tool and was of great significance for locating the biomarkers related to survival prognosis in CSCC.

A predictive biomarker panel for bone metastases: Liquid biopsy approach

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Data mining of published microarray datasets directed us to the identification of a multi gene panel involving of 15 genes that are particular to bone metastases.

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Serum exosomal markers HSP90AA1, SPP1, IL3, and PTK2 found in the present study might be useful in detecting the early spread of bone metastases leading to better clinical outcomes.

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This multi-gene panel and their related pathways may assist as promising conclusion predictors using novel approaches of exosome as liquid biopsy and their application in therapeutic targets in breast and lung cancer patients with bone metastases.

Bone metastases is one of the common metastatic site and leading cause of cancer-related mortality in progressive cancer patients. The purpose of the present study is to establish a liquid biopsy based multi-gene classifier and associated signalling pathways for early diagnosis of bone metastases. We used publically available microarray datasets and analysed them in a platform/chip-specific manner using GeneSpring software. Analyses of gene expression datasets identified 15 consistently over-expressed genes with statistical significance. Further, expression profile of same set of 15 genes were compared in breast and lung cancer exosome derived mRNA with (n = 10) and without (n = 10) bone metastases against healthy controls. ROC curve analysis performed individually for all the 15 genes shortlisted the 5 most relevant genes with significant sensitivity and specificity in both cancers. This liquid biopsy-based bone metastases predictor using multi-gene panel is a unique approach with potential clinical applications for effective management of aggressive cancers.

Risk factors, prognostic factors, and nomograms for bone metastasis in patients with newly diagnosed infiltrating duct carcinoma of the breast: a population-based study

Background

Breast cancer is the most common malignancy in women, and it is also the leading cause of death in female patients; the most common pathological type of BC is infiltrating duct carcinoma (IDC). Some nomograms have been developed to predict bone metastasis (BM) in patients with breast cancer. However, there are no studies on diagnostic and prognostic nomograms for BM in newly diagnosed IDC patients.

Methods

IDC patients with newly diagnosed BM from 2010 to 2016 in the Surveillance, Epidemiology and End Results (SEER) database were reviewed. Multivariate logistic regression analysis was used to identify risk factors for BM in patients with IDC. Univariate and multivariate Cox proportional hazards regression analysis were used to explore the prognostic factors of BM in patients with IDC. We then constructed nomograms to predict the risk and prognosis of BM for patients with IDC. The results were validated using bootstrap resampling and retrospective research on 113 IDC patients with BM from 2015 to 2018 at the Affiliated Hospital of Chengde Medical University.

Results

This study included 141,959 patients diagnosed with IDC in the SEER database, of whom 2383 cases were IDC patients with BM. The risk factors for BM in patients with IDC included sex, primary site, grade, T stage, N stage, liver metastasis, race, brain metastasis, breast cancer subtype, lung metastasis, insurance status, and marital status. The independent prognostic factors were brain metastases, race, grade, surgery, chemotherapy, age, liver metastases, breast cancer subtype, insurance status, and marital status. Through calibration, receiver operating characteristic curve and decision curve analyses, we found that the nomogram for predicting the prognosis of IDC patients with BM displayed great performance both internally and externally.

Conclusion

These nomograms are expected to be a precise and personalized tool for predicting the risk and prognosis for BM in patients with IDC. This will help clinicians develop more rational and effective treatment strategies.

Prognostic Significance of a Novel Score Model Based on Preoperative Indicators in Patients with Breast Cancer Spine Metastases (BCSM)

Background

Surgery remains the mainstay of treatment for breast cancer spinal metastasis (BCSM) to relieve symptoms and improve the quality of life of BCSM patients. Therefore, it is important to effectively predict the prognosis of patients to determine whether they can undergo surgical operation. However, the prevalent methods for prognosis evaluation lack specificity and sensitivity for indicated malignancies like breast cancer because they are built on a relatively small number of heterogeneous types of primary tumors. The aim of the present study was to explore a novel predictive model based on the clinical, pathological and blood parameters obtained from BCSM patients before they received surgical intervention.

Methods

Altogether, 144 patients were included in this study. Univariate and multivariate analyses were performed to investigate the significance of preoperative parameters and identify independent factors for prognostic prediction of BCSM. A nomogram for survival prediction was then established and validated. Time-dependent ROC (TDROC) curves were graphed to evaluate the accuracy of the novel model vs other scoring systems including Tomita Score, revised Tokuhashi Score, modified Bauer Score and New England Spinal Metastasis Score. P values <0.05 were considered statistically significant.

Results

Independent factors, including preoperative postmenopausal (P=0.034), visceral metastasIs (P=0.021), preoperative Frankel Score (P=0.001), estrogen receptor status (P=0.014), platelet-to-lymphocyte ratio (P=0.012), lymphocyte-monocyte ratio (P<0.001) and albumin-globulin ratio (P=0.017), were selected into the nomogram model with the C-index of 0.834 (95% CI, 0.789–0.890). TDROC curves showed that the Changzheng Hospital (CZ) Score system had the best performance and exhibited the largest IAUC value in comparison with the other scoring systems.

Conclusion

We constructed a nomogram model known as CZ Score based on the significant factors to predict the prognosis for BCSM patients. The result showed that CZ Score had a better value for prognostic evaluation and surgical decision-making as compared with the other scoring systems.

NAT1 promotes osteolytic metastasis in luminal breast cancer by regulating the bone metastatic niche via NF-κB/IL-1B signaling pathway

Breast cancer is a molecularly heterogeneous disease that can be subdivided into different subtypes. Compared with the other subtypes, luminal breast cancer (LBC) is considered more susceptible to bone metastasis. However, the intrinsic mechanisms remain elusive. Bioinformatics analysis of the preset study showed that N-acetyltransferase 1 (NAT1) was specifically expressed in LBC and closely correlated with bone metastasis. In addition, NAT1 could promote LBC cell migration and clonal formation, induce osteoclast differentiation and raise the Rankl/Opg ratio in osteoblasts. Our in vivo experiment demonstrated that NAT1 promoted LBC bone metastasis and bone destruction, which could be reversed by NAT1 inhibitor treatment. The result of cytokine array showed that NAT1 could significantly over activate the NF-κB signaling pathway and up-regulate the expression of IL-1B, which further worked as downstream factors in these processes. All these results demonstrated NAT1 was up-regulated in LBC and promoted the formation of bone metastatic niche and osteolytic bone metastasis through the NAT1/NF-κB/IL-1B axis. This finding may provide a new pathway to help understand the mechanisms of LBC bone metastasis and suggest a novel therapeutic and diagnostic target for its treatment.

Personal Medicine and Bone Metastases: Biomarkers, Micro-RNAs and Bone Metastases

Bone metastasis is a major cause of morbidity within solid tumours of the breast, prostate, lung and kidney. Metastasis to the skeleton is associated with a wide range of complications including bone fractures, spinal cord compression, hypercalcaemia and increased bone pain. Improved treatments for bone metastasis, such as the use of anti-bone resorptive bisphosphonate agents, within post-menopausal women have improved disease-free survival; however, these treatments are not without side effects. There is thus a need for biomarkers, which will predict the risk of developing the spread to bone within these cancers. The application of molecular profiling techniques, together with animal model systems and engineered cell-lines has enabled the identification of a series of potential bone-metastasis biomarker molecules predictive of bone metastasis risk. Some of these biomarker candidates have been validated within patient-derived samples providing a step towards clinical utility. Recent developments in multiplex biomarker quantification now enable the simultaneous measurement of up to 96 micro-RNA/protein molecules in a spatially defined manner with single-cell resolution, thus enabling the characterisation of the key molecules active at the sites of pre-metastatic niche formation as well as tumour-stroma signalling. These technologies have considerable potential to inform biomarker discovery. Additionally, a potential future extension of these discoveries could also be the identification of novel drug targets within cancer spread to bone. This chapter summarises recent findings in biomarker discovery within the key bone metastatic cancers (breast, prostate, lung and renal cell carcinoma). Tissue-based and circulating blood-based biomarkers are discussed from the fields of genomics, epigenetic regulation (micro-RNAs) and protein/cell-signalling together with a discussion of the potential future development of these markers towards clinical development.

Radiomics Based on CECT in Differentiating Kimura Disease From Lymph Node Metastases in Head and Neck: A Non-Invasive and Reliable Method

Background: Kimura disease may be easily misdiagnosed as malignant tumors such as lymph node metastases based on imaging and clinical symptoms. The aim of this article is to investigate whether the radiomic features and the model based on the features on venous-phase contrast-enhanced CT (CECT) images can distinguish Kimura disease from lymph node metastases in the head and neck.

Methods: A retrospective analysis of 14 patients of head and neck Kimura disease (a total of 38 enlarged lymph nodes) and 39 patients with head and neck lymph node metastases (a total of 39 enlarged lymph nodes), confirmed by biopsy or surgery resection, was conducted. All patients accepted CECT within 10 days before biopsy or surgery resection. Radiomic features based on venous-phase CECT were generated automatically from Artificial-Intelligence Kit (AK) software. All lymph nodes were randomly divided into the training set (n = 54) and testing set (n = 23) in a ratio of 7:3. ANOVA + Mann–Whitney, Spearman correlation, least absolute shrinkage and selection operator, and Gradient Descent were introduced for the reduction of the highly redundant features. Binary logistic regression model was constructed based on the selected features. Receiver operating characteristic was used to evaluate the diagnostic performance of the features and the model. Finally, a nomogram was established for model application.

Results: Seven features were screened out at the end. Significant difference was found between the two groups for all the features with area under the curves (AUCs) ranging from 0.759 to 0.915. The AUC of the model's identification performance was 0.970 in the training group and 0.977 in the testing group. The disease discrimination efficiency of the model was better than that of any single feature.

Conclusions: The radiomic features and the model based on these features on venous-phase CECT images had very good performance for the discrimination between Kimura disease and lymph node metastases in the head and neck.

Current and Emerging Biomarkers Predicting Bone Metastasis Development

Bone is one of the preferential sites of distant metastases from malignant tumors, with the highest prevalence observed in breast and prostate cancers. Patients with bone metastases (BMs) may experience skeletal-related events, such as severe bone pain, pathological fractures, spinal cord compression, and hypercalcemia, with negative effects on the quality of life. In the last decades, a deeper understanding of the molecular mechanisms underlying the BM onset has been gained, leading to the development of bone-targeting agents. So far, most of the research has been focused on the pathophysiology and treatment of BM, with only relatively few studies investigating potential predictors of risk for BM development. The ability to select such "high-risk" patients could allow early identification of those most likely to benefit from interventions to prevent or delay BM. This review summarizes several evidences for the potential use of specific biomarkers able to predict early the BM development.

A complete overview of REEP1: old and new insights on its role in hereditary spastic paraplegia and neurodegeneration

At the end of 19th century, Adolf von Strümpell and Sigmund Freud independently described the symptoms of a new pathology now known as hereditary spastic paraplegia (HSP). HSP is part of the group of genetic neurodegenerative diseases usually associated with slow progressive pyramidal syndrome, spasticity, weakness of the lower limbs, and distal-end degeneration of motor neuron long axons. Patients are typically characterized by gait symptoms (with or without other neurological disorders), which can appear both in young and adult ages depending on the different HSP forms. The disease prevalence is at 1.3-9.6 in 100 000 individuals in different areas of the world, making HSP part of the group of rare neurodegenerative diseases. Thus far, there are no specific clinical and paraclinical tests, and DNA analysis is still the only strategy to obtain a certain diagnosis. For these reasons, it is mandatory to extend the knowledge on genetic causes, pathology mechanism, and disease progression to give clinicians more tools to obtain early diagnosis, better therapeutic strategies, and examination tests. This review gives an overview of HSP pathologies and general insights to a specific HSP subtype called spastic paraplegia 31 (SPG31), which rises after mutation of REEP1 gene. In fact, recent findings discovered an interesting endoplasmic reticulum antistress function of REEP1 and a role of this protein in preventing τ accumulation in animal models. For this reason, this work tries to elucidate the main aspects of REEP1, which are described in the literature, to better understand its role in SPG31 HSP and other pathologies.

Genome-Wide Identification of a Novel Autophagy-Related Signature for Colorectal Cancer

Background:

Plenty of evidence has suggested that autophagy plays a crucial role in the biological processes of cancers. This study aimed to screen autophagy-related genes (ARGs) and establish a novel a scoring system for colorectal cancer (CRC).

Methods:

Autophagy-related genes sequencing data and the corresponding clinical data of CRC in The Cancer Genome Atlas were used as training data set. The GSE39582 data set from the Gene Expression Omnibus was used as validation set. An autophagy-related signature was developed in training set using univariate Cox analysis followed by stepwise multivariate Cox analysis and assessed in the validation set. Then we analyzed the function and pathways of ARGs using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes (KEGG) database. Finally, a prognostic nomogram combining the autophagy-related risk score and clinicopathological characteristics was developed according to multivariate Cox analysis.

Results:

After univariate and multivariate analysis, 3 ARGs were used to construct autophagy-related signature. The KEGG pathway analyses showed several significantly enriched oncological signatures, such as p53 signaling pathway, apoptosis, human cytomegalovirus infection, platinum drug resistance, necroptosis, and ErbB signaling pathway. Patients were divided into high- and low-risk groups, and patients with high risk had significantly shorter overall survival (OS) than low-risk patients in both training set and validation set. Furthermore, the nomogram for predicting 3- and 5-year OS was established based on autophagy-based risk score and clinicopathologic factors. The area under the curve and calibration curves indicated that the nomogram showed well accuracy of prediction.

Conclusions:

Our proposed autophagy-based signature has important prognostic value and may provide a promising tool for the development of personalized therapy.

A gene expression signature-based nomogram model in prediction of breast cancer bone metastases

Breast cancer is prone to form bone metastases and subsequent skeletal‐related events (SREs) dramatically decrease patients’ quality of life and survival. Prediction and early management of bone lesions are valuable; however, proper prognostic models are inadequate. In the current study, we reviewed a total of 572 breast cancer patients in three microarray data sets including 191 bone metastases and 381 metastases‐free. Gene set enrichment analysis (GSEA) indicated less aggressive and low‐grade features of patients with bone metastases compared with metastases‐free ones, while luminal subtypes are more prone to form bone metastases. Five bone metastases‐related genes (KRT23, REEP1, SPIB, ALDH3B2, and GLDC) were identified and subjected to construct a gene expression signature‐based nomogram (GESBN) model. The model performed well in both training and testing sets for evaluation of breast cancer bone metastases (BCBM). Clinically, the model may help in prediction of early bone metastases, prevention and management of SREs, and even help to prolong survivals for patients with BCBM. The five‐gene GESBN model showed some implications as molecular diagnostic markers and therapeutic targets. Furthermore, our study also provided a way for analysis of tumor organ‐specific metastases. To the best of our knowledge, this is the first published model focused on tumor organ‐specific metastases.