Molecular gene signature and prognosis of non-small cell lung cancer

Pulmonary leiomyosarcoma arising in pulmonary hamartoma: an exceptional occurrence in a rare tumor

A solitary peripheral lung nodule was found in the left lung of a 52-year-old man. It was located in the lower lobe and measured 18.5 cm of major axis on chest computed tomography. A tru-cut core biopsy was obtained and a proliferation of bland, monomorphic, spindle cells in interlacing fascicles was observed. Accordingly, a surgical resection of the neoplasm was subsequently carried out. Macroscopically, the tumor appeared as a well-circumscribed nodule with a firm and whitish cut surface. Histologically, the neoplasm was predominantly composed of bland and monomorphic spindle cells, with a predominantly fascicular growth pattern, in which many tubular and cleft-like spaces of entrapped normal respiratory epithelium were involved. Myxoid change, stromal hyalinization and scattered bizarre mononucleated and multinucleated cells were also observed. Based on clinico-morphological, immunophenotypical and molecular features, we made a diagnosis of malignant transformation of pulmonary adenoleiomyomatous hamartoma into pulmonary leiomyosarcoma. As far as we know, this is the first described case of this exceptionally rare occurrence in an already rare neoplasm.

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.

Comparison of multiple gene expression platforms for measuring a bladder cancer hypoxia signature

Tumour hypoxia status provides prognostic information and predicts response to hypoxia‑modifying treatments. A previous study by our group derived a 24‑gene signature to assess hypoxia in bladder cancer. The objectives of the present study were to compare platforms for generating signature scores, identify cut‑off values for prospective studies, assess intra‑tumour heterogeneity and confirm hypoxia relevance. Briefly, RNA was extracted from prospectively collected diagnostic biopsies of muscle invasive bladder cancer (51 patients), and gene expression was measured using customised Taqman Low Density Array (TLDA) cards, NanoString and Clariom S arrays. Cross‑platform transferability of the gene signature was assessed using regression and concordance analysis. The cut‑off values were the cohort median expression values. Intra‑ and inter‑tumour variability were determined in a retrospective patient cohort (n=51) with multiple blocks (2‑18) from the same tumour. To demonstrate relevance, bladder cancer cell lines were exposed to hypoxia (0.1% oxygen, 24 h), and extracted RNA was run on custom TLDA cards. Hypoxia scores (HS) values showed good agreement between platforms: Clariom S vs. TLDA (r=0.72, P<0.0001; concordance 73%); Clariom S vs. NanoString (r=0.84, P<0.0001; 78%); TLDA vs. NanoString (r=0.80, P<0.0001; 78%). Cut‑off values were 0.047 (TLDA), 7.328 (NanoString) and 6.667 (Clariom S). Intra‑tumour heterogeneity in gene expression and HS (coefficient of variation 3.9%) was less than inter‑tumour (7.9%) variability. HS values were higher in bladder cancer cells exposed to hypoxia compared with normoxia (P<0.02). In conclusion, the present study revealed that application of the 24‑gene bladder cancer hypoxia signature was platform agnostic, cut‑off values determined prospectively can be used in a clinical trial, intra‑tumour heterogeneity was low and the signature was sensitive to changes in oxygen levels in vitro.

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.

Comprehensive Analysis of the Expression, Relationship to Immune Infiltration and Prognosis of TIM-1 in Cancer

TIM-1 is a critical gene that regulates T-helper cell development. However, little research has revealed the distribution, prognosis, and immune infiltration of TIM-1 in cancers. TCGA, GEO, Oncomine, TIMER, Kaplan-Meier, PrognoScan, GEPIA, TISIDB, and HPA databases were used to analyze TIM-1 in cancers. High TIM-1 expression was observed in bladder, cholangio, head and neck, colorectal, gastric, kidney, liver, lung adenocarcinoma, skin, uterine corpus endometrial, and pancreatic cancers compared to the normal tissues, and immunofluorescence shows that TIM-1 is mainly localized in vesicles. Simultaneously, high TIM-1 expression was closely related with poorer overall survival in gastric, lung adenocarcinoma, and poorer disease-specific survival in gastric cancer in the TCGA cohort, and was validated in the GEO cohort. Moreover, high expression of TIM-1, correlated with clinical relevance of gastric cancer and lung adenocarcinoma, was associated with tumor-infiltrating lymphocytes in lung adenocarcinoma and gastric cancer. Finally, immunohistochemistry showed TIM-1 expression was higher in lung adenocarcinoma and gastric cancer compared to the normal tissues. In summary, we applied integrated bioinformatics approaches to suggest that TIM-1 can be used as a prognostic biomarker in gastric and lung adenocarcinoma, which might provide a novel direction to explore the pathogenesis of gastric and lung adenocarcinoma.

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.

Integrated transcriptomics reveals master regulators of lung adenocarcinoma and novel repositioning of drug candidates

BACKGROUND

Lung adenocarcinoma is the major cause of cancer-related deaths in the world. Given this, the importance of research on its pathophysiology and therapy remains a key health issue. To assist in this endeavor, recent oncology studies are adopting Systems Biology approaches and bioinformatics to analyze and understand omics data, bringing new insights about this disease and its treatment.

METHODS

We used reverse engineering of transcriptomic data to reconstruct nontumorous lung reference networks, focusing on transcription factors (TFs) and their inferred target genes, referred as regulatory units or regulons. Afterwards, we used 13 case-control studies to identify TFs acting as master regulators of the disease and their regulatory units. Furthermore, the inferred activation patterns of regulons were used to evaluate patient survival and search drug candidates for repositioning.

RESULTS

The regulatory units under the influence of ATOH8, DACH1, EPAS1, ETV5, FOXA2, FOXM1, HOXA4, SMAD6, and UHRF1 transcription factors were consistently associated with the pathological phenotype, suggesting that they may be master regulators of lung adenocarcinoma. We also observed that the inferred activity of FOXA2, FOXM1, and UHRF1 was significantly associated with risk of death in patients. Finally, we obtained deptropine, promazine, valproic acid, azacyclonol, methotrexate, and ChemBridge ID compound 5109870 as potential candidates to revert the molecular profile leading to decreased survival.

CONCLUSION

Using an integrated transcriptomics approach, we identified master regulator candidates involved with the development and prognostic of lung adenocarcinoma, as well as potential drugs for repurposing.

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.

Inhibition of oncogenic Src induces FABP4-mediated lipolysis via PPARγ activation exerting cancer growth suppression

Background

c-Src is a driver oncogene well-known for tumorigenic signaling, but little for metabolic function. Previous reports about c-Src regulation of glucose metabolism prompted us to investigate its function in other nutrient modulation, particularly in lipid metabolism.

Methods

Oil-red O staining, cell growth assay, and tumor volume measurement were performed to determine lipid amount and growth inhibitory effect of treatments in lung cancer cells and xenograft model. Gene expression was evaluated by immunoblotting and relative RT-PCR. Transcriptional activity of peroxisome proliferator-activated receptor gamma (PPARγ) was assessed by luciferase assay. Reactive oxygen species (ROS) was measured using ROS sensing dye. Oxygen consumption rate was evaluated by Seahorse XF Mito Stress Test. Clinical relevance of candidate proteins was examined using patient samples and public database analysis.

Findings

Inhibition of Src induced lipolysis and increased intracellular ROS. Src inhibition derepressed PPARγ transcriptional activity leading to induced expression of lipolytic gene fatty acid binding protein (FABP) 4 which accompanies reduced lipid droplets and decreased tumor growth. The reverse correlation of Src and FABP4 was confirmed in pair-matched lung cancer patient samples, and further analysis using public datasets revealed upregulation of lipolytic genes is associated with better prognosis of cancer patients.

Interpretation

This study provides an insight of how oncogenic factor Src concurrently regulates both cellular signaling pathways and metabolic plasticity to drive cancer progression.

Fund

National Research Foundation of Korea and Korea Health Industry Development Institute.

Patient-derived induced pluripotent stem cells for models of cancer and cancer stem cell research

Induced pluripotent stem cells (iPSCs) are embryonic stem cell-like cells reprogrammed from somatic cells by four transcription factors, OCT4, SOX2, KLF4 and c-MYC. iPSCs derived from cancer cells (cancer-iPSCs) could be a novel strategy for studying cancer. During cancer cell reprogramming, the epigenetic status of the cancer cell may be altered, such that it acquires stemness and pluripotency. The cellular behavior of the reprogrammed cells exhibits dynamic changes during the different stages of reprogramming. The cells may acquire the properties of cancer stem cells (CSCs) during the process of reprogramming, and lose their carcinogenic properties during reprogramming into a cancer-iPSCs. Differentiation of cancer-iPSCs by teratoma formation or organoid culturing could mimic the process of tumorigenesis. Some of the molecular mechanisms associated with cancer progression could be elucidated using the cancer-iPSC model. Furthermore, cancer-iPSCs could be expanded in culture system or bioreactors, and serve as cell sources for research, and as personal disease models for therapy and drug screening. This article introduces cancer studies that used the cell reprogramming strategy.

Targeting the SET and RING-associated (SRA) domain of ubiquitin-like, PHD and ring finger-containing 1 (UHRF1) for anti-cancer drug development

Ubiquitin-like containing PHD Ring Finger 1 (UHRF1) is a multi-domain protein with a methyl-DNA binding SRA (SET and RING-associated) domain, required for maintenance DNA methylation mediated by DNMT1. Primarily expressed in proliferating cells, UHRF1 is a cell-cycle regulated protein that is required for S phase entry. Furthermore, UHRF1 participates in transcriptional gene regulation by connecting DNA methylation to histone modifications. Upregulation of UHRF1 may serve as a biomarker for a variety of cancers; including breast, gastric, prostate, lung and colorectal carcinoma. To this end, overexpression of UHRF1 promotes cancer metastasis by triggering aberrant patterns of DNA methylation, and subsequently, silencing tumor suppressor genes. Various small molecule effectors of UHRF1 have been reported in the literature, although the mechanism of action may not be fully characterized. Small molecules that potentially bind to the SRA domain may affect the ability of UHRF1 to bind hemimethylated DNA; thereby reducing aberrant DNA methylation. Therefore, in a subset of cancers, small molecule UHRF1 inhibitors may restore normal gene expression and serve as useful anti-cancer therapeutics.

Upregulation of UHRF1 promotes the progression of melanoma by inducing cell proliferation

Melanoma is the most aggressive cutaneous cancer due to its propensity to metastasise and proliferate. Melanoma accounts for 80–90% of skin-cancer related deaths worldwide. Alhough numerous published studies have attempted to define the markers of diagnosis and prognosis of melanoma, a sensitive and specific biomarker for melanoma remains unknown. Recently, ubiquitin-like with PHD and ring finger domains 1 (UHRF1) has attracted attention due to its role in cell proliferation and it has been deemed as a potential therapeutic target for cancer. The aim of the present study was to investigate the role and the clinical significance of UHRF1 in melanoma. Immunohistochemical analysis was performed with tissue microarray (TMA) to examine the expression of UHRF1 and Ki-67, and the role of UHRF1 in cell proliferation was determined through CCK-8, colony formation and flow cytometry by interfering with the expression of UHRF1. Subsequently, the relationship among the expression of UHRF1 and several major clinical characteristics of melanoma were analysed to evaluate the role of UHRF1 in the progression of melanoma. Finally, the clinical significance of UHRF1 was estimated in 56 melanoma patients. It was observed that the expression of UHRF1 was significantly upregulated in melanoma compared with benign nevi tissues (P<0.05). In addition, the downregulation of the expression of UHRF1 significantly decreased cell proliferation. Furthermore, the level of UHRF1 was positively correlated with the expression of Ki-67 in melanoma cells, as well as in melanoma tissues. Clinically, a high level of UHRF1 was prone to be related to a high TNM classification (P=0.017) and Breslow's thickness (P=0.034) of melanoma. Furthermore, a high level of UHRF1 was positively associated with a shorter overall survival of melanoma patients. Importantly, the Cox regression model analysis demonstrated that the expression of UHRF1 was an independent prognostic factor for the overall survival of melanoma patients. In conclusion, the elevated expression of UHRF1 plays an important role in melanoma cell proliferation and progression, and it can be used as a prognostic biomarker for melanoma.

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.

Association of tumor TROP2 expression with prognosis varies among lung cancer subtypes

TROP2 is a transmembrane glycoprotein that is overexpressed in various cancers. Emerging evidence suggests that TROP2-targeting therapies are efficacious and safe in patients with multiple prior treatments. TROP2 is a promising target for lung cancer treatment; however, little is known regarding the association of TROP2 expression with clinicopathological/molecular features, including prognosis, in lung cancer. We examined consecutive cases of adenocarcinoma, squamous cell carcinoma (SqCC), and high-grade neuroendocrine tumor (HGNET) for the membranous expression of TROP2 using immunohistochemistry. High TROP2 expression was observed in 64% (172/270) of adenocarcinomas, 75% (150/201) of SqCCs, and 18% (21/115) of HGNETs. Intriguingly, the association of TROP2 expression with mortality was dependent on the lung cancer subtype. High TROP2 expression was associated with higher lung cancer-specific mortality in adenocarcinomas [univariable hazard ratio (HR) = 1.60, 95% confidence interval (CI) = 1.07–2.44, P = 0.022)], but not in SqCCs (univariable HR = 0.79, 95% CI = 0.35–1.94, P = 0.79). In HGNETs, high TROP2 expression was associated with lower lung cancer-specific mortality in both univariable and multivariable analyses (multivariable HR = 0.13, 95% CI = 0.020–0.44, P = 0.0003). Our results suggest a differential role for TROP2 in different lung cancer subtypes.

Expression of Bcl-2 is a favorable prognostic biomarker in lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) is the second major type of lung cancer globally. The majority of patients with LUSC are clinically diagnosed at the advanced stages, thus it is urgent to identify suitable prognostic markers for LUSC. B-cell lymphoma 2 (Bcl-2) has been widely studied in non-small cell lung cancer (NSCLC). However, the prognostic role of Bcl-2 in NSCLC remains conflicting and controversial, particularly for LUSC. Although certain studies have been performed to identify the prognostic value of Bcl-2, to the best of our knowledge, no study has investigated the prognostic role of Bcl-2 in LUSC specifically. The present study aimed to comprehensively evaluate the prognostic value of Bcl-2 in LUSC. Microarray data for LUSC were downloaded from public databases, including the Gene Expression Omnibus and The Cancer Genome Atlas. Microarray data of 901 patients with LUSC from 16 data sets were retrieved. The meta-z algorithm was applied and the combined z score was identified as -2.43, suggesting Bcl-2 is a favorable prognostic biomarker. Furthermore, immunohistochemical staining of Bcl-2 expression was performed in a tissue microarray of 72 patients with LUSC and survival analysis demonstrated that patients with high expression Bcl-2 exhibited significantly more improved overall survival rates compared with those with low Bcl-2 expression. Multivariate Cox regression revealed that high expression of Bcl-2 is an independent favorable prognostic factor (hazard ratio, 0.295; confidence interval, 0.097-0.904; P<0.05). Therefore, the results of the present study demonstrated that Bcl-2 is a favorable prognostic biomarker in LUSC.

A 16-gene signature predicting prognosis of patients with oral tongue squamous cell carcinoma

Background

Oral tongue squamous cell carcinoma (OTSCC) is the most common subtype of oral cancer. A predictive gene signature is necessary for prognosis of OTSCC.

Methods

Five microarray data sets of OTSCC from the Gene Expression Omnibus (GEO) and one data set from The Cancer Genome Atlas (TCGA) were obtained. Differentially expressed genes (DEGs) of GEO data sets were identified by integrated analysis. The DEGs associated with prognosis were screened in the TCGA data set by univariate survival analysis to obtain a gene signature. A risk score was calculated as the summation of weighted expression levels with coefficients by Cox analysis. The signature was used to distinguish carcinoma, estimated by receiver operator characteristic curves and the area under the curve (AUC). All were validated in the GEO and TCGA data sets.

Results

Integrated analysis of GEO data sets revealed 300 DEGs. A 16-gene signature and a risk score were developed after survival analysis. The risk score was effective to stratify patients into high-risk and low-risk groups in the TCGA data set (P < 0.001). The 16-gene signature was valid to distinguish the carcinoma from normal samples (AUC 0.872, P < 0.001).

Discussion

We identified a useful 16-gene signature for prognosis of OTSCC patients, which could be applied to clinical practice. Further studies were needed to prove the findings.

The epigenetic integrator UHRF1: on the road to become a universal biomarker for cancer

Cancer is one of the deadliest diseases in the world causing record number of mortalities in both developed and undeveloped countries. Despite a lot of advances and breakthroughs in the field of oncology still, it is very hard to diagnose and treat the cancers at early stages. Here in this review we analyze the potential of Ubiquitin-like containing PHD and Ring Finger domain 1 (UHRF1) as a universal biomarker for cancers. UHRF1 is an important epigenetic regulator maintaining DNA methylation and histone code in the cell. It is highly expressed in a variety of cancers and is a well-known oncogene that can disrupt the epigenetic code and override the senescence machinery. Many studies have validated UHRF1 as a powerful diagnostic and prognostic tool to differentially diagnose cancer, predict the therapeutic response and assess the risk of tumor progression and recurrence. Highly sensitive, non-invasive and cost effective approaches are therefore needed to assess the level of UHRF1 in patients, which can be deployed in diagnostic laboratories to detect cancer and monitor disease progression.