Three-Gene Expression Signature Predicts Survival in Early-Stage Squamous Cell Carcinoma of the Lung

ANLN Promotes the Proliferation and Migration of Gallbladder Cancer Cells via STRA6-Mediated Activation of PI3K/AKT Signaling

The ANLN gene encodes anillin, a protein that binds to actin. Recent research has identified ANLN's function in the initiation and advancement of different cancers. However, its impact on gallbladder cancer (GBC) remains unexplored. This study aimed to elucidate its possible molecular mechanisms in GBC. ANLN expression was assessed using quantitative real-time polymerase chain reaction (QRT-PCR), Western blotting (WB), and immunohistochemistry (IHC), revealing elevated levels in GBC tissues. ANLN knockdown resulted in the inhibition of cell proliferation and migration, leading to apoptosis and cell cycle arrest. Conversely, ANLN overexpression had the opposite effects on GBC cells. In vivo experiments confirmed that ANLN knockdown inhibited GBC cell growth. RNA-seq and bioinformatics analysis revealed ANLN's function in activating the PI3K/AKT signaling pathway. We further confirmed that ANLN could upregulate STRA6 expression, which activated PI3K/AKT signaling to enhance the growth and movement of GBC cells. These findings demonstrate ANLN's involvement in GBC initiation and progression, suggesting its potential as a novel target for GBC.

Radiomics and artificial intelligence for precision medicine in lung cancer treatment

Lung cancer is the leading cause of cancer-related deaths worldwide. It exhibits, at the mesoscopic scale, phenotypic characteristics that are generally indiscernible to the human eye but can be captured non-invasively on medical imaging as radiomic features, which can form a high dimensional data space amenable to machine learning. Radiomic features can be harnessed and used in an artificial intelligence paradigm to risk stratify patients, and predict for histological and molecular findings, and clinical outcome measures, thereby facilitating precision medicine for improving patient care. Compared to tissue sampling-driven approaches, radiomics-based methods are superior for being non-invasive, reproducible, cheaper, and less susceptible to intra-tumoral heterogeneity. This review focuses on the application of radiomics, combined with artificial intelligence, for delivering precision medicine in lung cancer treatment, with discussion centered on pioneering and groundbreaking works, and future research directions in the area.

Gene Screening in High-Throughput Right-Censored Lung Cancer Data

Background

Advances in sequencing technologies have allowed collection of massive genome-wide information that substantially advances lung cancer diagnosis and prognosis. Identifying influential markers for clinical endpoints of interest has been an indispensable and critical component of the statistical analysis pipeline. However, classical variable selection methods are not feasible or reliable for high-throughput genetic data. Our objective is to propose a model-free gene screening procedure for high-throughput right-censored data, and to develop a predictive gene signature for lung squamous cell carcinoma (LUSC) with the proposed procedure.

Methods

A gene screening procedure was developed based on a recently proposed independence measure. The Cancer Genome Atlas (TCGA) data on LUSC was then studied. The screening procedure was conducted to narrow down the set of influential genes to 378 candidates. A penalized Cox model was then fitted to the reduced set, which further identified a 6-gene signature for LUSC prognosis. The 6-gene signature was validated on datasets from the Gene Expression Omnibus.

Results

Both model-fitting and validation results reveal that our method selected influential genes that lead to biologically sensible findings as well as better predictive performance, compared to existing alternatives. According to our multivariable Cox regression analysis, the 6-gene signature was indeed a significant prognostic factor (p-value < 0.001) while controlling for clinical covariates.

Conclusions

Gene screening as a fast dimension reduction technique plays an important role in analyzing high-throughput data. The main contribution of this paper is to introduce a fundamental yet pragmatic model-free gene screening approach that aids statistical analysis of right-censored cancer data, and provide a lateral comparison with other available methods in the context of LUSC.

Vav1 accelerates Ras-driven lung cancer and modulates its tumor microenvironment

The potential impact of Vav1 on human cancer was only recently acknowledged, as it is detected as a mutant or an overexpressed gene in various cancers, including lung cancer. Vav1, which is normally and exclusively expressed in the hematopoietic system functions as a specific GDP/GTP nucleotide exchange factor (GEF), strictly regulated by tyrosine phosphorylation. To investigate whether Vav1 plays a causative or facilitating role in-vivo in lung cancer development and to examine whether it co-operates with other oncogenes, such as mutant K-Ras, we generated novel mouse strains that express: Vav1 or K-Ras^G12D in type II pneumocytes, as well as a transgenic mouse line that expresses both Vav1 and K-Ras^G12D in these cells. Coexpression of Vav1 and K-Ras^G12D in the lungs dramatically increased malignant lung cancer lesions, and did so significantly faster than K-Ras^G12D alone, strongly suggesting that these two oncogenes synergize to enhance lung tumor development. Vav1 expression alone had no apparent effects on lung tumorigenesis. The increase in lung cancer in K-Ras^G12D/Vav1 mice was accompanied by an increase in B-cell, T-cells, and monocyte infiltration in the tumor microenvironment. Concomitantly, ERK phosphorylation was highly elevated in the lungs of K-Ras^{G12 D}/Vav1 mice. Also, several cytokines such as IL-4 and IL-13 which play a significant role in the immune system, were elevated in lungs of Vav1 and K-Ras^{G12 D}/Vav1 mice. Our findings emphasize the contribution of Vav1 to lung tumor development through its signaling properties.

Comprehensive analyses reveal the carcinogenic and immunological roles of ANLN in human cancers

Background

Anillin (ANLN) is an actin-binding protein that is essential for cell division and contributes to cell growth and migration. Although previous studies have shown that ANLN is related to carcinogenesis, no pan-cancer analyses of ANLN have been reported. Accordingly, in this study, we evaluated the carcinogenic roles of ANLN in various cancer types using online databases.

Methods

We evaluated the potential carcinogenic roles of ANLN using TIMER2 and Gene Expression Omnibus databases with 33 types of cancers. We further investigated the associations of ANLN with patient prognosis, genetic alterations, phosphorylation levels, and immune infiltration in multiple cancers using GEPIA2, cBioPortal, UACLAN, and TIMER2 databases. Additionally, the potential functions of ANLN were explored using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses. Reverse transcription quantitative polymerase chain reaction and immunohistochemistry were used to determine ANLN mRNA and protein expression in colorectal cancer (CRC), gastric cancer (GC), and hepatocellular carcinoma (HCC) cell lines.

Results

ANLN was overexpressed in various tumor tissues compared with corresponding normal tissues, and significant correlations between ANLN expression and patient prognosis, genetic alterations, phosphorylation levels, and immune infiltration were noted. Moreover, enrichment analysis suggested that ANLN functionally affected endocytosis, regulation of actin cytoskeleton, and oxytocin signaling pathways. Importantly, ANLN mRNA and protein expression levels were upregulated in gastrointestinal cancers, including CRC, GC, and HCC.

Conclusions

Our findings suggested that ANLN participated in tumorigenesis and cancer progression and may have applications as a promising biomarker of immune infiltration and prognosis in various cancers.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02610-1.

Molecular Radiobiology in Non-Small Cell Lung Cancer: Prognostic and Predictive Response Factors

Simple Summary

The identification of prognostic and predictive gene signatures of response to cancer treatment (radiotherapy) could help in making therapeutic decisions in patients affected by NSCLC. There are multiple proposals for gene signatures that attempt to predict survival or predict response to treatment (not radiotherapy), but they mainly focus on early stages or metastasis at diagnosis. In contrast, there have been few studies that raise these predictive and/or prognostic elements in nonmetastatic locally advanced stages, where treatment with ionizing radiation plays an important role. In this work, we review in depth previous works discovering the prognostic and predictive response factors in non-small cell lung cancer, specially focused on non-deeply studied radiation-based therapy.

Abstract

Non-small-cell lung cancer (NSCLC) is the leading cause of cancer-related death worldwide, generating huge economic and social impacts that have not slowed in recent years. Oncological treatment for this neoplasm usually includes surgery, chemotherapy, treatments on molecular targets and ionizing radiation. The prognosis in terms of overall survival (OS) and the different therapeutic responses between patients can be explained, to a large extent, by the existence of widely heterogeneous molecular profiles. The identification of prognostic and predictive gene signatures of response to cancer treatment, could help in making therapeutic decisions in patients affected by NSCLC. Given the published scientific evidence, we believe that the search for prognostic and/or predictive gene signatures of response to radiotherapy treatment can significantly help clinical decision-making. These signatures may condition the fractions, the total dose to be administered and/or the combination of systemic treatments in conjunction with radiation. The ultimate goal is to achieve better clinical results, minimizing the adverse effects associated with current cancer therapies.

A Six-Gene Prognostic and Predictive Radiotherapy-Based Signature for Early and Locally Advanced Stages in Non-Small-Cell Lung Cancer

Simple Summary

The search for prognostic and/or predictive gene signatures of the response to radiotherapy treatment can significantly aid clinical decision making. These signatures can condition the fractionation, the total dose to be administered, and/or the combination of systemic treatments and radiation. The ultimate goal is to achieve better clinical results, as well as to minimize the adverse effects associated with current cancer therapies. To this end, we analyzed the intrinsic radiosensitivity of 15 NSCLC lines and found the differences in gene expression levels between radiosensitive and radioresistant lines, resulting in a potentially applicable six-gene signature in NSCLC patients. The six-gene signature had the ability to predict overall survival and progression-free survival (PFS), which could translate into a prediction of the response to the cancer treatment received.

Abstract

Non-small-cell lung cancer (NSCLC) is the leading cause of cancer death worldwide, generating an enormous economic and social impact that has not stopped growing in recent years. Cancer treatment for this neoplasm usually includes surgery, chemotherapy, molecular targeted treatments, and ionizing radiation. The prognosis in terms of overall survival (OS) and the disparate therapeutic responses among patients can be explained, to a great extent, by the existence of widely heterogeneous molecular profiles. The main objective of this study was to identify prognostic and predictive gene signatures of response to cancer treatment involving radiotherapy, which could help in making therapeutic decisions in patients with NSCLC. To achieve this, we took as a reference the differential gene expression pattern among commercial cell lines, differentiated by their response profile to ionizing radiation (radiosensitive versus radioresistant lines), and extrapolated these results to a cohort of 107 patients with NSCLC who had received radiotherapy (among other therapies). We obtained a six-gene signature (APOBEC3B, GOLM1, FAM117A, KCNQ1OT1, PCDHB2, and USP43) with the ability to predict overall survival and progression-free survival (PFS), which could translate into a prediction of the response to the cancer treatment received. Patients who had an unfavorable prognostic signature had a median OS of 24.13 months versus 71.47 months for those with a favorable signature, and the median PFS was 12.65 months versus 47.11 months, respectively. We also carried out a univariate analysis of multiple clinical and pathological variables and a bivariate analysis by Cox regression without any factors that substantially modified the HR value of the proposed gene signature.

Screening and Validation of Significant Genes with Poor Prognosis in Pathologic Stage-I Lung Adenocarcinoma

Background

Although more pathologic stage-I lung adenocarcinoma (LUAD) was diagnosed recently, some relapsed or distantly metastasized shortly after radical resection. The study aimed to identify biomarkers predicting prognosis in the pathologic stage-I LUAD and improve the understanding of the mechanisms involved in tumorigenesis.

Methods

We obtained the expression profiling data for non-small cell lung cancer (NSCLC) patients from the NCBI-GEO database. Differentially expressed genes (DEGs) between early-stage NSCLC and normal lung tissue were determined. After function enrichment analyses on DEGs, the protein-protein interaction (PPI) network was built and analyzed with the Search Tool for the Retrieval of Interacting Genes (STRING) and Cytoscape. Overall survival (OS) and mRNA levels of genes were performed with Kaplan–Meier analysis and Gene Expression Profiling Interactive Analysis (GEPIA). qPCR and western blot analysis of hub genes in stage-I LUAD patients validated the significant genes with poor prognosis.

Results

A total of 172 DEGs were identified, which were mainly enriched in terms related to management of extracellular matrix (ECM), receptor signaling pathway, cell adhesion, activity of endopeptidase, and receptor. The PPI network identified 11 upregulated hub genes that were significantly associated with OS in NSCLC and highly expressed in NSCLC tissues compared with normal tissues by GEPIA. Elevated expression of ANLN, EXO1, KIAA0101, RRM2, TOP2A, and UBE2T were identified as potential risk factors in pathologic stage-I LUAD. Except for ANLN and KIAA0101, the hub genes mRNA levels were higher in tumors compared with adjacent non-cancerous samples in the qPCR analysis. The hub genes protein levels were also overexpressed in tumors. In vitro experiments showed that knockdown of UBE2T in LUAD cell lines could inhibit cell proliferation and cycle progression.

Conclusions

The DEGs can probably be used as potential predictors for stage-I LUAD worse prognosis and UBE2T may be a potential tumor promoter and target for treatment.

DDX56 modulates post-transcriptional Wnt signaling through miRNAs and is associated with early recurrence in squamous cell lung carcinoma

BACKGROUND

Early recurrence is a major obstacle to prolonged postoperative survival in squamous cell lung carcinoma (SqCLC). The molecular mechanisms underlying early SqCLC recurrence remain unclear, and effective prognostic biomarkers for predicting early recurrence are needed.

METHODS

We analyzed primary tumor samples of 20 SqCLC patients using quantitative proteomics to identify differentially-expressed proteins in patients who experienced early versus late disease recurrence. The expression and prognostic significance of DDX56 was evaluated using a SqCLC tumor tissue microarray and further verified using different online databases. We performed in vitro and in vivo experiments to obtain detailed molecular insight into the functional role of DDX56 in SqCLC.

RESULTS

We found that DDX56 exhibited increased expression in tumors of patients who experienced early versus late disease recurrence. Increased DDX56 expression in SqCLC tumors was subsequently confirmed as an independent prognostic factor of poor recurrence-free survival in independent SqCLC cohorts. Functionally, DDX56 promotes SqCLC cell growth and migration in vitro, and xenograft tumor progression in vivo. Mechanistically, DDX56 post-transcriptionally promotes expression of multiple Wnt signaling pathway-related genes, including CTNNB1, WNT2B, and represses a subset of miRNAs, including miR-378a-3p, a known suppressor of Wnt signaling. Detailed analysis revealed that DDX56 facilitated degradation of primary miR-378a, leading to down-regulation of mature miR-378a-3p and thus derepression of the target gene WNT2B.

CONCLUSION

We identified DDX56 as a novel independent prognostic biomarker that exerts its oncogenic effects through miRNA-mediated post-transcriptional regulation of Wnt signaling genes to promote early SqCLC recurrence. DDX56 may assist in identifying SqCLC patients at increased risk of early recurrence and who could benefit from Wnt signaling-targeted therapies.

Actin-binding protein anillin promotes the progression of hepatocellular carcinoma in vitro and in mice

Hepatocellular carcinoma (HCC) is a common type of tumor with high mortality worldwide. Investigations associated with the molecular etiology of HCC and screening novel therapeutic targets are still urgently in need. Anillin (ANLN), as a type of evolutionarily conserved actin-binding protein, is involved in multiple cellular processes. ANLN widely affected the progression and metastasis of several types of cancer, and its overexpression was frequently demonstrated in previous studies. The present study demonstrated high expression of ANLN in human HCC tissues, which was also associated the prognosis of patients with HCC. The associations between ANLN expression and the clinicopathological features were determined, including the number of tumor nodes (P=0.011) and tumor size (P=0.003) of patients with HCC. It was found that ANLN promoted cell proliferation, invasion and migration of HCC cells in vitro, and affected tumor growth in vivo. Therefore, ANLN is suggested as a promising therapeutic target for the treatment of HCC.

Oncoprotein-specific molecular interaction maps (SigMaps) for cancer network analyses

Tumor-specific elucidation of physical and functional oncoprotein interactions could improve tumorigenic mechanism characterization and therapeutic response prediction. Current interaction models and pathways, however, lack context specificity and are not oncoprotein specific. We introduce SigMaps as context-specific networks, comprising modulators, effectors and cognate binding-partners of a specific oncoprotein. SigMaps are reconstructed de novo by integrating diverse evidence sources-including protein structure, gene expression and mutational profiles-via the OncoSig machine learning framework. We first generated a KRAS-specific SigMap for lung adenocarcinoma, which recapitulated published KRAS biology, identified novel synthetic lethal proteins that were experimentally validated in three-dimensional spheroid models and established uncharacterized crosstalk with RAB/RHO. To show that OncoSig is generalizable, we first inferred SigMaps for the ten most mutated human oncoproteins and then for the full repertoire of 715 proteins in the COSMIC Cancer Gene Census. Taken together, these SigMaps show that the cell's regulatory and signaling architecture is highly tissue specific.

Actin-binding protein Anillin promotes the progression of gastric cancer in vitro and in mice

BACKGROUND

To detect the expression levels of actin-binding protein anillin (ANLN) in human gastric cancer (GC) tissues and explore the possible involvement of ANLN in GC cell proliferation, migration, and invasion.

METHODS

The bioinformation analysis was performed in TCGA database to explore the expression of ANLN in human GC tissues and the difference of ANLN expression between multiple types of cancers. IHC assays and clinical pathological analysis were performed to confirm ANLN expression and its correlation with clinical features of GC patients. Colony formation, CCK-8, wound closure, and transwell assays were performed to detect its effects on GC cell proliferation, migration, and invasion in vitro. Tumor growth was also measured using a xenograft animal model.

RESULTS

We found the high expression of ANLN in human GC tissues based on the results from TCGA database and IHC staining. We further noticed ANLN depletion resulted in the inhibition of GC cell proliferation, migration, and invasion. Our data further confirmed that ANLN contributed to tumor growth of GC cells in vivo.

CONCLUSIONS

We confirmed the involvement of ANLN in GC progression and thought ANLN could serve as a promising therapeutic target for GC.

A novel predictive model incorporating immune-related gene signatures for overall survival in melanoma patients

Melanoma is the most invasive type of skin cancer, in which the immune system plays a vital role. In this study, we aimed to establish a prognostic prediction nomogram for melanoma patients that incorporates immune-related genes (IRGs). Ninety-seven differentially expressed IRGs between melanoma and normal skin were screened using gene expression omnibus database (GEO). Among these IRGs, a two-gene signature consisting of CCL8 and DEFB1 was found to be closely associated with patient prognosis using the cancer genome atlas (TCGA) database. Survival analysis verified that the IRGs score based on the signature gene expressions efficiently distinguished between high- and low-risk patients, and was identified to be an independent prognostic factor. A nomogram integrating the IRGs score, age and TNM stage was established to predict individual prognosis for melanoma. The prognostic performance was validated by the TCGA/GEO-based concordance indices and calibration plots. The area under the curve demonstrated that the nomogram was superior than the conventional staging system, which was confirmed by the decision curve analysis. Overall, we developed and validated a nomogram for prognosis prediction in melanoma based on IRGs signatures and clinical parameters, which could be valuable for decision making in the clinic.

Identifying Tumorigenesis and Prognosis-Related Genes of Lung Adenocarcinoma: Based on Weighted Gene Coexpression Network Analysis

Lung adenocarcinoma is the most frequently diagnosed subtype of nonsmall cell lung cancer. The molecular mechanisms of the initiation and progression of lung adenocarcinoma remain to be further determined. This study aimed to screen genes related to the progression of lung adenocarcinoma. By weighted gene coexpression network analysis (WGCNA), we constructed a free-scale gene coexpression network to evaluate the correlations between multiple gene sets and patients' clinical traits, then further identify predictive biomarkers. GSE11969 was obtained from the Gene Expression Omnibus (GEO) database which contained the gene expression data of 90 lung adenocarcinoma patients. Data of the Cancer Genome Atlas (TCGA) were employed as the validation cohort. After the average linkage hierarchical clustering, a total of 9 modules were generated. In the clinical significant module (R = 0.44, P < 0.0001), we identified 29 network hub genes. Subsequent verification in the TCGA database showed that 11 hub genes (ANLN, CDCA5, FLJ21924, LMNB1, MAD2L1, RACGAP1, RFC4, SNRPD1, TOP2A, TTK, and ZWINT) were significantly associated with poor survival data of lung adenocarcinomas. Besides, the results of receiver operating characteristic curves indicated that the mRNA levels of this group of genes exhibited high specificity and sensitivity to distinguish malignant lesions from nonmalignant tissues. Apart from mRNA levels, we found that the protein abundances of these 11 genes were remarkably upregulated in lung adenocarcinomas compared with normal tissues. In conclusion, by the WGCNA method, a panel of 11 genes were identified as predictive biomarkers for tumorigenesis and poor prognosis of lung adenocarcinomas.

Systematic construction and validation of an immune prognostic model for lung adenocarcinoma

Lung adenocarcinoma (LUAD), the most common non‐small‐cell lung cancer, is characterized by a dense lymphocytic infiltrate, which indicates that the immune system plays an active role in the development and growth of this cancer. However, no investigations to date have proposed robust models for predicting survival outcome for patients with LUAD in terms of tumour immunology. A total of 761 LUAD patients were included in this study, in which the database of The Cancer Genome Atlas (TCGA) was utilized for discovery, and the Gene Expression Omnibus (GEO) database was utilized for validation. Bioinformatics analysis and R language tools were utilized to construct an immune prognostic model and annotate biological functions. Lung adenocarcinoma showed a weakened immune phenotype compared with adjacent normal tissues. Immune‐related gene sets were profiled, an immune prognostic model based on 2 immune genes (ANLN and F2) was developed with the TCGA database to distinguish cases as having a low or high risk of unfavourable prognosis, and the model was verified with the GEO database. The model was prognostically significant in stratified cohorts, including stage I‐II, stage III‐IV and epidermal growth factor receptor (EGFR) mutant subsets, and was considered to be an independent prognostic factor for LUAD. Furthermore, the low‐ and high‐risk groups showed marked differences in tumour‐infiltrating leucocytes, tumour mutation burden, aneuploidy and PD‐L1 expression. In conclusion, an immune prognostic model was proposed for LUAD that is capable of independently identifying patients at high risk for poor survival, suggesting a relationship between local immune status and prognosis.

FUBP1 promotes neuroblastoma proliferation via enhancing glycolysis-a new possible marker of malignancy for neuroblastoma

Background

Neuroblastoma (NB) is one of the deadliest paediatric solid tumours due to its rapid proliferative characteristics. Amplified copies of MYCN are considered the most important marker for the prediction of tumour relapse and progression in NB, but they were only detected in 20–30% of NB patients, indicating there might be other oncogenes in the development of NB. The far upstream element binding protein 1 (FUBP1) was first identified as a transcriptional regulator of the proto-oncogene MYC. However, the expression and role of FUBP1 in NB have not been documented.

Methods

FUBP1 expression was analysed from GEO database and verified by immunohistochemistry (IHC) and western blotting (WB) in NB tissues and cell lines. Cell proliferation and apoptosis were detected by Cell Counting Kit-8, Colony formation assay, EDU, TUNEL staining and flow cytometric analysis. Several glycolytic metabolites production was confirmed by ELISA and oxygen consuming rate (OCR). Luciferase assay, WB, chromatin immunoprecipitation (CHIP) were used to explore the mechanisms of the effect of FUBP1 on NB.

Results

FUBP1 mRNA levels were increased along with the increase in International Neuroblastoma Staging System (INSS) stages. High expression of FUBP1 with low N-Myc expression accounted for 44.6% of NB patient samples (n = 65). In addition, FUBP1 protein levels were remarkably increased with NB malignancy in the NB tissue microarray (NB: n = 65; ganglioneuroblastoma: n = 31; ganglioneuroma: n = 27). Furthermore, FUBP1 expression was negatively correlated with patient survival rate but positively correlated with ki67 content. In vitro experiments showed that FUBP1 promotes NB cell proliferation and inhibits cell apoptosis via enhancing glycolysis and ATP production. Mechanistically, FUBP1 inhibited the degradation of HIF1α via downregulation of Von Hippel-Lindau (VHL), the E3 ligase for HIF1α, resulting in upregulation of lactate dehydrogenase isoform B (LDHB) expression to enhance glycolysis. Overexpressed or silenced N-Myc could not regulate FUBP1 or LDHB levels.

Conclusions

Taken together, our findings demonstrate for the first time that elevated FUBP1 promotes NB glycolysis and growth by targeting HIF1α rather than N-Myc, suggesting that FUBP1 is a novel and powerful oncogene in the development of NB independent of N-Myc and may have potential in the diagnosis and treatment of NB.

Cascaded Wx: A Novel Prognosis-Related Feature Selection Framework in Human Lung Adenocarcinoma Transcriptomes

Artificial neural network-based analysis has recently been used to predict clinical outcomes in patients with solid cancers, including lung cancer. However, the majority of algorithms were not originally developed to identify genes associated with patients' prognoses. To address this issue, we developed a novel prognosis-related feature selection framework called Cascaded Wx (CWx). The CWx framework ranks features according to the survival of a given cohort by training neural networks with three different high- and low-risk groups in a cascaded fashion. We showed that this approach accurately identified features that best identify the patients' prognoses, compared to other feature selection algorithms, including the Cox proportional hazards and Coxnet models, when applied to The Cancer Genome Atlas lung adenocarcinoma (LUAD) transcriptome data. The prognostic potential of the top 100 genes identified by CWx outperformed or was comparable to those identified by the other methods as assessed by the concordance index (c-index). In addition, the top 100 genes identified by CWx were found to be associated with the Wnt signaling pathway, providing biologically relevant evidence for the value of these genes in predicting the prognosis of patients with LUAD. Further analyses of other cancer types showed that the genes identified by CWx had the highest prognostic values according to the c-index. Collectively, the CWx framework will potentially be of great use to prognosis-related biomarker discoveries in a variety of diseases.

An expression signature model to predict lung adenocarcinoma-specific survival

Background

The current TNM staging system plays a central role in lung adenocarcinoma (LUAD) prognosis. However, it may not adequately stratify the risk of tumor recurrence. With the aid of gene expression profiling, we identified 31 lncRNAs whose expressions in tumor tissues could be used as a risk indicator for the guidance of lung cancer therapy. This exploratory analysis may shed new light on identification of potential prognostic factors.

Materials and methods

A survival prediction scoring model was developed from the data that are publicly available in The Cancer Genome Atlas (TCGA) LUAD RNA Sequencing dataset. Multivariate Cox regression analysis and Kaplan–Meier analysis were performed on a cohort of 254 stage I lung carcinoma patients with survival records.

Results

Our model indicates that the panels comprising 31 lncRNAs are highly associated with overall survival (OS): 18.9% (95% CI: 10.4%–34.5%) and 89.5% (95% CI: 80.7%–99.2%) for the high- and low-risk group, respectively. The specificity and sensitivity of the model are verified, which show that the area under receiver operating characteristic curve yields 0.881, meaning our model has good accuracy and it is feasible for further applications.

Conclusion

The 31-lncRNA model might be able to predict OS in patients with LUAD with high accuracy. Its further applications in biomolecular experiments using clinical samples with independent cohorts of patients are needed to verify the results.

Prognostic significance of ANLN in lung adenocarcinoma

Anillin actin binding protein (ANLN) is a biomarker of cancer progression and is overexpressed in lung adenocarcinoma. The aim of the present study was to investigate the role of ANLN protein and RNA in the development of lung adenocarcinoma. The ANLN protein sequence was downloaded from The National Centre for Biotechnology information, RNA sequencing (RNA-seq) data was obtained from The Cancer Genome Atlas database. All immunohistochemical staining pictures were adapted from the Human Protein Atlas. PyMOL software was employed to predict protein functional changes in response to mutations. Gene Set Enrichments Analysis was employed for pathway analysis. The results indicated that ANLN experiences genetic change and overexpression at the RNA and protein levels in patients with lung adenocarcinoma. Kaplan-Meier survival curve analysis revealed significant differences between high and low RNA-seq expression levels in ANLN, and patients exhibiting higher expression of ANLN had a relatively poor prognosis. Pathway analysis demonstrated that ANLN was involved in developmental processes via the regulation of nuclear division' pathway. In conclusion, ANLN has potential for use as a diagnostic and prognostic biomarker to diagnoseand predict the outcome of lung adenocarcinoma.

A 16-gene expression signature to distinguish stage I from stage II lung squamous carcinoma

The present study aimed to perform screening of a gene signature for the discrimination and prognostic prediction of stage I and II lung squamous carcinoma. A microarray meta‑analysis was performed to identify differentially expressed genes (DEGs) between stage I and II lung squamous carcinoma samples in seven microarray datasets collected from the Gene Expression Omnibus database via the MetaQC and MetaDE package in R. The important DEGs were selected according to the betweenness centrality value of the protein‑protein interaction (PPI) network. Support vector machine (SVM) analysis was performed to screen the feature genes for discrimination and prognosis. One independent dataset downloaded from The Cancer Genome Atlas was used to validate the reliability. Pathway enrichment analysis was also performed for the feature genes. A total of 924 DEGs were identified to construct a PPI network consisting of 392 nodes and 686 edges. The top 100 of the 392 nodes were selected as crucial genes to construct an SVM classifier, and a 16‑gene signature (caveolin 1, eukaryotic translation elongation factor 1γ, casein kinase 2α1, tyrosine 3-monooxygenase/tryptophan 5-monooxygenase activation η, tyrosine 3‑monooxygenase/tryptophan 5‑monooxygenase activation θ, pleiotrophin, insulin receptor, insulin receptor substrate 1, 3‑phosphoinositide‑dependent protein kinase‑1, specificity protein 1, COP9 signalosome subunit 6, N‑myc downstream regulated gene 1, retinoid X receptor α, heat shock protein 90α A1, karyopherin subunit β1 and erythrocyte membrane protein band 4.1) with high discrimination accuracy was identified. This 16‑gene signature had significant prognostic value, and patients with stage II lung squamous carcinoma exhibited shorter survival rates, compared with those with stage I disease. Seven DEGs of the 16-gene signature were significantly involved in the phosphoinositide 3‑kinase‑Akt signaling pathway. The 16‑gene signature identified in the present study may be useful for stratifying the patients with stage I or II lung squamous carcinoma and predicting prognosis.

A Risk Stratification Model for Lung Cancer Based on Gene Coexpression Network and Deep Learning

Risk stratification model for lung cancer with gene expression profile is of great interest. Instead of previous models based on individual prognostic genes, we aimed to develop a novel system-level risk stratification model for lung adenocarcinoma based on gene coexpression network. Using multiple microarray, gene coexpression network analysis was performed to identify survival-related networks. A deep learning based risk stratification model was constructed with representative genes of these networks. The model was validated in two test sets. Survival analysis was performed using the output of the model to evaluate whether it could predict patients' survival independent of clinicopathological variables. Five networks were significantly associated with patients' survival. Considering prognostic significance and representativeness, genes of the two survival-related networks were selected for input of the model. The output of the model was significantly associated with patients' survival in two test sets and training set (p < 0.00001, p < 0.0001 and p = 0.02 for training and test sets 1 and 2, resp.). In multivariate analyses, the model was associated with patients' prognosis independent of other clinicopathological features. Our study presents a new perspective on incorporating gene coexpression networks into the gene expression signature and clinical application of deep learning in genomic data science for prognosis prediction.

Cell-surface marker discovery for lung cancer

Lung cancer is the leading cause of cancer deaths in the United States. Novel lung cancer targeted therapeutic and molecular imaging agents are needed to improve outcomes and enable personalized care. Since these agents typically cannot cross the plasma membrane while carrying cytotoxic payload or imaging contrast, discovery of cell-surface targets is a necessary initial step. Herein, we report the discovery and characterization of lung cancer cell-surface markers for use in development of targeted agents. To identify putative cell-surface markers, existing microarray gene expression data from patient specimens were analyzed to select markers with differential expression in lung cancer compared to normal lung. Greater than 200 putative cell-surface markers were identified as being overexpressed in lung cancers. Ten cell-surface markers (CA9, CA12, CXorf61, DSG3, FAT2, GPR87, KISS1R, LYPD3, SLC7A11 and TMPRSS4) were selected based on differential mRNA expression in lung tumors vs. non-neoplastic lung samples and other normal tissues, and other considerations involving known biology and targeting moieties. Protein expression was confirmed by immunohistochemistry (IHC) staining and scoring of patient tumor and normal tissue samples. As further validation, marker expression was determined in lung cancer cell lines using microarray data and Kaplan–Meier survival analyses were performed for each of the markers using patient clinical data. High expression for six of the markers (CA9, CA12, CXorf61, GPR87, LYPD3, and SLC7A11) was significantly associated with worse survival. These markers should be useful for the development of novel targeted imaging probes or therapeutics for use in personalized care of lung cancer patients.

Generating a robust prediction model for stage I lung adenocarcinoma recurrence after surgical resection

Lung cancer mortality remains high even after successful resection. Adjuvant treatment benefits stage II and III patients, but not stage I patients, and most studies fail to predict recurrence in stage I patients. Our study included 211 lung adenocarcinoma patients (stages I-IIIA; 81% stage I) who received curative resections at Taipei Veterans General Hospital between January 2001 and December 2012. We generated a prediction model using 153 samples, with validation using an additional 58 clinical outcome-blinded samples. Gene expression profiles were generated using formalin-fixed, paraffin-embedded tissue samples and microarrays. Data analysis was performed using a supervised clustering method. The prediction model generated from mixed stage samples successfully separated patients at high vs. low risk for recurrence. The validation tests hazard ratio (HR = 4.38) was similar to that of the training tests (HR = 4.53), indicating a robust training process. Our prediction model successfully distinguished high- from low-risk stage IA and IB patients, with a difference in 5-year disease-free survival between high- and low-risk patients of 42% for stage IA and 45% for stage IB (p < 0.05). We present a novel and effective model for identifying lung adenocarcinoma patients at high risk for recurrence who may benefit from adjuvant therapy. Our prediction performance of the difference in disease free survival between high risk and low risk groups demonstrates more than two fold improvement over earlier published results.

ANLN is a prognostic biomarker independent of Ki-67 and essential for cell cycle progression in primary breast cancer

BACKGROUND

Anillin (ANLN), an actin-binding protein required for cytokinesis, has recently been presented as part of a prognostic marker panel in breast cancer. The objective of the current study was to further explore the prognostic and functional value of ANLN as a single biomarker in breast cancer.

METHODS

Immunohistochemical assessment of ANLN protein expression was performed in two well characterized breast cancer cohorts (n = 484) with long-term clinical follow-up data and the results were further validated at the mRNA level in a publicly available transcriptomics dataset. The functional relevance of ANLN was investigated in two breast cancer cell lines using RNA interference.

RESULTS

High nuclear fraction of ANLN in breast tumor cells was significantly associated with large tumor size, high histological grade, high proliferation rate, hormone receptor negative tumors and poor prognosis in both examined cohorts. Multivariable analysis showed that the association between ANLN and survival was significantly independent of age in cohort I and significantly independent of proliferation, as assessed by Ki-67 expression in tumor cells, age, tumor size, ER and PR status, HER2 status and nodal status in cohort II. Analysis of ANLN mRNA expression confirmed that high expression of ANLN was significantly correlated to poor overall survival in breast cancer patients. Consistent with the role of ANLN during cytokinesis, transient knock-down of ANLN protein expression in breast cancer cell lines resulted in an increase of senescent cells and an accumulation of cells in the G2/M phase of the cell cycle with altered cell morphology including large, poly-nucleated cells. Moreover, ANLN siRNA knockdown also resulted in decreased expression of cyclins D1, A2 and B1.

CONCLUSIONS

ANLN expression in breast cancer cells plays an important role during cell division and a high fraction of nuclear ANLN expression in tumor cells is correlated to poor prognosis in breast cancer patients, independent of Ki-67, tumor size, hormone receptor status, HER2 status, nodal status and age.

Integrative analysis of lung development-cancer expression associations reveals the roles of signatures with inverse expression patterns

Recent studies have focused on exploring the associations between organ development and malignant tumors; however, the clinical relevance of the development signatures was inadequately addressed in lung cancer. In this study, we explored the associations between lung development and lung cancer progression by analyzing a total of two development and seven cancer datasets. We identified representative expression patterns (continuously up- and down-regulated) from development and cancer profiles, and inverse pattern associations were observed at both the gene and functional levels. Furthermore, we dissected the biological processes dominating the associations, and found that proliferation and immunity were respectively involved in the two inverse development-cancer expression patterns. Through sub-pathway analysis of the signatures with inverse expression patterns, we finally identified a 13-gene risk signature from the cell cycle sub-pathway, and evaluated its predictive performance for lung cancer patient clinical outcome using independent cohorts. Our findings indicated that the integrative analysis of development and cancer expression patterns provided a framework for identifying effective molecular signatures for clinical utility.

Combined clinical and genomic signatures for the prognosis of early stage non-small cell lung cancer based on gene copy number alterations

Background

The development of a more refined prognostic methodology for early non-small cell lung cancer (NSCLC) is an unmet clinical need. An accurate prognostic tool might help to select patients at early stages for adjuvant therapies.

Results

A new integrated bioinformatics searching strategy, that combines gene copy number alterations and expression, together with clinical parameters was applied to derive two prognostic genomic signatures. The proposed methodology combines data from patients with and without clinical data with a priori information on the ability of a gene to be a prognostic marker. Two initial candidate sets of 513 and 150 genes for lung adenocarcinoma (ADC) and squamous cell carcinoma (SCC), respectively, were generated by identifying genes which have both: a) significant correlation between copy number and gene expression, and b) significant prognostic value at the gene expression level in external databases. From these candidates, two panels of 7 (ADC) and 5 (SCC) genes were further identified via semi-supervised learning. These panels, together with clinical data (stage, age and sex), were used to construct the ADC and SCC hazard scores combining clinical and genomic data. The signatures were validated in two independent datasets (n = 73 for ADC, n = 97 for SCC), confirming that the prognostic value of both clinical-genomic models is robust, statistically significant (P = 0.008 for ADC and P = 0.019 for SCC) and outperforms both the clinical models (P = 0.060 for ADC and P = 0.121 for SCC) and the genomic models applied separately (P = 0.350 for ADC and P = 0.269 for SCC).

Conclusion

The present work provides a methodology to generate a robust signature using copy number data that can be potentially used to any cancer. Using it, we found new prognostic scores based on tumor DNA that, jointly with clinical information, are able to predict overall survival (OS) in patients with early-stage ADC and SCC.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-015-1935-0) contains supplementary material, which is available to authorized users.

Critical limitations of prognostic signatures based on risk scores summarized from gene expression levels: a case study for resected stage I non-small-cell lung cancer

Most of current gene expression signatures for cancer prognosis are based on risk scores, usually calculated as some summaries of expression levels of the signature genes, whose applications require presetting risk score thresholds and data normalization. In this study, we demonstrate the critical limitations of such type of signatures that the risk scores of samples will change greatly when they are normalized together with different samples, which would induce spurious risk classification and difficulty in clinical settings, and the risk scores of independent samples are incomparable if data normalization is not adopted. To overcome these limitations, we propose a rank-based method to extract a prognostic gene pair signature for overall survival of stage I non-small-cell lung cancer. The prognostic gene pair signature is verified in three integrated data sets detected by different laboratories with different microarray platforms. We conclude that, different from the type of signatures based on risk scores summarized from gene expression levels, the rank-based signatures could be robustly applied at the individualized level to independent clinical samples assessed in different laboratories.

Vav1 promotes lung cancer growth by instigating tumor-microenvironment cross-talk via growth factor secretion

Vav1 is a signal transducer that functions as a scaffold protein and a regulator of cytoskeleton organization in the hematopoietic system, where it is exclusively expressed. Recently, Vav1 was shown to be involved in diverse human cancers, including lung cancer. We demonstrate that lung cancer cells that abnormally express Vav1 secrete growth factors in a Vav1-dependent manner. Transcriptome analysis demonstrated that Vav1 depletion results in a marked reduction in the expression of colony-stimulating-factor-1 (CSF1), a hematopoietic growth factor. The association between Vav1 expression and CSF1 was further supported by signal transduction experiments, supporting involvement of Vav1 in regulating lung cancer secretome. Blocking of ERK phosphorylation, led to a decrease in CSF1 transcription, thus suggesting a role for ERK, a downstream effector of Vav1, in CSF1 expression. CSF1-silenced cells exhibited reduced focus formation, proliferation abilities, and growth in NOD/SCID mice. CSF1-silenced H358 cells resulted in significantly smaller tumors, showing increased fibrosis and a decrease in tumor infiltrating macrophages. Finally, immunohistochemical analysis of primary human lung tumors revealed a positive correlation between Vav1 and CSF1 expression, which was associated with tumor grade. Additional results presented herein suggest a potential cross-talk between cancer cells and the microenvironment controlled by CSF1/Vav1 signaling pathways.

Membrane carbonic anhydrase IX expression and relapse risk in resected stage I-II non-small-cell lung cancer

BACKGROUND

Adjuvant chemotherapy reduces recurrences of non-small-cell lung cancer (NSCLC). To determine which patients need adjuvant chemotherapy, we assessed factors associated with time to relapse (TTR).

METHODS

In 230 resected stage I-II NSCLCs, we correlated immunohistochemistry scores for factors associated with cell growth rate, growth regulation, hypoxia, cell survival, and cell death with TTR.

RESULTS

With a median follow-up of 82 months (1-158) for those alive and relapse free at last follow-up, median time to recurrence was not reached. The 2- and 5-year probabilities of maintaining freedom from recurrence were 80.7% (95% confidence interval, 75.3%, 86.4%) and 74.6% (95% confidence interval, 68.6%, 81.2%), respectively. TTR curves flattened at an apparent cure rate of 70%. In multicovariate Cox models, factors correlating with shorter TTR were membranous carbonic anhydrase IX (mCAIX) staining (any versus none, hazard ratio = 2.083, p = 0.023) and node stage (N1 versus N0, hazard ratio = 2.591, p = 0.002). mCAIX scores correlated positively with tumor size, grade, squamous histology, necrosis, mitoses, Ki67, p53, nuclear DNA methyltransferase 1, and cytoplasmic enhancer-of-split-and-hairy-related protein, and they correlated inversely with papillary histology, epidermal growth factor receptor mutation (trend), copper transporter-1, and cytoplasmic hypoxia-inducible factor-1α, vascular endothelial growth factor, DNA methyltransferase 1, and excision repair cross-complementing rodent repair deficiency, complementation group 1.

CONCLUSION

Nodal stage and mCAIX immunohistochemistry were the strongest independent predictors of shorter TTR in resected NSCLCs. mCAIX correlated with tumor size, markers of tumor proliferation and necrosis, and tumor genetic characteristics, and it paradoxically correlated inversely with the hypoxia markers, hypoxia-inducible factor-1α and vascular endothelial growth factor. Presence of mCAIX could help determine patients with high risk of recurrence who might require adjuvant chemotherapy.

Apolipoprotein C1 (APOC1) as a novel diagnostic and prognostic biomarker for lung cancer: A marker phase I trial

BACKGROUND

Tumor cells continuously evolve over time in response to host pressures. However, explanations as to how tumor cells are influenced by the inflammatory tumor microenvironment over time are, to date, poorly defined. We hypothesized that prognostic biomarkers could be obtained by exploring the expression of inflammation-associated genes between early and late stage lung cancer tumor samples.

METHODS

Candidate inflammation-associated genes, apolipoprotein C-1 (APOC1), MMP1, KMO)1, CXCL5, CXCL)7, IL-1α, IL-1β, TNF-α and IL-6 were verified by real-time quantitative polymerase chain reaction. Gene expression profiles and immunofluorescence staining of 30 lung cancer tissues were compared.

RESULTS

Expressions of APOC1 and IL-6 mRNA on tumor tissues in late stage disease were significantly higher than in early stage lung cancer samples. Immunofluorescence staining of tumor samples showed that the expression of APOC1 gradually increased from early to late stage in lung cancer patients. The expression levels of IL-6 and APOC1 in tumor samples were positively correlated; however, no prognostic value of APOC1 can be identified in serum samples.

CONCLUSIONS

We found that the level of tumor APOC1 was highly expressed in late stage lung cancer. Further research is warranted to determine the molecular mechanisms underlying the cross talk of APOC1 and IL-6 in tumor progression. An expanded sample size marker phase II study may lead to the discovery of new lung cancer therapeutics targeting APOC1.

Knockdown of ANLN by lentivirus inhibits cell growth and migration in human breast cancer

Anillin (ANLN), an actin-binding protein, is required for cytokinesis. Recently, ANLN has been identified as a biomarker in diverse human cancers; however, the precise role of ANLN in breast cancer remains unclear. In this study, we firstly detected the expression of ANLN in 71 patients with breast cancer by immunohistochemistry, and found ANLN was highly expressed in breast cancer tissues. To evaluate the function of ANLN in breast cancer cells, we employed lentivirus-mediated RNA interference to knock down ANLN expression in two human breast cancer cell lines, MDA-MB-231, and ZR-75-30. Knockdown of ANLN remarkably inhibited the proliferation rate and colony formation ability of both breast cancer cell lines. Moreover, flow cytometry analysis showed that depletion of ANLN in MDA-MB-231 cells blocked the cell cycle progression, with more cells delayed at G2/M phase, due to phosphorylation of Cdc2 and suppression of Cyclin D1. Furthermore, knockdown of ANLN strongly suppressed the migration of breast cancer cells, strengthening the evidence that ANLN could be involved in breast cancer progression. Our results may suggest ANLN as a potential target candidate in breast cancer.

Molecular profiles of non-small cell lung cancers in cigarette smoking and never-smoking patients

PURPOSE

Molecular features of non-small cell lung cancer (NSCLC) in never-smokers are not well recognized. We assessed the expression of genes potentially related to lung cancer etiology in smoking vs. never-smoking NSCLC patients.

METHODS

We assayed frozen tumor samples from surgically resected 31 never-smoking and 54 clinically pair-matched smoking NSCLC patients, and from corresponding normal lung tissue from 27 and 43 patients, respectively. Expression of 21 genes, including cell membrane kinases, sex hormone receptors, transcription factors, growth factors and others was assessed by reverse transcription - quantitative PCR.

RESULTS

Expression of 5 genes was significantly higher in tumors of non-smokers vs. smokers: CSF1R (p<0.0001), RRAD (p<0.0001), PR (p=0.0004), TGFBR2 (p=0.0027) and EPHB6 (p=0.0033). Expression of AKR1B10 (p<0.0001), CDKN2A (p<0.0001), CHRNA6 (p<0.0001), SOX9 (p<0.0001), survivin (p<0.0001) and ER2 (p=0.002) was significantly higher in tumors compared to normal lung tissue. Expression of AR (p<0.0001), EPHB6 (p<0.0001), PR (p<0.0001), TGFBR2 (p<0.0001), TGFBR3 (p<0.0001), ER1 (p=0.0006) and DLG1 (p=0.0016) was significantly lower in tumors than in normal lung tissue. Expression of IGF2 was higher in tumors than in healthy lung tissue in never-smokers (p=0.003), and expression of AHR (p<0.0001), CSF1R (p<0.0001) and RRAD (p<0.0001) was lower in tumors than in healthy lung tissue in smokers.

CONCLUSION

Expression of several genes in NSCLC is strongly related to smoking history. Lower expression of PR and higher expression of ER2 in tumors suggests a possibility of hormonal therapeutic intervention in selected NSCLC patients. Distinct molecular features of NSCLC in never-smokers, e.g. CHRNA6 upregulation, may prompt new treatment strategies.

Using logistic regression to improve the prognostic value of microarray gene expression data sets: application to early-stage squamous cell carcinoma of the lung and triple negative breast carcinoma

BACKGROUND

Numerous microarray-based prognostic gene expression signatures of primary neoplasms have been published but often with little concurrence between studies, thus limiting their clinical utility. We describe a methodology using logistic regression, which circumvents limitations of conventional Kaplan Meier analysis. We applied this approach to a thrice-analyzed and published squamous cell carcinoma (SQCC) of the lung data set, with the objective of identifying gene expressions predictive of early death versus long survival in early-stage disease. A similar analysis was applied to a data set of triple negative breast carcinoma cases, which present similar clinical challenges.

METHODS

Important to our approach is the selection of homogenous patient groups for comparison. In the lung study, we selected two groups (including only stages I and II), equal in size, of earliest deaths and longest survivors. Genes varying at least four-fold were tested by logistic regression for accuracy of prediction (area under a ROC plot). The gene list was refined by applying two sliding-window analyses and by validations using a leave-one-out approach and model building with validation subsets. In the breast study, a similar logistic regression analysis was used after selecting appropriate cases for comparison.

RESULTS

A total of 8594 variable genes were tested for accuracy in predicting earliest deaths versus longest survivors in SQCC. After applying the two sliding window and the leave-one-out analyses, 24 prognostic genes were identified; most of them were B-cell related. When the same data set of stage I and II cases was analyzed using a conventional Kaplan Meier (KM) approach, we identified fewer immune-related genes among the most statistically significant hits; when stage III cases were included, most of the prognostic genes were missed. Interestingly, logistic regression analysis of the breast cancer data set identified many immune-related genes predictive of clinical outcome.

CONCLUSIONS

Stratification of cases based on clinical data, careful selection of two groups for comparison, and the application of logistic regression analysis substantially improved predictive accuracy in comparison to conventional KM approaches. B cell-related genes dominated the list of prognostic genes in early stage SQCC of the lung and triple negative breast cancer.

Carbonic anhydrase IX, a hypoxia-induced catalytic component of the pH regulating machinery in tumors

Acidic tissue microenvironment contributes to tumor progression via multiple effects including the activation of angiogenic factors and proteases, reduced cell-cell adhesion, increased migration and invasion, etc. In addition, intratumoral acidosis can influence the uptake of anticancer drugs and modulate the response of tumors to conventional therapy. Acidification of the tumor microenvironment often develops due to hypoxia-triggered oncogenic metabolism, which leads to the extensive production of lactate, protons, and carbon dioxide. In order to avoid intracellular accumulation of the acidic metabolic products, which is incompatible with the survival and proliferation, tumor cells activate molecular machinery that regulates pH by driving transmembrane inside-out and outside-in ion fluxes. Carbonic anhydrase IX (CA IX) is a hypoxia-induced catalytic component of the bicarbonate import arm of this machinery. Through its catalytic activity, CA IX directly participates in many acidosis-induced features of tumor phenotype as demonstrated by manipulating its expression and/or by in vitro mutagenesis. CA IX can function as a survival factor protecting tumor cells from hypoxia and acidosis, as a pro-migratory factor facilitating cell movement and invasion, as a signaling molecule transducing extracellular signals to intracellular pathways (including major signaling and metabolic cascades) and converting intracellular signals to extracellular effects on adhesion, proteolysis, and other processes. These functional implications of CA IX in cancer are supported by numerous clinical studies demonstrating the association of CA IX with various clinical correlates and markers of aggressive tumor behavior. Although our understanding of the many faces of CA IX is still incomplete, existing knowledge supports the view that CA IX is a biologically and clinically relevant molecule, exploitable in anticancer strategies aimed at targeting adaptive responses to hypoxia and/or acidosis.

Analytical validation of a practical molecular assay prognostic of survival in nonsquamous non-small cell lung cancer

A molecular assay prognostic of survival in resected nonsquamous non-small cell lung cancer designed to meet the need for improved risk stratification in early-stage disease has recently been described. This assay measures the expression levels of 14 genes using RNA extracted from formalin-fixed, paraffin-embedded (FFPE) tissues. The assay underwent blinded clinical validation in 2 large international cohorts involving approximately 1500 patients; the analytical precision and reproducibility of this assay, however, have not yet been reported. For each of the 14 TaqMan quantitative polymerase chain reaction (PCR) primer and probe sets used in the molecular prognostic assay, the linear range, PCR efficiency, limits of blank, limits of quantitation, and quantitative bias were determined using serial dilutions of pooled RNA extracted from FFPE samples. The reproducibility of the entire molecular assay was determined by performing repeat testing of FFPE samples over multiple days. The linear range of individual quantitative TaqMan PCR primer and probe sets was between 2(10)- and 2(15)-fold input RNA. The median C(T) of the quantitative PCR primer and probe sets at 10 ng of input RNA was 24.3; the median efficiency was 91.2%. The median quantitative bias across all quantitative PCR primer and probe sets was 0.75% (range, 0.32% to 1.32%). In repeat testing, the mean SD of the risk score (scaled from 1 to 100) was 2.18, with a mean coefficient of variation of 0.08. The molecular prognostic assay presented in this study demonstrates high precision and reproducibility, validating its clinical utility as a reliable prognostic tool that can contribute to the management of patients with early-stage disease.

Lung Cancer Gene Signatures and Clinical Perspectives

Microarrays have been used for more than two decades in preclinical research. The tumor transcriptional profiles were analyzed to select cancer-associated genes for in-deep functional characterization, to stratify tumor subgroups according to the histopathology or diverse clinical courses, and to assess biological and cellular functions behind these gene sets. In lung cancer-the main type of cancer causing mortality worldwide-biomarker research focuses on different objectives: the early diagnosis of curable tumor diseases, the stratification of patients with prognostic unfavorable operable tumors to assess the need for further therapy regimens, or the selection of patients for the most efficient therapies at early and late stages. In non-small cell lung cancer, gene and miRNA signatures are valuable to differentiate between the two main subtypes' squamous and non-squamous tumors, a discrimination which has further implications for therapeutic schemes. Further subclassification within adenocarcinoma and squamous cell carcinoma has been done to correlate histopathological phenotype with disease outcome. Those tumor subgroups were assigned by diverse transcriptional patterns including potential biomarkers and therapy targets for future diagnostic and clinical applications. In lung cancer, none of these signatures have entered clinical routine for testing so far. In this review, the status quo of lung cancer gene signatures in preclinical and clinical research will be presented in the context of future clinical perspectives.

Novel medium-term carcinogenesis model for lung squamous cell carcinoma induced by N-nitroso-tris-chloroethylurea in mice

Targeted treatments for lung cancer based on pathological diagnoses are required to enhance therapeutic efficacy. There are few well-established animal models for lung squamous cell carcinoma although several highly reproducible mouse models for lung adenoma and adenocarcinoma are available. This study was carried out to establish a new lung squamous cell carcinoma mouse model. In the first experiment, female A/J mice were painted topically on back skin twice weekly with 75 μL 0.013 M N-nitroso-tris-chloroethylurea for 2, 4, and 8 weeks (n = 15-20 per group) as initiation of lung lesions, and surviving mice were killed at 18 weeks. In the second experiment, mice were treated as above for 4 weeks and killed at 6, 12, or 18 weeks (n = 3 per group). Lung lobes were subjected to histopathological, immunohistochemical, immunoblotting, and ultrastructural analyses. In the case of treatment for 2, 4, and 8 weeks, incidences of lung squamous cell carcinoma were 25, 54, and 71%, respectively. Cytokeratin 5/6 and epidermal growth factor receptor were clearly expressed in dysplasia and squamous cell carcinoma. Desmosomes and tonofilaments developed in the squamous cell carcinoma. Considering the carcinogenesis model, we conclude that 2 or 4 weeks of N-nitroso-tris-chloroethylurea treatment may be suitable for investigating new chemicals for promotional or suppressive effects on lung squamous cell carcinoma.

Yin Yang gene expression ratio signature for lung cancer prognosis

Many studies have established gene expression-based prognostic signatures for lung cancer. All of these signatures were built from training data sets by learning the correlation of gene expression with the patients' survival time. They require all new sample data to be normalized to the training data, ultimately resulting in common problems of low reproducibility and impracticality. To overcome these problems, we propose a new signature model which does not involve data training. We hypothesize that the imbalance of two opposing effects in lung cancer cells, represented by Yin and Yang genes, determines a patient's prognosis. We selected the Yin and Yang genes by comparing expression data from normal lung and lung cancer tissue samples using both unsupervised clustering and pathways analyses. We calculated the Yin and Yang gene expression mean ratio (YMR) as patient risk scores. Thirty-one Yin and thirty-two Yang genes were identified and selected for the signature development. In normal lung tissues, the YMR is less than 1.0; in lung cancer cases, the YMR is greater than 1.0. The YMR was tested for lung cancer prognosis prediction in four independent data sets and it significantly stratified patients into high- and low-risk survival groups (p = 0.02, HR = 2.72; p = 0.01, HR = 2.70; p = 0.007, HR = 2.73; p = 0.005, HR = 2.63). It also showed prediction of the chemotherapy outcomes for stage II & III. In multivariate analysis, the YMR risk factor was more successful at predicting clinical outcomes than other commonly used clinical factors, with the exception of tumor stage. The YMR can be measured in an individual patient in the clinic independent of gene expression platform. This study provided a novel insight into the biology of lung cancer and shed light on the clinical applicability.

Integrating genomics and proteomics-oriented biomarkers to comprehend lung cancer

BACKGROUND

Lung cancer is the leading cause of cancer deaths worldwide. Recent years have brought tremendous progress in the development of genomic and proteomic platforms to study lung cancer progression and biomarker identification.

OBJECTIVE

To evaluate and integrate potential innovations of 'omics' (e.g., genomics and proteomics) technologies in dissecting biomarkers for lung cancer.

METHODS

Omics technologies permit simultaneous monitoring of many hundreds or thousands of macro and small molecules, as well as functional monitoring of multiple pivotal cellular pathways. Discussion follows to explore the principal challenges in the development of cancer biomarkers integrating genomics with proteomics data sets with their functional counterparts in conjunction with clinical data.

RESULTS/CONCLUSION

Sets of genes and gene interactions affecting different subsets of cancers can be determined using genomics in lung cancer. Proteomic studies have generated numerous functional data sets of potential diagnostic, prognostic and therapeutic significance in lung cancer. It is likely that omics will take a central place in the understanding, diagnosis, monitoring and treatment of lung cancer. Here the potential benefits and pitfalls of these methodologies are reviewed for the faster discovery of therapeutically valuable biomarkers for lung cancer.

Biomarkers and molecular testing for early detection, diagnosis, and therapeutic prediction of lung cancer

The search for biomarkers in the management of lung cancer involves the use of multiple platforms to examine changes in gene, protein, and microRNA expression. Multiple studies have been published in an attempt to describe early detection, diagnostic, prognostic, and predictive biomarkers using chiefly tissues and blood elements. Studies are characterized by a lack of commonality of specific biomarkers, and a lack of validated, clinically useful markers. The future of biomarker discovery as a means of tailoring therapy for patients with lung cancer will involve next-generation sequencing along with collaborative efforts to integrate and validate candidate markers.

Cancer module genes ranking using kernelized score functions

Background

Co-expression based Cancer Modules (CMs) are sets of genes that act in concert to carry out specific functions in different cancer types, and are constructed by exploiting gene expression profiles related to specific clinical conditions or expression signatures associated to specific processes altered in cancer. Unfortunately, genes involved in cancer are not always detectable using only expression signatures or co-expressed sets of genes, and in principle other types of functional interactions should be exploited to obtain a comprehensive picture of the molecular mechanisms underlying the onset and progression of cancer.

Results

We propose a novel semi-supervised method to rank genes with respect to CMs using networks constructed from different sources of functional information, not limited to gene expression data. It exploits on the one hand local learning strategies through score functions that extend the guilt-by-association approach, and on the other hand global learning strategies through graph kernels embedded in the score functions, able to take into account the overall topology of the network. The proposed kernelized score functions compare favorably with other state-of-the-art semi-supervised machine learning methods for gene ranking in biological networks and scales well with the number of genes, thus allowing fast processing of very large gene networks.

Conclusions

The modular nature of kernelized score functions provides an algorithmic scheme from which different gene ranking algorithms can be derived, and the results show that using integrated functional networks we can successfully predict CMs defined mainly through expression signatures obtained from gene expression data profiling. A preliminary analysis of top ranked "false positive" genes shows that our approach could be in perspective applied to discover novel genes involved in the onset and progression of tumors related to specific CMs.

Gene expression from bronchoscopy obtained tumour samples as a predictor of outcome in advanced inoperable lung cancer

Background

Several studies have shown the prognostic and predictive potential of molecular markers in combined therapy for lung cancer. Most of them referred, however, to operable early stage NSCLC. The aim of the present study is to correlate the expression of multiple mRNA markers in bronchoscopy obtained cancer specimens with clinical outcome of advanced lung cancer.

Methods

Bronchoscopy cancer specimens were taken from 123 patients with radiological diagnosis of advanced lung tumor. Out of 123 patients 50 were diagnosed with squamous cell cancer, 17 with adenocarcinoma, 12 with NOS, 32 with SCLC and one with large cell neuroendocrinal cancer. In 11 patients other tumours were diagnosed. The group was heterogeneous with respect to clinical stage, performance of the patients and treatment. Quantitative real time PCR was carried out by ABI 7900 HT machine, with Universal Probe Library (Roche) fluorescent probes. The genes selected for the analysis were ERCC1, EGFR, BRCA1, CSF1, CA9, DUSP6, STAT1, ERBB3, MMD, FN1, and CDKN1B.

Results

More than 50 ng of RNA (the amount considered sufficient for the analysis) was isolated in 82 out of 112 lung cancer specimens (73%), including 60/80 (75.0%) of NSCLC specimens and 22/32 (68,7%) of SCLC samples. The highest Cohen’s κ coefficient for discrimination between small cell, squamous cell and adenocarcinoma was found for CDKN1B, CSF and EGFR1 (κ = 0.177, p = 0.0041). A multivariate Cox regression model has shown a significant impact of clinical stage (p<0.001, RR = 4.19), ERCC1 (p = 0.01, RR = 0.43) and CA9 (p = 0.03, RR = 2.11) expression on overall survival in a group of 60 patients with NSCLC.

Conclusion

These results show the feasibility of multiple gene expression analysis in bronchoscopy obtained cancer specimens as prognostic markers in radiotherapy and chemotherapy for advanced lung cancer. A limiting factor was relatively high proportion of samples from which sufficient amount of RNA could not be isolated.

Quantitative proteomics reveals regulation of karyopherin subunit alpha-2 (KPNA2) and its potential novel cargo proteins in nonsmall cell lung cancer

The process of nucleocytoplasmic shuttling is mediated by karyopherins. Dysregulated expression of karyopherins may trigger oncogenesis through aberrant distribution of cargo proteins. Karyopherin subunit alpha-2 (KPNA2) was previously identified as a potential biomarker for nonsmall cell lung cancer by integration of the cancer cell secretome and tissue transcriptome data sets. Knockdown of KPNA2 suppressed the proliferation and migration abilities of lung cancer cells. However, the precise molecular mechanisms underlying KPNA2 activity in cancer remain to be established. In the current study, we applied gene knockdown, subcellular fractionation, and stable isotope labeling by amino acids in cell culture-based quantitative proteomic strategies to systematically analyze the KPNA2-regulating protein profiles in an adenocarcinoma cell line. Interaction network analysis revealed that several KPNA2-regulating proteins are involved in the cell cycle, DNA metabolic process, cellular component movements and cell migration. Importantly, E2F1 was identified as a potential novel cargo of KPNA2 in the nuclear proteome. The mRNA levels of potential effectors of E2F1 measured using quantitative PCR indicated that E2F1 is one of the "master molecule" responses to KPNA2 knockdown. Immunofluorescence staining and immunoprecipitation assays disclosed co-localization and association between E2F1 and KPNA2. An in vitro protein binding assay further demonstrated that E2F1 interacts directly with KPNA2. Moreover, knockdown of KPNA2 led to subcellular redistribution of E2F1 in lung cancer cells. Our results collectively demonstrate the utility of quantitative proteomic approaches and provide a fundamental platform to further explore the biological roles of KPNA2 in nonsmall cell lung cancer.

Molecular signatures of lung cancer: defining new diagnostic and therapeutic paradigms

Molecular profiling holds great promise for improving our ability to diagnose, prognosticate, and select individualized treatments for lung cancer patients. However, using multidimensional data and novel technologies to derive these profiles is limited by our ability to employ the assay in a clinical scenario where it can impact the course of disease. Although many molecular signatures have been reported in lung cancer, as of yet, few have been sufficiently validated for widespread clinical use. Recently, several novel signatures have been reported, which address critical aspects of patient care and/or demonstrate improved efforts for appropriate clinical validation. Here, we present our opinion on the current state of the field of molecular signatures in lung cancer.

A practical molecular assay to predict survival in resected non-squamous, non-small-cell lung cancer: development and international validation studies

BACKGROUND

The frequent recurrence of early-stage non-small-cell lung cancer (NSCLC) is generally attributable to metastatic disease undetected at complete resection. Management of such patients depends on prognostic staging to identify the individuals most likely to have occult disease. We aimed to develop and validate a practical, reliable assay that improves risk stratification compared with conventional staging.

METHODS

A 14-gene expression assay that uses quantitative PCR, runs on formalin-fixed paraffin-embedded tissue samples, and differentiates patients with heterogeneous statistical prognoses was developed in a cohort of 361 patients with non-squamous NSCLC resected at the University of California, San Francisco. The assay was then independently validated by the Kaiser Permanente Division of Research in a masked cohort of 433 patients with stage I non-squamous NSCLC resected at Kaiser Permanente Northern California hospitals, and on a cohort of 1006 patients with stage I-III non-squamous NSCLC resected in several leading Chinese cancer centres that are part of the China Clinical Trials Consortium (CCTC).

FINDINGS

Kaplan-Meier analysis of the Kaiser validation cohort showed 5 year overall survival of 71·4% (95% CI 60·5-80·0) in low-risk, 58·3% (48·9-66·6) in intermediate-risk, and 49·2% (42·2-55·8) in high-risk patients (p(trend)=0·0003). Similar analysis of the CCTC cohort indicated 5 year overall survivals of 74·1% (66·0-80·6) in low-risk, 57·4% (48·3-65·5) in intermediate-risk, and 44·6% (40·2-48·9) in high-risk patients (p(trend)<0·0001). Multivariate analysis in both cohorts indicated that no standard clinical risk factors could account for, or provide, the prognostic information derived from tumour gene expression. The assay improved prognostic accuracy beyond National Comprehensive Cancer Network criteria for stage I high-risk tumours (p<0·0001), and differentiated low-risk, intermediate-risk, and high-risk patients within all disease stages.

INTERPRETATION

Our practical, quantitative-PCR-based assay reliably identified patients with early-stage non-squamous NSCLC at high risk for mortality after surgical resection.

FUNDING

UCSF Thoracic Oncology Laboratory and Pinpoint Genomics.

MicroRNA profiling and prediction of recurrence/relapse-free survival in stage I lung cancer

About 30% stage I non-small cell lung cancer (NSCLC) patients undergoing resection will recur. Robust prognostic markers are required to better manage therapy options. MicroRNAs (miRNAs) are a class of small non-coding RNAs of 19-25 nt and play important roles in gene regulation in human cancers. The purpose of this study is to identify miRNA expression profiles that would better predict prognosis of stage I NSCLC. MiRNAs extracted from 527 stage I NSCLC patients were profiled on the human miRNA expression profiling v2 panel (Illumina). The expression profiles were analyzed for their association with cancer subtypes, lung cancer brain metastasis and recurrence/relapse free survival (RFS). MiRNA expression patterns between lung adenocarcinoma and squamous cell carcinoma differed significantly with 171 miRNAs, including Let-7 family members and miR-205. Ten miRNAs associated with brain metastasis were identified including miR-145*, which inhibit cell invasion and metastasis. Two miRNA signatures that are highly predictive of RFS were identified. The first contained 34 miRNAs derived from 357 stage I NSCLC patients independent of cancer subtype, whereas the second containing 27 miRNAs was adenocarcinoma specific. Both signatures were validated using formalin-fixed paraffin embedded and/or fresh frozen tissues in independent data set with 170 stage I patients. Our findings have important prognostic or therapeutic implications for the management of stage I lung cancer patients. The identified miRNAs hold great potential as targets for histology-specific treatment or prevention and treatment of recurrent disease.

Adjuvant chemotherapy for resected non-small-cell lung cancer: future perspectives for clinical research

Adjuvant chemotherapy for non-small-cell lung carcinoma (NSCLC) is a debated issue in clinical oncology. Although it is considered a standard for resected stage II-IIIA patients according to the available guidelines, many questions are still open. Among them, it should be acknowledged that the treatment for stage IB disease has shown so far a limited (if sizable) efficacy, the role of modern radiotherapies requires to be evaluated in large prospective randomized trials and the relative impact of age and comorbidities should be weighted to assess the reliability of the trials' evidences in the context of the everyday-practice. In addition, a conclusive evidence of the best partner for cisplatin is currently awaited as well as a deeper investigation of the fading effect of chemotherapy over time. The limited survival benefit since first studies were published and the lack of reliable prognostic and predictive factors beyond pathological stage, strongly call for the identification of bio-molecular markers and classifiers to identify which patients should be treated and which drugs should be used. Given the disappointing results of targeted therapy in this setting have obscured the initial promising perspectives, a biomarker-selection approach may represent the basis of future trials exploring adjuvant treatment for resected NSCLC.