Finding biomarkers for non-small cell lung cancer diagnosis and prognosis

Despite of several decades of efforts,lung cancer remains one of most deadly diseases,with a 5-year survival rate approximately 15％ worldwide.In China,the situation is even worse.Although there is no o...     

Identification of potential immunotherapy biomarkers for breast cancer by bioinformatics analysis

Breast cancer is a serious malignancy with a high incidence worldwide and a tendency to relapse. We used integrated bioinformatics analysis to identify potential biomarkers in breast carcinoma in the present study. Microarray data, 127breast tumor samples and 23 non-tumor samples, received from the Gene Expression Omnibus (GEO) dataset; 121 differentially expressed genes (DEGs) were selected. Functional analysis using DAVID revealed that these DEGs were highly gathered in endodermal cell differentiation and proteinaceous extracellular matrix. Five bioactive compounds (prostaglandin J2, tanespimycin, semustine, 5182598, and flunarizine) were identified using Connectivity Map. We used Cytoscape software and STRING dataset to structure a protein–protein interaction (PPI) network. The expression of CD24, MMP1, SDC1, and SPP1 was much higher in breast carcinoma tissue than in Para cancerous tissues analyzed by Gene Expression Profiling Interactive Analysis (GEPIA) and ONCOMINE. Overexpression ofCD24, MMP1, SDC1, and SPP1 indicated the poor prognosis in breast carcinoma patients analyzed by Kaplan–Meier (KM) Plotter. Immunohistochemistry microarray was used to further confirm that protein expression of CD24, MMP1, SDC1, and SPP1 was much higher in tumor sections than in Para cancerous tissues. Hub genes expression at the protein level was correlated tothe breast cancer subtype and grade. Furthermore, immunity analysis showed that CD24, MMP1, SDC1, and SPP1 were potentially associated with five immune cell types infiltration (CD8+ T cells, CD4+ T cells, neutrophils, macrophages,and dendritic cells) by TIMER. Thus, this study indicates potential biomarkers that could have applications in the development of immune therapy for breast cancer. However, further studies are required for verifying these results in vivo and vitro.     

Serum microRNA biomarkers for detection of non-small cell lung cancer

Non small cell lung cancer (NSCLC) is the leading cause of cancer-related mortality world-wide and the majority of cases are diagnosed at late stages of disease. There is currently no cost-effective screening test for NSCLC, and the development of such a test is a public health imperative. Recent studies have suggested that chest computed tomography screening of patients at high risk of lung cancer can increase survival from disease, however, the cost effectiveness of such screening has not been established. In this Phase I/II biomarker study we examined the feasibility of using serum miRNA as biomarkers of NSCLC using RT-qPCR to examine the expression of 180 miRNAs in sera from 30 treatment naive NSCLC patients and 20 healthy controls. Receiver operating characteristic curves (ROC) and area under the curve were used to identify differentially expressed miRNA pairs that could distinguish NSCLC from healthy controls. Selected miRNA candidates were further validated in sera from an additional 55 NSCLC patients and 75 healthy controls. Examination of miRNA expression levels in serum from a multi-institutional cohort of 50 subjects (30 NSCLC patients and 20 healthy controls) identified differentially expressed miRNAs. A combination of two differentially expressed miRNAs miR-15b and miR-27b, was able to discriminate NSCLC from healthy controls with sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of 100% in the training set. Upon further testing on additional 130 subjects (55 NSCLC and 75 healthy controls), this miRNA pair predicted NSCLC with a specificity of 84% (95% CI 0.73-0.91), sensitivity of 100% (95% CI; 0.93-1.0), NPV of 100%, and PPV of 82%. These data provide evidence that serum miRNAs have the potential to be sensitive, cost-effective biomarkers for the early detection of NSCLC. Further testing in a Phase III biomarker study in is necessary for validation of these results.     

免疫细胞浸润在非小细胞肺癌诊断与预后中的应用

免疫细胞浸润对癌症的诊断与预后有着重要意义。文中收集TCGA数据库已收录的非小细胞肺癌肿瘤与正常组织基因表达数据,利用CIBERSORT工具得到22种免疫细胞占比来评估免疫细胞浸润情况。以22种免疫细胞占比为特征,用机器学习方法构建了非小细胞肺癌肿瘤与正常组织的分类模型,其中随机森林方法构建的模型分类效果AUC=0.987、敏感性0.98及特异性0.84。并且用随机森林方法构建的肺腺癌和肺鳞癌肿瘤组织分类模型效果AUC=0.827、敏感性0.75及特异性0.77。用LASSO回归筛选22种免疫细胞特征,保留8种强相关特征组成的免疫细胞评分结合临床特征构建了非小细胞肺癌预后模型。经评估及验证,预后模型C-index=0.71并且3年和5年的校准曲线拟合良好,可以对预后风险度进行准确预测。本研究基于免疫细胞浸润所构建的分类模型与预后模型,旨在对非小细胞肺癌的诊断与预后研究提供新的策略。     

Integrative analysis identifies potential DNA methylation biomarkers for pan-cancer diagnosis and prognosis

DNA methylation status is closely associated with diverse diseases, and is generally more stable than gene expression, thus abnormal DNA methylation could be important biomarkers for tumor diagnosis, treatment and prognosis. However, the signatures regarding DNA methylation changes for pan-cancer diagnosis and prognosis are less explored. Here we systematically analyzed the genome-wide DNA methylation patterns in diverse TCGA cancers with machine learning. We identified seven CpG sites that could effectively discriminate tumor samples from adjacent normal tissue samples for 12 main cancers of TCGA (1216 samples, AUC > 0.99). Those seven potential diagnostic biomarkers were further validated in the other 9 different TCGA cancers and 4 independent datasets (AUC > 0.92). Three out of the seven CpG sites were correlated with cell division, DNA replication and cell cycle. We also identified 12 CpG sites that can effectively distinguish 26 different cancers (7605 samples), and the result was repeatable in independent datasets as well as two disparate tumors with metastases (micro-average AUC > 0.89). Furthermore, a series of potential signatures that could significantly predict the prognosis of tumor patients for 7 different cancer were identified via survival analysis (p-value < 1e-4). Collectively, DNA methylation patterns vary greatly between tumor and adjacent normal tissues, as well as among different types of cancers. Our identified signatures may aid the decision of clinical diagnosis and prognosis for pan-cancer and the potential cancer-specific biomarkers could be used to predict the primary site of metastatic breast and prostate cancers.     

Enriched sera protein profiling for detection of non-small cell lung cancer biomarkers

Background

Non Small Cell Lung Cancer (NSCLC) is the major cause of cancer related-death. Many patients receive diagnosis at advanced stage leading to a poor prognosis. At present, no satisfactory screening tests are available in clinical practice and the discovery and validation of new biomarkers is mandatory. Surface Enhanced Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (SELDI-ToF-MS) is a recent high-throughput technique used to detect new tumour markers. In this study we performed SELDI-ToF-MS analysis on serum samples treated with the ProteoMiner? kit, a combinatorial library of hexapeptide ligands coupled to beads, to reduce the wide dynamic range of protein concentration in the sample. Serum from 44 NSCLC patients and 19 healthy controls were analyzed with IMAC30-Cu and H50 ProteinChip Arrays.

Results

Comparing SELDI-ToF-MS protein profiles of NSCLC patients and healthy controls, 28 protein peaks were found significantly different (p < 0.05), and were used as predictors to build decision classification trees. This statistical analysis selected 10 protein peaks in the low-mass range (2-24 kDa) and 6 in the high-mass range (40-80 kDa). The classification models for the low-mass range had a sensitivity and specificity of 70.45% (31/44) and 68.42% (13/19) for IMAC30-Cu, and 72.73% (32/44) and 73.68% (14/19) for H50 ProteinChip Arrays.

Conclusions

These preliminary results suggest that SELDI-ToF-MS protein profiling of serum samples pretreated with ProteoMiner? can improve the discovery of protein peaks differentially expressed between NSCLC patients and healthy subjects, useful to build classification algorithms with high sensitivity and specificity. However, identification of the significantly different protein peaks needs further study in order to provide a better understanding of the biological nature of these potential biomarkers and their role in the underlying disease process.     

Application of radiography of computed tomography in non-small cell lung cancer using prognosis model

ObjectiveStudying the diagnostic value of CT imaging in non-small cell lung cancer (NSCLC), and establishing a prognosis model combined with clinical characteristics is the objective, so as to provide a reference for the survival prediction of NSCLC patients.MethodCT scan data of NSCLC 200 patients were taken as the research object. Through image segmentation, the radiology features of CT images were extracted. The reliability and performance of the prognosis model based on the optimal feature number of specific algorithm and the prognosis model based on the global optimal feature number were compared.Results30-RELF-NB (30 optimal features, RELF feature selection algorithm and NB classifier) has the highest accuracy and AUC (area under the subject characteristic curve) in the prognosis model based on the optimal features of specific algorithm. Among the prognosis models based on global optimal features, 25-NB (25 global optimal features, naive Bayes classification algorithm classifier) has the highest accuracy and AUC. Compared with the prediction model based on feature training of specific feature selection algorithm, the overall performance and stability of the prediction model based on global optimal feature are higher.ConclusionThe prognosis model based on the global optimal feature established in this paper has good reliability and performance, and can be applied to the CT radiology of NSCLC.     

Apoptosis in non-small cell lung cancer as related to drug resistance and prognosis

The percentage of apoptotic cells among the tumor cells (apoptotic index) was determined in a series of 178 non-small cell lung carcinomas with a long term clinical follow-up by a terminal desoxynucleotidyl transferase mediated dUTP nick end labelling technique. In survival analysis, we found no statistically significant correlation between the apoptotic index and survival times. We estimated also the sensitivity of specimens to doxorubicin by a short term test. Tumors with a high apoptotic activity were more sensitive to doxorubicin than tumors with a less apoptotic index. Thus, our data indicate that apoptosis may be involved in drug response of lung tumors and it could be useful for the chemotherapeutic strategy to design drugs which trigger apoptosis.     

On the identification of biomarkers for non-small cell lung cancer in serum and pleural effusion

The current imperative need for new biomarkers of non-small cell lung cancer (NSCLC) prompted us to compare the proteome of serum and pleural effusion samples from cancer patients with those with benign lung diseases as pneumonia or tuberculosis. Samples were prefractionated through affinity chromatography prior to 2D-DIGE to detect proteins with altered expression in cancer patients. Overall, we identified more potential biomarkers in pleural effusion, which is closer to the affected organ, than in serum. Nevertheless, in both cases principal component analysis demonstrated that the pattern of significantly altered proteins discriminates between disease groups.The biomarker candidates comprise proteins increased in malignant pleural effusions as gelsolin and the metalloproteinase inhibitor 2, and others with lower levels as S100-A8 and S100-A9. The most interesting protein was the pigment epithelium-derived factor (PEDF), which is related to angiogenesis inhibition, and was significantly overexpressed both in serum and pleural effusion from NSCLC patients. More than 12 PEDF isoforms were specifically immunodetected in both fluids in 2-D blots, most of them overexpressed in NSCLC. Thus, further validation would be ideally directed to quantify individual PEDF isoforms, as it may be only one or some of them the ones altered in the cancer process.     

Potential application of non-small cell lung cancer-associated autoantibodies to early cancer diagnosis

To identify a panel of tumor associated autoantibodies which can potentially be used as biomarkers for the early diagnosis of non-small cell lung cancer (NSCLC). Thirty-five unique and in-frame expressed phage proteins were isolated. Based on the gene expression profiling, four proteins were selected for further study. Both receiver operating characteristic curve analysis and leave-one-out method revealed that combined measurements of four antibodies produced have better predictive accuracies than any single marker alone. Leave-one-out validation also showed significant relevance with all stages of NSCLC patients. The panel of autoantibodies has a high potential for detecting early stage NSCLC.     

Epigenetic therapy approaches in non-small cell lung cancer: Update and perspectives

Non-small cell lung cancer (NSCLC) still constitutes the most common cancer-related cause of death worldwide. All efforts to introduce suitable treatment options using chemotherapeutics or targeted therapies have, up to this point, failed to exhibit a substantial effect on the 5-year-survival rate. The involvement of epigenetic alterations in the evolution of different cancers has led to the development of epigenetics-based therapies, mainly targeting DNA methyltransferases (DNMTs) and histone-modifying enzymes. So far, their greatest success stories have been registered in hematologic neoplasias. As the effects of epigenetic single agent treatment of solid tumors have been limited, the investigative focus now lies on combination therapies of epigenetically active agents with conventional chemotherapy, immunotherapy, or kinase inhibitors. This review includes a short overview of the most important preclinical approaches as well as an extensive discussion of clinical trials using epigenetic combination therapies in NSCLC, including ongoing trials. Thus, we are providing an overview of what lies ahead in the field of epigenetic combinatory therapies of NSCLC in the coming years.     

Identification of isocitrate dehydrogenase 1 as a potential diagnostic and prognostic biomarker for non-small cell lung cancer by proteomic analysis

Lung cancer is the leading cause of cancer-related death in the world. To explore tumor biomarkers for clinical application, two-dimensional fluorescence difference gel electrophoresis and subsequent MALDI-TOF/TOF mass spectrometry were performed to identify proteins differentially expressed in 12 pairs of lung squamous cell tumors and their corresponding normal tissues. A total of 28 nonredundant proteins were identified with significant alteration in lung tumors. The up-regulation of isocitrate dehydrogenase 1 (IDH1), superoxide dismutase 2, 14-3-3ε, and receptor of activated protein kinase C1 and the down-regulation of peroxiredoxin 2 in tumors were validated by RT-PCR and Western blot analysis in independent 15 pairs of samples. Increased IDH1 expression was further verified by the immunohistochemical study in extended 73 squamous cell carcinoma and 64 adenocarcinoma clinical samples. A correlation between IDH1 expression and poor overall survival of non-small cell lung cancer (NSCLC) patients was observed. Furthermore, ELISA analysis showed that the plasma level of IDH1 was significantly elevated in NSCLC patients compared with benign lung disease patients and healthy individuals. In addition, knockdown of IDH1 by RNA interference suppressed the proliferation of NSCLC cell line and decreased the growth of xenograft tumors in vivo. These observations suggested that IDH1, as a protein promoting tumor growth, could be used as a plasma biomarker for diagnosis and a histochemical biomarker for prognosis prediction of NSCLC.     

Proteomic approaches to identify blood-based biomarkers for depression and bipolar disorders

Introduction: Major depressive disorder (MDD) and bipolar disorder (BD) are leading causes of disability worldwide, yet many people remain undiagnosed, are misdiagnosed, and/or ineffectively treated. Diagnosis relies on the clinical assessment of symptoms, and there is currently no molecular or brain-imaging diagnostic test available. Identifying and validating protein biomarkers could provide a more accurate and objective means of diagnosis.

Areas covered: Proteomics is a powerful tool that enables the identification and quantification of novel candidate biomarkers of disease. In this review, we discuss the role of proteomic technologies in biomarker discovery and validation, peripheral blood as a source of protein biomarkers, statistical methods for analyzing proteomic data, and some existing challenges in the field. We also review a selection of previously published studies focused on identifying blood-based diagnostic protein biomarkers of MDD and BD within a ten-year period.

Expert commentary: Proteomic studies have led to the identification of numerous potential biomarkers of MDD and BD. However, clinical validation and translation into clinical practice have not yet been achieved. Conducting large-scale validation studies and addressing various factors that limit the reproducibility of the proteomic findings are key to ensure that robust and reliable biomarker tests are developed and clinically validated.     

MicroRNAs: Potential biomarkers for cancer diagnosis,prognosis and targets for therapy

MicroRNAs have a revolutionary impact on cancer research over recent years. They emerge as important players in tumorigenesis, leading to a paradigm shift in oncology. The widespread and comprehensive use of microRNA microarrays has enabled the identification of a number of microRNAs as potential biomarkers for cancer. It is encouraging to report that microRNAs have remarkable stability in both formalin-fixed tissue and blood. Many microRNAs have been identified to act as oncogenes, tumor suppressors, or even modulators of cancer stem cells and metastasis. Some studies not only reported the identified microRNA biomarkers, but also deciphered their target genes and the underlying mechanisms. The rapid discovery of many microRNA targets and their relevant pathways has contributed to the development of microRNA-based therapeutics, but the developing progress of antisense or siRNA drugs has been hampered by stability, specificity and delivery problems. This review summarizes the most significant and latest findings of original researches on microRNAs involvement in cancer, focusing on the potential of cancer-related microRNAs as biomarkers for diagnosis, prognosis and targets for therapy.     

Five microRNAs in plasma as novel biomarkers for screening of early-stage non-small cell lung cancer

Background

In order to find novel noninvasive biomarkers with high accuracy for the screening of early-stage non-small cell lung cancer (NSCLC), we investigate the predictive power of 5 microRNAs (miR-20a, miR-145, miR-21, miR223 and miR-221) as potential biomarkers in early-stage NSCLC.

Methods

In training set, 25 early-stage NSCLC patients and 25 matched healthy controls are included to assess the miRNA expression profile between early-stage NSCLC patients and healthy controls by real-time RT-PCR. We found that five of these miRNAs (miR-20a, miR-223, miR-21, miR-221 and miR-145) levels in NSCLC patients were significantly dysregulated compared with the healthy groups and thus were selected to validation set. Therefore, a validation experiment was further performed to investigate the potential predictive power of these five miRNAs based on 126 early-stage NSCLC patients, 42 NCPD patients and 60 healthy controls. The receiver operating characteristic (ROC) curves were generated for the five miRNAs.

Results

ROC curve analyses suggested that these five plasma miRNAs could be promising biomarkers for NSCLC, with relatively high AUC values as follows: miR-20a, 0.89 with 95% CI of [0.85-0.93]; miR-223, 0.94 with 95% CI of [0.91-0.96]; miR-21, 0.77 with 95% CI of [0.71-0.83]; miR-155, 0.92 with 95% CI of [0.89-0.96]; miR-145, 0.77 with 95% CI of [0.71-0.83]. Stratified analyses indicated that plasma miR-20a, miR-223, miR-21 and miR-145 showed better predictive value in smokers than in non-smokers, while miR-155 might be more suitable for non-smokers. In addition, all of these five miRNAs could differentiate NSCLC from controls with a higher accuracy in advanced stage and squamous carcinoma subgroups.

Conclusions

In conclusion, our study suggested that five plasma miRNAs (miR-20a, miR-145, miR-21, miR-223 and miR-221) can be used as promising biomarkers in early screening of NSCLC. Nevertheless, further validation and optimizing improvement should be performed on larger sample to confirm our results.     

Identifying breast cancer subtypes associated modules and biomarkers by integrated bioinformatics analysis

Breast cancer is the most common form of cancer afflicting women worldwide. Patients with breast cancer of different molecular classifications need varied treatments. Since it is known that the development of breast cancer involves multiple genes and functions, identification of functional gene modules (clusters of the functionally related genes) is indispensable as opposed to isolated genes, in order to investigate their relationship derived from the gene co-expression analysis. In total, 6315 differentially expressed genes (DEGs) were recognized and subjected to the co-expression analysis. Seven modules were screened out. The blue and turquoise modules have been selected from the module trait association analysis since the genes in these two modules are significantly correlated with the breast cancer subtypes. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment show that the blue module genes engaged in cell cycle, DNA replication, p53 signaling pathway, and pathway in cancer. According to the connectivity analysis and survival analysis, 8 out of 96 hub genes were filtered and have shown the highest expression in basal-like breast cancer. Furthermore, the hub genes were validated by the external datasets and quantitative real-time PCR (qRT-PCR). In summary, hub genes of Cyclin E1 (CCNE1), Centromere Protein N (CENPN), Checkpoint kinase 1 (CHEK1), Polo-like kinase 1 (PLK1), DNA replication and sister chromatid cohesion 1 (DSCC1), Family with sequence similarity 64, member A (FAM64A), Ubiquitin Conjugating Enzyme E2 C (UBE2C) and Ubiquitin Conjugating Enzyme E2 T (UBE2T) may serve as the prognostic markers for different subtypes of breast cancer.     

Computerized analysis of cellular features and biomarkers for cytologic diagnosis of early lung cancer

OBJECTIVE: To present a set of novel computerized analysis algorithms to construct a computer-aided cytologic diagnosis (CACD) system to differentiate lung cancer biomarkers and identify cancer cells in the tissue-based specimen images. STUDY DESIGN: Molecular methods, including application of cancer-specific markers, may prove to be complementary to cytology diagnosis, especially when they are combined with CACD system for biomarker assessment. We trained a novel CACD system to recognize expression of the cancer biomarkers histone H2AX in lung cancer cells and then tested the accuracy of this system to distinguish resected lung cancer from preneoplastic and normal tissues. The major characteristics of CACD algorithms is to adapt detection parameters according to cellular image contents. Our newly developed wavelet transform is able to adaptively select different resolution and orientation features based on image content requirements. RESULTS: Visual, statistical and quantitative results as CACD performance evaluation are presented in this paper. CONCLUSION: The presented algorithms and CACD system for cellular feature enhancement, segmentation and classification are very important in distinguishing benign and malignant lesions.     

Urinary exosomal long noncoding RNAs serve as biomarkers for early detection of non-small cell lung cancer

Objective: Increasing the efficiency of early diagnosis using noninvasive biomarkers is crucial for enhancing the survival rate of lung cancer patients. We explore the differential expression of non-small cell lung cancer (NSCLC)-related long noncoding RNAs (lncRNAs) in urinary exosomes in NSCLC patients and normal controls to diagnose lung cancer.Methods: A differential expression analysis between NSCLC patients and healthy controls was performed using microarrays. Gene ontology (GO) term and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses were used to predict potential functions of lncRNAs in NSCLC. quantitative real-time PCR (QT-PCR) was used to verify microarray results.Results: A total of 640 lncRNAs (70 up- and 570 down-regulated) were differentially expressed in NSCLC patients in comparison to healthy controls. Six lncRNAs were detected by QT-PCR. GO term and KEGG pathway analyses showed that differential lncRNAs were enriched in cellular component organization or biogenesis, as well as other biological processes and signaling pathways, such as the PI3K-AKT, FOXO, p53, and fatty acid biosynthesis.Conclusions: The differential lncRNAs in urinary exosomes are potential diagnostic biomarkers of NSCLC. The lncRNAs enriched in specific pathways may be associated with tumor cell proliferation, tumor cell apoptosis, and the cell cycle involved in the pathogenesis of NSCLC.     

Neuroendocrine differentiation contributes to radioresistance development and metastatic potential increase in non-small cell lung cancer

Radiation treatment induces neuroendocrine differentiation (NED) in non-small cell lung cancer (NSCLC) A549 and H157 cells, so higher NE-like features in radioresistant A549 (A549R26-1) and H157 (H157R24-1) cells are observed than in parental cells. We detected higher NED marker expressions in A549R26-1 cell-derived tumors than in A549 cell-derived tumors. In mechanism studies, we found that NED induction in A549R26-1 and H157R24-1 cells was accompanied by increased intracellular cAMP and IL-6 levels. Treatment of radioresistant lung cancer cells with the inhibitor (SQ22536) of adenylate cyclase (AC) which is the enzyme responsible for the cAMP production, or the neutralizing antibody (Ab) of IL-6, resulted in decreased NE-like features in radioresistant lung cancer cells. In addition, we found MEK/Erk is the signaling pathway that triggers the cAMP- and IL-6-mediated NED induction in radioresistant lung cancer cells. Also, we found that MEK/Erk signaling pathway inhibition decreased NED in radioresistant cells. Radioresistant lung cancer cells exhibiting high NE-like features also showed higher radioresistance and higher metastatic potential than parental cells. When we inhibited cAMP-, or IL-6-mediated pathways, or the downstream MEK/Erk signaling pathway, radiosensitivity of radioresistant lung cancer cells was significantly increased and their metastatic potential was significantly reduced. In in vivo mouse studies, reducing NED by treating mice with the MEK/Erk inhibitor increased radiosensitivity. Immunohistochemical staining of tumor tissues lowered expressions of the NED/epithelial-mesenchymal transition (EMT)/metastatic markers when mice were treated with the MEK/Erk inhibitor.