An 8-gene signature for prediction of prognosis and chemoresponse in non-small cell lung cancer

5-mRNA-based prognostic signature of survival in lung adenocarcinoma

BACKGROUND

Lung adenocarcinoma (LUAD) is the most common non-small-cell lung cancer, with a high incidence and a poor prognosis.

AIM

To construct effective predictive models to evaluate the prognosis of LUAD patients.

METHODS

In this study, we thoroughly mined LUAD genomic data from the Gene Expression Omnibus (GEO) (GSE43458, GSE32863, and GSE27262) and the Cancer Genome Atlas (TCGA) datasets, including 698 LUAD and 172 healthy (or adjacent normal) lung tissue samples. Univariate regression and LASSO regression analyses were used to screen differentially expressed genes (DEGs) related to patient prognosis, and multivariate Cox regression analysis was applied to establish the risk score equation and construct the survival prognosis model. Receiver operating characteristic curve and Kaplan-Meier survival analyses with clinically independent prognostic parameters were performed to verify the predictive power of the model and further establish a prognostic nomogram.

RESULTS

A total of 380 DEGs were identified in LUAD tissues through GEO and TCGA datasets, and 5 DEGs (TCN1, CENPF, MAOB, CRTAC1 and PLEK2) were screened out by multivariate Cox regression analysis, indicating that the prognostic risk model could be used as an independent prognostic factor (Hazard ratio = 1.520, P < 0.001). Internal and external validation of the model confirmed that the prediction model had good sensitivity and specificity (Area under the curve = 0.754, 0.737). Combining genetic models and clinical prognostic factors, nomograms can also predict overall survival more effectively.

CONCLUSION

A 5-mRNA-based model was constructed to predict the prognosis of lung adenocarcinoma, which may provide clinicians with reliable prognostic assessment tools and help clinical treatment decisions.

Benefits from Adjuvant Chemotherapy in Patients with Resected Non-Small Cell Lung Cancer: Possibility of Stratification by Gene Amplification of ACTN4 According to Evaluation of Metastatic Ability

Surgical treatment is the best curative treatment option for patients with non-small cell lung cancer (NSCLC), but some patients have recurrence beyond the surgical margin even after receiving curative surgery. Therefore, therapies with anti-cancer agents also play an important role perioperatively. In this paper, we review the current status of adjuvant chemotherapy in NSCLC and describe promising perioperative therapies, including molecularly targeted therapies and immune checkpoint inhibitors. Previously reported biomarkers of adjuvant chemotherapy for NSCLC are discussed along with their limitations. Adjuvant chemotherapy after resective surgery was most effective in patients with metastatic lesions located just outside the surgical margin; in addition, these metastatic lesions were the most sensitive to adjuvant chemotherapy. Thus, the first step in predicting patients who have sensitivity to adjuvant therapies is to perform a qualified evaluation of metastatic ability using markers such as actinin-4 (ACTN4). In this review, we discuss the potential use of biomarkers in patient stratification for effective adjuvant chemotherapy and, in particular, the use of ACTN4 as a possible biomarker for NSCLC.

A nine-gene signature to improve prognosis prediction of colon carcinoma

This study aims to establish a gene model that can robustly and effectively predict the prognosis of colon carcinoma patients via bioinformatics. Data along with clinical information in GSE39582 Series Matrix were firstly downloaded from Gene Expression Omnibus (GEO) database. Next, differentially expressed genes (DEGs) were obtained through "edgeR" analysis. Finally, a risk predication model was established through a series of regression analyses, and then prognostic performance of the model was comprehensively evaluated though Kaplan-Meier and receiver operating characteristic (ROC) analysis. Gene set enrichment analysis (GSEA) was further performed. Totally, 846 DEGs were obtained by analyzing the gene expression data in GSE39582 dataset. A 9-gene signature-based risk predication model was established via regression analyses, and the model-based risk score was formulated as: Riskscore = (-0.1214) * TNFRSF11A + (-0.2617) * TMEM97 + (-0.1041) * LGR5 + 0.0973 * KLK10 + 0.1655 * HOXB8 + 0.227 * FKBP10 + (-0.1312) * CXCL13 + (-0.1316) * CXCL10 + 0.2593 * CD36. Kaplan-Meier curve showed that colon carcinoma patients in the high-risk group had a lower survival rate. GSEA showed that high-risk group and low-risk group displayed significant difference in biological pathways including ECM RECEPTOR INTERACTION. Besides, correlation analysis between the riskscore of the model and clinical features of patients revealed that the model could effectively predict the prognosis of patients in different ages (age>65, age<65) and stages (tumor_stage I/II, tumor_stage III/IV, T3&T4, N0&N1, N2&N3, M0). This study provides a robust model for the prognosis prediction of colon carcinoma, and lays a basis for researching the molecular mechanism underlying the development of colon carcinoma.

Pan-Cancer Transcriptional Models Predicting Chemosensitivity in Human Tumors

MOTIVATION

Despite increasing understanding of the molecular characteristics of cancer, chemotherapy success rates remain low for many cancer types. Studies have attempted to identify patient and tumor characteristics that predict sensitivity or resistance to different types of conventional chemotherapies, yet a concise model that predicts chemosensitivity based on gene expression profiles across cancer types remains to be formulated. We attempted to generate pan-cancer models predictive of chemosensitivity and chemoresistance. Such models may increase the likelihood of identifying the type of chemotherapy most likely to be effective for a given patient based on the overall gene expression of their tumor.

RESULTS

Gene expression and drug sensitivity data from solid tumor cell lines were used to build predictive models for 11 individual chemotherapy drugs. Models were validated using datasets from solid tumors from patients. For all drug models, accuracy ranged from 0.81 to 0.93 when applied to all relevant cancer types in the testing dataset. When considering how well the models predicted chemosensitivity or chemoresistance within individual cancer types in the testing dataset, accuracy was as high as 0.98. Cell line-derived pan-cancer models were able to statistically significantly predict sensitivity in human tumors in some instances; for example, a pan-cancer model predicting sensitivity in patients with bladder cancer treated with cisplatin was able to significantly segregate sensitive and resistant patients based on recurrence-free survival times (P = .048) and in patients with pancreatic cancer treated with gemcitabine (P = .038). These models can predict chemosensitivity and chemoresistance across cancer types with clinically useful levels of accuracy.

A novel signature predicts recurrence risk and therapeutic response in breast cancer patients

Acetylserotonin O-methyltransferase (ASMT) is a key enzyme in the synthesis of melatonin. Although melatonin has been shown to exhibit anticancer activity and prevents endocrine resistance in breast cancer, the role of ASMT in breast cancer progression remains unclear. In this retrospective study, we analyzed gene expression profiles in 27 data sets on 7244 patients from 11 countries. We found that ASMT expression was significantly reduced in breast cancer tumors relative to healthy tissue. Among breast cancer patients, those with higher levels of ASMT expression had better relapse-free survival outcomes and longer metastasis-free survival times. Following treatment with tamoxifen, patients with greater ASMT expression experienced longer periods before relapse or distance recurrence. Motivated by these results, we devised an ASMT gene signature that can correctly identify low-risk cases with a sensitivity and specificity of 0.997 and 0.916, respectively. This signature was robustly validated using 23 independent breast cancer mRNA array data sets from different platforms (consisting of 5800 patients) and an RNAseq data set from TCGA (comprising 1096 patients). Intriguingly, patients who are classified as high-risk by the signature benefit from adjuvant chemotherapy, and those with grade II tumors who are classified as low-risk exhibit improved overall survival and distance relapse-free outcomes following endocrine therapy. Together, our findings more clearly elucidate the roles of ASMT, provide strategies for improving the efficacy of tamoxifen treatment and help to identify those patients who may maximally benefit from adjuvant or endocrine therapies.

Tsukushi is a novel prognostic biomarker and correlates with tumor-infiltrating B cells in non-small cell lung cancer

A recent study has reported that tsukushi (TSKU) may be related to the development of lung cancer. However, few studies focused on if TSKU associated with the prognosis and immune infiltration cells in non-small cell lung cancer (NSCLC). The effect of TSKU expression on prognosis with NSCLC was analyzed in the PrognoScan database and validated in The Cancer Genome Atlas. The composition of tumor infiltrating cells was quantified by methylation and expression data. We combined levels of tumor infiltrating cells with TSKU to evaluate the survival of patients. The analysis of a cohort (GSE31210, N=204) of lung cancer patients demonstrated that high TSKU expression was strongly associated with poor overall survival (P =1.90E-05). The combination of high TSKU expression and low infiltration B cells identified a subtype of patients with poor survival in NSCLC. Besides, the proportion of B cells in NSCLC patients with TSKU hypermethylation were higher than those patients with TSKU hypomethylation (P <0.001). Overall, high TSKU expression combined with low infiltration of B cells may associate with a poor prognosis of NSCLC patients. TSKU might be a potential prognostic biomarker involved in tumor immune infiltration in NSCLC.

Denoising Autoencoder, A Deep Learning Algorithm, Aids the Identification of A Novel Molecular Signature of Lung Adenocarcinoma

Precise biomarker development is a key step in disease management. However, most of the published biomarkers were derived from a relatively small number of samples with supervised approaches. Recent advances in unsupervised machine learning promise to leverage very large datasets for making better predictions of disease biomarkers. Denoising autoencoder (DA) is one of the unsupervised deep learning algorithms, which is a stochastic version of autoencoder techniques. The principle of DA is to force the hidden layer of autoencoder to capture more robust features by reconstructing a clean input from a corrupted one. Here, a DA model was applied to analyze integrated transcriptomic data from 13 published lung cancer studies, which consisted of 1916 human lung tissue samples. Using DA, we discovered a molecular signature composed of multiple genes for lung adenocarcinoma (ADC). In independent validation cohorts, the proposed molecular signature is proved to be an effective classifier for lung cancer histological subtypes. Also, this signature successfully predicts clinical outcome in lung ADC, which is independent of traditional prognostic factors. More importantly, this signature exhibits a superior prognostic power compared with the other published prognostic genes. Our study suggests that unsupervised learning is helpful for biomarker development in the era of precision medicine.

Identification of Prognosis-Related Genes in Bladder Cancer Microenvironment across TCGA Database

Background

Bladder cancer (BCa) is a common urothelial malignancy. The Cancer Genome Atlas (TCGA) database allows for an opportunity to analyze the relationship between gene expression and clinical outcomes in bladder cancer patients. This study is aimed at identifying prognosis-related genes in the bladder cancer microenvironment.

Methods

Immune scores and stromal scores were calculated by applying the ESTIMATE algorithm. We divided bladder cancer patients into high and low groups based on their immune/stromal scores. Then, differentially expressed genes (DEGs) were identified in bladder cancer patients based on the TCGA database. We evaluated the correlation between immune/stromal scores and clinical characteristics as well as prognosis. Finally, we validated identified genes associated with bladder cancer prognosis through a cohort study in the Gene Expression Omnibus (GEO) database.

Results

A higher stromal score was associated with female (vs. malep = 0.037), age > 65 (vs.age ≤ 65 p = 0.015), T3/4 (vs. T1/2,p < 0.001), N status(p = 0.016), and pathological high grade (vs. low gradeP < 0.001). By analyzing DEGs, there were 1125 genes commonly upregulated, and 209 genes were commonly downregulated. Protein-protein interaction networks further showed the important protein that may be involved in the biological behavior and prognosis of BCa, such as FN1, CXCL12, CD3E, LCK, and ZAP70. A total of 14 DEGs were found to be associated with overall survival of bladder cancer. After validation by a cohort of 165 BCa cases with detailed follow-up information from GSE13507, 10 immune-associated DEGs were demonstrated to be predictive of prognosis in BCa. Among them, 5 genes have not been reported previously associated with the prognosis of BCa, including BTBD16, OLFML2B, PRRX1, SPINK4, and SPON2.

Conclusions

Our study elucidated tight associations between stromal score and clinical characteristics as well as prognosis in BCa. Moreover, we obtained a group of genes closely related to the prognosis of BCa in the tumor microenvironment.

A Novel Six-Gene Signature for Prognosis Prediction in Ovarian Cancer

Ovarian cancer (OC) is the most malignant tumor in the female reproductive tract. Although abundant molecular biomarkers have been identified, a robust and accurate gene expression signature is still essential to assist oncologists in evaluating the prognosis of OC patients. In this study, samples from 367 patients in The Cancer Genome Atlas (TCGA) database were subjected to mRNA expression profiling. Then, we used a gene set enrichment analysis (GSEA) to screen genes correlated with epithelial–mesenchymal transition (EMT) and assess their prognostic power with a Cox proportional regression model. Six genes (TGFBI, SFRP1, COL16A1, THY1, PPIB, BGN) associated with overall survival (OS) were used to construct a risk assessment model, after which the patients were divided into high-risk and low-risk groups. The six-gene signature was an independent prognostic biomarker of OS for OC patients based on the multivariate Cox regression analysis. In addition, the six-gene model was validated with samples from the Gene Expression Omnibus (GEO) database. In summary, we established a six-gene signature relevant to the prognosis of OC, which might become a therapeutic tool with clinical applications in the future.

A 70-Gene Signature for Predicting Treatment Outcome in Advanced-Stage Cervical Cancer

Cervical cancer is the fourth most common cancer in women worldwide. The current approaches still have limitations in predicting the therapy outcome of each individual because of cancer heterogeneity. The goal of this study was to establish a gene expression signature that could help when choosing the right therapeutic method for the treatment of advanced-stage cervical cancer. The 666 patients were collected from four independent datasets. The 70-gene expression signature was established using univariate Cox proportional hazard regression analysis. The 70-gene signature was significantly different between low- and high-risk groups in the training dataset (p = 4.24e-6) and in the combined three validation datasets (p = 4.37e-3). Treatment of advanced-stage cancer patients in the high-risk group with molecular-targeted therapy combined with chemoradiotherapy yielded a better survival rate than with only chemoradiotherapy (p = 0.0746). However, treatment of the patients in the low-risk group with the combined therapy resulted in significantly lower survival (p = 0.00283). Functional classification of 70 genes revealed involvement of the angiogenesis pathway, specifically phosphatidylinositol 3-kinase signaling (p = 0.040), extracellular matrix organization (p = 0.0452), and cell adhesion (p = 0.011). The 70-gene signature could predict the prognosis and indicate an optimal therapeutic modality in molecular-targeted therapy or chemotherapy for advanced-stage cervical cancer.

Bioinformatics Analysis to Screen the Key Prognostic Genes in Tumor Microenvironment of Bladder Cancer

Bladder cancer (BLCA) is the fifth most common cancer and has the features of low survival rate and high morbidity and mortality. The Cancer Genome Atlas (TCGA) is a pool of global gene expression profile and contains huge amounts of cancer genomics data, which makes it possible to inquire the relationship between gene expression and prognosis of a series of malignant tumors including BLCA. Immune and stromal cells are two major components of tumor microenvironment (TME) which play an important role in judging the prognosis of tumor and influencing the progression of malignant, inflammatory, and metabolic disorders. In our study, we conducted a quantitative analysis of immune and stromal elements based on the ESTIMATE algorithm and thus divided BLCA cases into high and low groups. Then the differentially expressed genes closely related to tumor prognosis between groups were identified and had been shown to correlate with immune response and stromal alterations, which was further confirmed by functional enrichment analysis and protein-protein interaction networks. We validated those genes through BLCA dates downloaded from ArrayExpress and thus got the marker genes to predict prognosis of BLCA. Additionally, immune cell infiltration analysis explored the correlation between the verified genes and immune cells. In conclusion, we identified a series of TME-related genes that assess the prognosis and explored the interaction between TME and tumor prognosis to guide clinical individualized treatment.

A 5-Gene Signature Is Closely Related to Tumor Immune Microenvironment and Predicts the Prognosis of Patients with Non-Small Cell Lung Cancer

Purpose

Establishing prognostic gene signature to predict clinical outcomes and guide individualized adjuvant therapy is necessary. Here, we aim to establish the prognostic efficacy of a gene signature that is closely related to tumor immune microenvironment (TIME).

Methods and Results

There are 13,035 gene expression profiles from 130 tumor samples of the non-small cell lung cancer (NSCLC) in the data set GSE103584. A 5-gene signature was identified by using univariate survival analysis and Least Absolute Shrinkage and Selection Operator (LASSO) to build risk models. Then, we used the CIBERSORT method to quantify the relative levels of different immune cell types in complex gene expression mixtures. It was found that the ratio of dendritic cells (DCs) activated and mast cells (MCs) resting in the low-risk group was higher than that in the high-risk group, and the difference was statistically significant (P < 0.001 and P=0.03). Pathway enrichment results which were obtained by performing Gene Set Variation Analysis (GSVA) showed that the high-risk group identified by the 5-gene signature had metastatic-related gene expression, resulting in lower survival rates. Kaplan–Meier survival results showed that patients of the high-risk group had shorter disease-free survival (DFS) and overall survival (OS) than those of the low-risk group in the training set (P=0.0012 and P < 0.001). The sensitivity and specificity of the gene signature were better and more sensitive to prognosis than TNM (tumor/lymph node/metastasis) staging, in spite of being not statistically significant (P=0.154). Furthermore, Kaplan–Meier survival showed that patients of the high-risk group had shorter OS and PFS than those of the low-risk group (P=0.0035, P < 0.001, and P < 0.001) in the validating set (GSE31210, GSE41271, and TCGA). At last, univariate and multivariate Cox proportional hazard regression analyses were used to evaluate independent prognostic factors associated with survival, and the gene signature, lymphovascular invasion, pleural invasion, chemotherapy, and radiation were employed as covariates. The 5-gene signature was identified as an independent predictor of patient survival in the presence of clinical parameters in univariate and multivariate analyses (P < 0.001) (hazard ratio (HR): 3.93, 95% confidence interval CI (2.17–7.1), P=0.001, (HR) 5.18, 95% CI (2.6995–9.945), P < 0.001), respectively. Our 5-gene signature was also related to EGFR mutations (P=0.0111), and EGFR mutations were mainly enriched in low-risk group, indicating that EGFR mutations affect the survival rate of patients.

Conclusion

The 5-gene signature is a powerful and independent predictor that could predict the prognosis of NSCLC patients. In addition, our gene signature is correlated with TIME parameters, such as DCs activated and MCs resting. Our findings suggest that the 5-gene signature closely related to TIME could predict the prognosis of NSCLC patients and provide some reference for immunotherapy.

Genome-wide DNA copy number profiling and bioinformatics analysis of ovarian cancer reveals key genes and pathways associated with distinct invasive/migratory capabilities

Ovarian cancer (OC) metastasis presents major hurdles that must be overcome to improve patient outcomes. Recent studies have demonstrated copy number variations (CNVs) frequently contribute to alterations in oncogenic drivers. The present study used a CytoScan HD Array to analyse CNVs and loss of heterozygosity (LOH) in the entire genomes of 6 OC patients and human OC cell lines to determine the genetic target events leading to the distinct invasive/migratory capacities of OC. The results showed that LOH at Xq11.1 and Xp21.1 and gains at 8q21.13 were novel, specific CNVs. Ovarian cancer-related CNVs were then screened by bioinformatics analysis. In addition, transcription factors-target gene interactions were predicted with information from PASTAA analysis. As a result, six genes (i.e., GAB2, AKT1, EGFR, COL6A3, UGT1A1 and UGT1A8) were identified as strong candidates by integrating the above data with gene expression and clinical outcome data. In the transcriptional regulatory network, 4 known cancer-related transcription factors (TFs) interacted with 6 CNV-driven genes. The protein/DNA arrays revealed 3 of these 4 TFs as potential candidate gene-related transcription factors in OC. We then demonstrated that these six genes can serve as potential biomarkers for OC. Further studies are required to elucidate the pathogenesis of OC.

Prognostic value of immune cells in the tumor microenvironment of early-stage lung cancer: a meta-analysis

BACKGROUND

Early-stage non-small cell lung cancer (NSCLC) patients carry significant risk of recurrence post-surgery. In-depth characterization of the immune tumor microenvironment (TME) can have prognostic value. This study aimed to evaluate the association of individual immune cell types in the TME with clinical outcomes in surgically resected, early-stage NSCLC.

METHODS

We performed a systematic literature search of the National Library of Medicine database through November 2019, investigating predefined biomarkers (CD3+ T cells, CD4+ T helper cells, CD8+ cytotoxic T cells, CD20+ B cells, CD56+ & CD57+ Natural Killer (NK) cells, CD68+ Tissue Associated Macrophages (TAMS), FoxP3+ T regulatory cells, and Mast Cells (MC)), and their association with survival following PRISMA guidelines.

RESULTS

Studies that adjusted for important clinical covariates (such as stage and age) showed that higher levels of CD8+ cytotoxic T cells were associated with improved OS (HR = 0.68; 95% CI, 0.50-0.93) and DFS (HR = 0.60; 95% CI, 0.41-0.87), while increased CD20+ B cells (HR = 0.16; 95% CI, 0.04-0.64) and CD 56/57+ NK cells (HR = 0.50; 95% CI, 0.26-0.95) were associated with improved OS; lung cancers with increased FoxP3+ T regulatory cells (HR = 2.22; 95% CI, 1.47-3.34) had worse OS.

CONCLUSIONS

Immune cell components of the TME have prognostic value in early-stage, surgically resected NSCLC, and may reveal which patients are more likely to need additional systemic treatment, including immunotherapy. Clinical covariates need to be considered when evaluating the prognostic value of immune cells in the TME.

A Six-Gene Signature Predicts Survival of Adenocarcinoma Type of Non-Small-Cell Lung Cancer Patients: A Comprehensive Study Based on Integrated Analysis and Weighted Gene Coexpression Network

Background and Goals. To identify a multigene signature model for prognosis of non-small-cell lung cancer (NSCLC) patients, we first found 2146 consensus differentially expressed genes (DEGs) in NSCLC overlapped in Gene Expression Omnibus (GEO) and TCGA lung adenocarcinoma (LUAD) datasets using integrated analysis. We constructed a weighted gene coexpression network (WGCN) using the consensus DEGs and identified the module significantly associated with pathological M stage and consisted of 61 genes. After univariate Cox regression analysis and subsequent stepwise model selection by the Akaike information criterion (AIC) and multivariate Cox hazard model analysis, an mRNA signature model which calculated prognostic score was generated: prognostic score = (-0.2491 × EXP_RRAGB) + (-0.0679 × EXP_RSPH9) + (-0.2317 × EXP_RPS6KL1) + (-0.1035 × EXP_RXFP1) + 0.1571 × EXP_RRM2 + 0.1104 × EXP_RTL1, where EXP is the fragments per kilobase million (FPKM) value of the mRNA included in the model. The prognostic model separated NSCLC patients in the TCGA-LUAD dataset into the low- and high-risk score groups with a median prognostic score of 0.972. Higher scores predicted higher risk. The area under ROC curve (AUC) was 0.994 or 0.776 in predicting the 1- to 10-year survival of NSCLC patients. The prognostic performance of this prognostic model was validated by an independent GSE11969 dataset of NSCLC adenocarcinoma with AUC values between 0.822 and 0.755 in predicting 1- to 10-year survival of NSCLC. These results suggested that the six-gene signature functioned as an independent biomarker to predict the overall survival of NSCLC adenocarcinoma.

A Pathway-Based Strategy to Identify Biomarkers for Lung Cancer Diagnosis and Prognosis

Current research has identified several potential biomarkers for lung cancer diagnosis or prognosis. However, most of these biomarkers are derived from a relatively small number of samples using algorithms at the gene level. Hence, gene expression signatures discovered in these studies have little overlaps. In this study, we proposed a new strategy to identify biomarkers from multiple datasets at the pathway level. We integrated the genome-wide expression data of lung cancer tissues from 13 published studies and applied our strategy to identify lung cancer diagnostic and prognostic biomarkers. We identified a 32-gene signature that differentiates lung adenocarcinomas from other lung cancer subtypes. We also discovered a 43-gene signature that can predict the outcome of human lung cancers. We tested their performance in several independent cohorts, which confirmed their robust prognostic and diagnostic power. Furthermore, we showed that the proposed gene expression signatures were independent of several traditional clinical indicators in lung cancer management. Our results suggest that the pathway-based strategy is useful to identify transcriptomic biomarkers from large-scale gene expression datasets that were collected from multiple sources.

Comparative genomic analysis of collagen gene diversity

Collagen gene family, comprising 30% of the total protein mass in mammals, is the major part of extracellular matrix. To understand the complexity of collagen gene family, detailed sequence, phylogenetic and synteny analyses of 44 collagen genes were performed. According to sequence analysis results, Fibril-associated collagen with interrupted triple helices (FACITs) were identified as the most recently evolved vertebrate-specific collagens while Fibril-forming collagens and Collagen VI, VII, XXVI, and XXVIII were the most ancient collagens, originating at the time of choanoflagellates. Network-forming collagens were entirely conserved from arthopods to homo sapiens, except one gene loss event. Of note, bird specific gene dispensability of COL1A1, COL3A1, COL5A3 and COL11A2 genes was observed in Fibril-forming collagens. According to phylogenetic analysis, gene duplications in collagen family occurred at variable time points during invertebrate to vertebrate evolution. However, majority of gene duplications in FACITs and network-forming collagens occurred at fish time point, suggesting large scale duplications at the root of vertebrate lineage. Lastly, synteny analysis identified 12 conserved blocks containing 27 collagen genes in vertebrate species. Interestingly, dysregulation of seven conserved blocks including block1 (COL11A1), block3 (COL3A1, COL5A2), block5 (COL6A5, COL6A6), block7 (COL1A2), block9 (COL4A1, COL4A2), block11 (COL6A1, COL6A2, COL18A1) and block12 (COL4A5, COL4A6) were also reported in different diseases including cancer. The current study revealed many critical insights into sequence, structural and functional diversity of collagen gene family. In future, by using this information we may be able to establish the clinical and pathological relevance of these conserved collagen blocks in different diseases.

A targeted genomic alteration analysis predicts survival of melanoma patients under BRAF inhibitors

Several mechanisms have been described to elucidate the emergence of resistance to MAPK inhibitors in melanoma and there is a crucial need for biomarkers to identify patients who are likely to achieve a better and long-lasting response to BRAF inhibitors therapy. In this study, we developed a targeted approach combining both mRNA and DNA alterations analysis focusing on relevant gene alterations involved in acquired BRAF inhibitor resistance. We collected baseline tumor samples from 64 melanoma patients at BRAF inhibitor treatment initiation and showed that the presence, prior to treatment, of mRNA over-expression of genes' subset was significantly associated with improved progression free survival and overall survival. The presence of DNA alterations was in favor of better overall survival. The genomic analysis of relapsed-matched tumor samples from 20 patients allowed us to uncover the largest landscape of resistance mechanisms reported to date as at least one resistance mechanism was identified for each patient studied. Alterations in RB1 have been most frequent and hence represent an important additional acquired resistance mechanism. Our targeted genomic analysis emerges as a relevant tool in clinical practice to identify those patients who are more likely to achieve durable response to targeted therapies and to exhaustively describe the spectrum of resistance mechanisms. Our approach can be adapted to new targeted therapies by including newly identified genetic alterations.

DNA hypomethylation-related overexpression of SFN, GORASP2 and ZYG11A is a novel prognostic biomarker for early stage lung adenocarcinoma

Although alteration of DNA methylation in advanced cancer has been extensively investigated, few data for early-stage lung adenocarcinoma are available. Here, we compared DNA methylation profiles between adenocarcinoma in situ (AIS) and early invasive adenocarcinoma using the Infinium methylation array to investigate methylation abnormalities causing early progression of adenocarcinomas. We focused on differentially methylated sites which were located in promoter CpG islands or shore regions, and identified 579 hypermethylated sites and 23 hypomethylated sites in early invasive adenocarcinoma relative to AIS and normal lung. These hypermethylated genes were significantly associated with neuronal pathways such as the GABA receptor and serotonin signaling pathways. Among the hypomethylated genes, we found that GORASP2, ZYG11A, and SFN had significantly lower methylation rates at the shore regions and significantly higher protein expression in invasive adenocarcinoma. Moreover, overexpression of those proteins was strongly associated with patient’s poor outcome. Despite DNA demethylation at the promoter region might be rare relative to DNA hypermethylation, we identified 2 new genes, GORASP2 and ZYG11A, which show hypomethylation and overexpression in invasive adenocarcinoma, suggesting that they have important functions in tumor cells. These genes may be clinically applicable as prognostic indicators and could be potential novel target molecules for drug development.

Histology-dependent prognostic role of pERK and p53 protein levels in early-stage non-small cell lung cancer

Lung tumors represent a major health problem. In early stage NSCLC tumors, surgical resection is the preferred treatment, but 30-55% of patients will relapse within 5 years after surgery. Thus, the identification of prognostic biomarkers in early stage NSCLC patients, especially those which are therapeutically addressable, is crucial to enhance survival of these patients. We determined the immunohistochemistry expression of key proteins involved in tumorigenesis and oncogenic signaling, p53, EGFR, pAKT and pERK, and correlated their expression level to clinicopathological characteristics and patient outcome. We found EGFR expression is higher in the squamous cell carcinomas than in adenocarcinomas (p=0.043), and that nuclear p53 staining correlated with lower differentiated squamous tumors (p=0.034). Regarding the prognostic potential of the expression of these proteins, high pERK levels proved to be an independent prognostic factor for overall (p<0.001) and progression-free survival (p<0.001) in adenocarcinoma patients, but not in those from the squamous histology, and high p53 nuclear levels were identified as independent prognostic factor for progression-free survival (p=0.031) only in squamous cell carcinoma patients. We propose a role as early prognostic biomarkers for pERK protein levels in adenocarcinoma, and for nuclear p53 levels in squamous cell lung carcinoma. The determination of these potential biomarkers in the adequate histologic context may predict the outcome of early stage NSCLC patients, and may offer a therapeutic opportunity to enhance survival of these patients.

The expression of HOXA13 in lung adenocarcinoma and its clinical significance: A study based on The Cancer Genome Atlas, Oncomine and reverse transcription-quantitative polymerase chain reaction

Previous studies have investigated the association between HOXA13 and non-small cell lung cancer. However, the role of HOXA13 expression in the occurrence and progression of lung adenocarcinoma (LUAD) has not yet been investigated. In the present study, HOXA13-related data mining of The Cancer Genome Atlas (TCGA), polymerase chain reaction (PCR) data from our cases and the case information in Oncomine was conducted for validation. The expression data of HOXA13 in lung cancer cell lines were also collected from the Cancer Cell Line Encyclopedia (CCLE) database for further verification. A comprehensive meta-analysis of the expression of HOXA13 was also performed, integrating the data of TCGA, in-house PCR and Oncomine. Genes that were co-expressed with HOXA13 were subsequently identified through cBioPortal and Multi Experiment Matrix (MEM), and the potential role and mechanism of HOXA13 in LUAD was investigated. The expression value of HOXA13 in the LUAD group, which comprised 237 cases, was 3.74±2.694, significantly higher than its expression value in the non-cancerous group (0.92±0.608, P<0.001). The pooled SMD for HOXA13 was 0.346 (95% CI, 0.052–0.640; P=0.068; I2=51.3%; P=0.021), The meta-analysis of diagnostic tests revealed that the area under the summary receiver operating characteristic curve (SROC) was 0.78 (95% CI, 0.75–0.82). The results demonstrated that HOXA13 is highly expressed in LUAD. In addition to the studies on HOXA13 expression in tissues, the expression data of HOXA13 in lung cancer cell lines were also collected from the CCLE database for further verification of these conclusions. Genes that were co-expressed with HOXA13 were identified for pathway analysis. The most enriched Gene Ontology terms in the genes co-expressed with HOXA13 were positive regulation of transcription from RNA polymerase II promoter, signal transduction and positive regulation of GTPase activity in biological process; cytoplasm, integral component of membrane and plasma membrane in cellular component; and significantly involved in protein binding, transcription factor activity, sequence-specific DNA binding and sequence-specific DNA binding in molecular function. Kyoto Encyclopedia of Genes and Genomes analysis revealed that these target genes were clearly involved in Pathways in cancer, Proteoglycans in cancer and cAMP signaling pathway. The hub genes obtained from the four protein-protein interaction networks were associated with HOXA13. The results of the bioinformatics research in the present study revealed that HOXA13 may influence the expression of these hub genes in such a way as to promote the occurrence and development of LUAD. In conclusion, the expression of HOXA13 in patients with LUAD and its potential clinical value were analyzed comprehensively in the present study using data from a variety of sources. Through bioinformatics analysis, evidence that HOXA13 may promote the occurrence and development of LUAD was obtained.

Bioinformatic analysis of prognostic value of ZW10 interacting protein in lung cancer

Background

ZWINT is a crucial component of the mitotic checkpoint. However, its possible role in lung cancer is unclear. In this study, we determined its correlation with lung cancer.

Methods

Real-time PCR and immunohistochemistry (IHC) were used to determine 40 collected clinical lung cancer samples. Chi-square test was used to examine possible correlations between ZWINT expression and clinicopathological factors. The prognostic significance of mRNA expression of ZWINT in lung cancer was evaluated using the Kaplan–Meier plotter. Univariate and multivariate Cox proportional hazards regression analysis were performed to determine whether ZWINT is an independent risk factor for overall survival (OS) and disease-free survival (DFS) of lung cancer patients. Additionally, STRING database was used to analyze protein-protein interactions.

Results

In this study, we screened 13 GSE datasets and detected that ZWINT is highly expressed in multiple carcinomas including lung, melanoma, prostate, nasopharyngeal, gastric, pancreatic, colon, esophageal, ovarian, renal, breast and liver cancer. Real-time PCR and IHC results of collected clinical lung cancer samples confirmed that ZWINT is highly expressed in tumor tissues compared with adjacent non-tumor tissues. Additionally, high expression of ZWINT might predict poor OS and DFS in lung cancer patients. Moreover, disease stage and expression level of ZWINT were correlated with recurrence-free survival and OS in lung cancer. Analysis of protein-protein interaction based on STRING database gained 8 top genes which could interact with ZWINT, including PMF1, MIS12, DSN1, ZW10, BUB1, BUB1B, CASC5, NDC80, NSL1 and NUF2.

Conclusion

ZWINT is aberrantly highly expressed in lung tumor tissues and might be involved in the pathogenesis of lung cancer.

A five-gene based risk score with high prognostic value in colorectal cancer

Colorectal cancer (CRC) is one of the most frequently occurring malignancies worldwide. The outcomes of patients with similar clinical symptoms or at similar pathological stages remain unpredictable. This inherent clinical diversity is most likely due to the genetic heterogeneity. The present study aimed to create a predicting tool to evaluate patient survival based on genetic profile. Firstly, three Gene Expression Omnibus (GEO) datasets (GSE9348, GSE44076 and GSE44861) were utilized to identify and validate differentially expressed genes (DEGs) in CRC. The GSE14333 dataset containing survival information was then introduced in order to screen and verify prognosis-associated genes. Of the 66 DEGs, the present study screened out 46 biomarkers closely associated to patient overall survival. By Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway analysis, it was demonstrated that these genes participated in multiple biological processes which were highly associated with cancer proliferation, drug-resistance and metastasis, thus further affecting patient survival. The five most important genes, MET proto-oncogene, receptor tyrosine kinase, carboxypeptidase M, serine hydroxymethyltransferase 2, guanylate cyclase activator 2B and sodium voltage-gated channel a subunit 9 were selected by a random survival forests algorithm, and were further made up to a linear risk score formula by multivariable cox regression. Finally, the present study tested and verified this risk score within three independent GEO datasets (GSE14333, GSE17536 and GSE29621), and observed that patients with a high risk score had a lower overall survival (P<0.05). Furthermore, this risk score was the most significant compared with other predicting factors including age and American Joint Committee on Cancer stage, in the model, and was able to predict patient survival independently and directly. The findings suggest that this survival associated DEGs-based risk score is a powerful and accurate prognostic tool and is promisingly implemented in a clinical setting.

Beyond PD-L1 testing-emerging biomarkers for immunotherapy in non-small cell lung cancer

Recently, a firmer understanding of tumor immunology and tumor escape mechanisms has led to the development of immune checkpoint inhibitors, antibodies against programmed death-1 (PD-1) and its ligand (PD-L1). Nivolumab, pembrolizumab, and atezolizumab have dramatically altered the treatment paradigm in non-small cell lung cancer (NSCLC) and have each demonstrated improvements in outcomes and quality of life when compared to chemotherapy. Enrichment strategies to better select those patients more likely to respond have identified PD-L1 staining by immunohistochemistry (IHC) to be a predictive biomarker in both treatment naïve and refractory patients. Unfortunately, many challenges exist with this strategy and underscore the need for further exploration for more reliable biomarkers. Multiple tissue and plasma-based enrichment strategies have been identified in the hope of identifying patients more likely to benefit from checkpoint inhibitors. These include tumor mutational load; the "inflamed phenotype" including tumor infiltrating lymphocytes (TILS) and immunoscore; T-cell receptor clonality; gene signatures, and several plasma biomarkers. Several studies have revealed many of these biomarkers to be reliable predictors of response to immune checkpoint inhibitors across multiple tumor types. Given the small nature of these studies, additional prospective studies are warranted to formalize and validate each of these enrichment strategies.

The Prognostic 97 Chemoresponse Gene Signature in Ovarian Cancer

Patient diagnosis and care would be significantly improved by understanding the mechanisms underlying platinum and taxane resistance in ovarian cancer. Here, we aim to establish a gene signature that can identify molecular pathways/transcription factors involved in ovarian cancer progression, poor clinical outcome, and chemotherapy resistance. To validate the robustness of the gene signature, a meta-analysis approach was applied to 1,020 patients from 7 datasets. A 97-gene signature was identified as an independent predictor of patient survival in association with other clinicopathological factors in univariate [hazard ratio (HR): 3.0, 95% Confidence Interval (CI) 1.66–5.44, p = 2.7E-4] and multivariate [HR: 2.88, 95% CI 1.57–5.2, p = 0.001] analyses. Subset analyses demonstrated that the signature could predict patients who would attain complete or partial remission or no-response to first-line chemotherapy. Pathway analyses revealed that the signature was regulated by HIF1α and TP53 and included nine HIF1α-regulated genes, which were highly expressed in non-responders and partial remission patients than in complete remission patients. We present the 97-gene signature as an accurate prognostic predictor of overall survival and chemoresponse. Our signature also provides information on potential candidate target genes for future treatment efforts in ovarian cancer.

Cancer cells increase endothelial cell tube formation and survival by activating the PI3K/Akt signalling pathway

Background

Angiogenesis is a hallmark of cancer and plays a critical role in lung cancer progression, which involves interactions between cancer cells, endothelial cells and the surrounding microenvironment. However, the gene expression profiles and the changes in the biological phenotype of vascular endothelial cells after interactions with lung cancer cells remain unclear.

Methods

An indirect transwell co-culture system was used to survey the interaction between human umbilical vein endothelial cells (HUVECs) and human lung adenocarcinoma CL1-5 cells, as well as to investigate the morphological and molecular changes of HUVECs. The differentially expressed genes (DEGs) in HUVECs after co-culture with cancer cells were identified by microarray. Moreover, a publicly available microarray dataset of 293 non-small-cell lung cancer (NSCLC) patients was employed to evaluate the prognostic power of the gene signatures derived from HUVECs.

Results

The interaction between HUVECs and lung cancer cells changes the morphology of HUVECs, causing them to have a mesenchymal-like morphology and alter their cytoskeleton organization. Furthermore, after co-culture with lung cancer cells, HUVECs showed increased cell motility and microvessel tube formation ability and a decreased apoptotic percentage. Transcriptomic profiling of HUVECs revealed that many survival-, apoptosis- and angiogenesis-related genes were differentially expressed after interactions with lung cancer cells. Further investigations showed that the PI3K/Akt signalling pathway and COX-2 are involved in endothelial tube formation under the stimulation of lung cancer cells. Moreover, Rac-1 activation might promote endothelial cell motility through the increased formation of lamellipodia and filopodia. The inhibitors of PI3K and COX-2 could reverse the increased tube formation and induce the apoptosis of HUVECs. In addition, the gene signatures derived from the DEGs in HUVECs could predict overall survival and disease-free survival in NSCLC patients and serve as an independent prognostic factor.

Conclusions

In this study, we found that cancer cells can promote endothelial cell tube formation and survival, at least in part, through the PI3K/Akt signalling pathway and thus change the microenvironment to benefit tumour growth. The gene signatures from HUVECs are associated with the clinical outcome of NSCLC patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s13046-017-0495-3) contains supplementary material, which is available to authorized users.