Prognostic markers in lung cancer: is it ready for prime time?

TiRNA-Gly-GCC-002 is associated with progression in patients with hepatocellular carcinoma

Background

The transfer RNA (tRNA)-derived fragments, generated by the cleavage of mature and pre-tRNAs, play a vital role in the tumorigenesis and progression of hepatocellular carcinoma (HCC). However, the relationship between tRNA-derived fragments and the prognosis of patients with HCC has not been thoroughly studied. This study aims to discuss the relationship between tiRNA-Gly-GCC-002 and the prognosis of HCC patients and its role in guiding HCC treatment.

Methods

In this study, the differently expressed tRNA-derived fragments were screened out from the tumor tissues and paracancerous tissues. These tRNA-derived fragments were validated in the tissues and serum samples of patients with HCC by quantitative real-time polymerase chain reaction (qRT-PCR). The target genes of the tRNA-derived fragments were predicted with the microRNA target prediction database (miRDB), which was proceeded with gene set enrichment analysis (GSEA). After that, we analyzed the prognostic effect of the tRNA-derived fragment in relapse-free survival (RFS). Based on univariate and multivariate Cox regression analysis, independent prognostic factors for RFS were obtained. In addition, a column chart was constructed based on clinical pathological features and tiRNAGly-GCC-002.

Results

The tiRNA-Gly-GCC-002 was ultimately served as the candidate gene. Function analysis indicated that tiRNA-Gly-GCC-002 was primarily involved in adenyl nucleotide binding, cell cycle, cell cycle process and chromosome organization. We found that patients with high expression level of tiRNA-Gly-GCC-002 had worse prognosis than low expression level. The univariable and multivariable Cox regression analyses showed that tiRNAGly-GCC-002 was an important prognostic factor. Furthermore, the nomogram by combining tiRNA-Gly-GCC-002 expression level (P=0.03) and serum gamma-glutamyl transferase (GGT) level (P=0.001) was established to predict the prognosis of patients with HCC [concordance index (C-index): 0.789].

Conclusions

In summary, the tiRNA-Gly-GCC-002 can predict the outcome of patients with HCC, which may play a vital role in directing the treatment of HCC.

Prognostic value of blood-based protein biomarkers in non-small cell lung cancer: A critical review and 2008-2022 update

BACKGROUND

Therapeutic possibilities for non-small cell lung cancer (NSCLC) have considerably increased during recent decades.

OBJECTIVE

To summarize the prognostic relevance of serum tumor markers (STM) for early and late-stage NSCLC patients treated with classical chemotherapies, novel targeted and immune therapies.

METHODS

A PubMed database search was conducted for prognostic studies on carcinoembryonic antigen (CEA), cytokeratin-19 fragment (CYFRA 21-1), neuron-specific enolase, squamous-cell carcinoma antigen, progastrin-releasing-peptide, CA125, CA 19-9 and CA 15-3 STMs in NSCLC patients published from 2008 until June 2022.

RESULTS

Out of 1069 studies, 141 were identified as meeting the inclusion criteria. A considerable heterogeneity regarding design, patient number, analytical and statistical methods was observed. High pretherapeutic CYFRA 21-1 levels and insufficient decreases indicated unfavorable prognosis in many studies on NSCLC patients treated with chemo-, targeted and immunotherapies or their combinations in early and advanced stages. Similar results were seen for CEA in chemotherapy, however, high pretherapeutic levels were sometimes favorable in targeted therapies. CA125 is a promising prognostic marker in patients treated with immunotherapies. Combinations of STMs further increased the prognostic value over single markers.

CONCLUSION

Protein STMs, especially CYFRA 21-1, have prognostic potential in early and advanced stage NSCLC. For future STM investigations, better adherence to comparable study designs, analytical methods, outcome measures and statistical evaluation standards is recommended.

Interplay between signal transducers and activators of transcription (STAT) proteins and cancer: involvement, therapeutic and prognostic perspective

Signal transducers and activators of transcription or STAT are proteins that consist of various transcription factors that are responsible for activating genes regarding cell proliferation, differentiation, and apoptosis. They commonly activate several cytokine, growth, or hormone factors via the JAK-STAT signaling pathway by tyrosine phosphorylation which are responsible for giving rise to numerous immune responses. Mutations within the Janus-Kinases (JAKs) or the STATs can set off the commencement of various malfunctions of the immune system of the body; carcinogenesis being an inevitable outcome. STATs are known to act as both oncogenes and tumor suppressor genes which makes it a hot topic of investigation. Various STATs related mechanisms are currently being investigated to analyze its potential of serving as a therapeutic base for numerous immune diseases and cancer; a deeper understanding of the molecular mechanisms involved in the signaling pathways can contribute to the same. This review will throw light upon each STAT member in causing cancer malignancies by affecting subsequent signaling pathways and its genetic and epigenetic associations as well as various inhibitors that could be used to target these pathways thereby devising new treatment options. The review will also focus upon the therapeutic advances made in cancers that most commonly affect people and discuss how STAT genes are identified as prognostic markers.

Puf-A promotes cancer progression by interacting with nucleophosmin in nucleolus

Previously, we identified Puf-A as a novel member of Puf-family RNA-binding proteins; however, its biological functions remain obscure. Analysis of tumor samples of non-small cell lung cancer (NSCLC) showed that high Puf-A expression correlated with high histology grade and abnormal p53 status. Kaplan-Meier curve for overall survival revealed high expression of Puf-A to predict poor prognosis in stage I NSCLC. Among patients with colorectal cancer, high Puf-A expression also showed an adverse impact on overall survival. In lung cancer cell lines, downregulation of p53 increased Puf-A expression, and upregulation of p53 dampened its expression. However, luciferase reporter assays indicated that PUF-A locus harbored the p53-response element, but regulated Puf-A transcription indirectly. In vivo suppression of p53 in CCSP-rtTA/TetO-Cre/LSL-KrasG12D/p53flox/flox conditional mutant mice accelerated the progression of the KrasG12D-driven lung cancer, along with enhanced expression of Puf-A. Importantly, intranasal delivery of shPuf-A to the inducible KrasG12D/p53flox/flox mice suppressed tumor progression. Puf-A silencing led to marked decreases in the 80S ribosomes, along with decrease in S6 and L5 in the cytoplasm and accumulation in the nucleolus. Based on immunofluorescence staining and immunoprecipitation studies, Puf-A interacted with NPM1 in nucleolus. Puf-A silencing resulted in NPM1 translocation from nucleolus to nucleoplasm and this disruption of NPM1 localization was reversed by a rescue experiment. Mechanistically, Puf-A silencing altered NPM1 localization, leading to the retention of ribosomal proteins in nucleolus and diminished ribosome biogenesis, followed by cell-cycle arrest/cell death. Puf-A is a potential theranostic target for cancer therapy and an important player in cancer progression.

Diagnostic, Predictive, and Prognostic Biomarkers in Non-Small Cell Lung Cancer (NSCLC) Management

Lung cancer is the leading cause of cancer-related deaths worldwide. Despite growing efforts for its early detection by screening populations at risk, the majority of lung cancer patients are still diagnosed in an advanced stage. The management of lung cancer has dramatically improved in the last decade and is no longer based on the "one-fits-all" paradigm or the general histological classification of non-small cell versus small cell lung cancer. Emerging options of targeted therapies and immunotherapies have shifted the management of lung cancer to a more personalized treatment approach, significantly influencing the clinical course and outcome of the disease. Molecular biomarkers have emerged as valuable tools in the prognosis and prediction of therapy response. In this review, we discuss the relevant biomarkers used in the clinical management of lung tumors, from diagnosis to prognosis. We also discuss promising new biomarkers, focusing on non-small cell lung cancer as the most abundant type of lung cancer.

A tumor microenvironment-related mRNA-ncRNA signature for prediction early relapse and chemotherapeutic sensitivity in early-stage lung adenocarcinoma

OBJECTIVES

Postoperative early relapse of early-stage lung adenocarcinoma is implicated in poor prognosis. The purpose of our study was to develop an integrated mRNA and non-coding RNA (ncRNA) signature to identify patients at high risk of early relapse in stage I-II lung adenocarcinoma who underwent complete resection.

METHODS

Early-stage lung adenocarcinoma data from Gene Expression Omnibus database were divided into training set and testing set. Propensity score matching analysis was performed between patients in early relapse group and long-term nonrelapse group from training set. Transcriptome analysis, random survival forest and LASSO Cox regression model were used to build an early relapse-related multigene signature. The robustness of the signature was evaluated in testing set and RNA-Seq dataset from The Cancer Genome Atlas (TCGA). The chemotherapy sensitivity, tumor microenvironment and mutation landscape related to the signature were explored using bioinformatics analysis.

RESULTS

Twelve mRNAs and one ncRNA were selected. The multigene signature achieved a strong power for early relapse prediction in training set (HR 3.19, 95% CI 2.16-4.72, P < 0.001) and testing set (HR 2.91, 95% CI 1.63-5.20, P = 0.002). Decision curve analyses revealed that the signature had a good clinical usefulness. Groups divided by the signature exhibited different chemotherapy sensitivity, tumor microenvironment characteristics and mutation landscapes.

CONCLUSIONS

Our results indicated that the integrated mRNA-ncRNA signature may be an innovative biomarker to predict early relapse of early-stage lung adenocarcinoma, and may provide more effective treatment strategies.

A seven-gene prognostic signature predicts overall survival of patients with lung adenocarcinoma (LUAD)

Background

Lung adenocarcinoma (LUAD) is one of the most common types in the world with a high mortality rate. Despite advances in treatment strategies, the overall survival (OS) remains short. Our study aims to establish a reliable prognostic signature closely related to the survival of LUAD patients that can better predict prognosis and possibly help with individual monitoring of LUAD patients.

Methods

Raw RNA-sequencing data were obtained from Fudan University and used as a training group. Differentially expressed genes (DEGs) for the training group were screened. The univariate, least absolute shrinkage and selection operator (LASSO), and multivariate cox regression analysis were conducted to identify the candidate prognostic genes and construct the risk score model. Kaplan–Meier analysis, time-dependent receiver operating characteristic (ROC) curve were used to evaluate the prognostic power and performance of the signature. Moreover, The Cancer Genome Atlas (TCGA-LUAD) dataset was further used to validate the predictive ability of prognostic signature.

Results

A prognostic signature consisting of seven prognostic-related genes was constructed using the training group. The 7-gene prognostic signature significantly grouped patients in high and low-risk groups in terms of overall survival in the training cohort [hazard ratio, HR = 8.94, 95% confidence interval (95% CI)] [2.041–39.2]; P = 0.0004), and in the validation cohort (HR = 2.41, 95% CI [1.779–3.276]; P < 0.0001). Cox regression analysis (univariate and multivariate) demonstrated that the seven-gene signature is an independent prognostic biomarker for predicting the survival of LUAD patients. ROC curves revealed that the 7-gene prognostic signature achieved a good performance in training and validation groups (AUC = 0.91, AUC = 0.7 respectively) in predicting OS for LUAD patients. Furthermore, the stratified analysis of the signature showed another classification to predict the prognosis.

Conclusion

Our study suggested a new and reliable prognostic signature that has a significant implication in predicting overall survival for LUAD patients and may help with early diagnosis and making effective clinical decisions regarding potential individual treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-021-01975-z.

A novel 12-gene signature as independent prognostic model in stage IA and IB lung squamous cell carcinoma patients

BACKGROUND

There is currently no formal consensus on the administration of adjuvant chemotherapy to stage I lung squamous cell carcinoma (LUSC) patients despite the poor prognosis. The side effects of adjuvant chemotherapy need to be balanced against the risk of tumour recurrence. Prognostic markers are thus needed to identify those at higher risks and recommend individualised treatment regimens.

METHODS

Clinical and sequencing data of stage I patients were retrieved from the Lung Squamous Cell Carcinoma project of the Cancer Genome Atlas (TCGA) and three tissue microarray datasets. In a novel K-resample gene selection algorithm, gene-wise Cox proportional hazard regressions were repeated for 50 iterations with random resamples from the TCGA training dataset. The top 200 genes with the best predictive power for survival were chosen to undergo an L1-penalised Cox regression for further gene selection.

RESULTS

A total of 602 samples of LUSC were included, of which 42.2% came from female patients, 45.3% were stage IA cancer. From an initial pool of 11,212 genes in the TCGA training dataset, a final set of 12 genes were selected to construct the multivariate Cox prognostic model. Among the 12 selected genes, 5 genes, STAU1, ADGRF1, ATF7IP2, MALL and KRT23, were adverse prognostic factors for patients, while seven genes, NDUFB1, CNPY2, ZNF394, PIN4, FZD8, NBPF26 and EPYC, were positive prognostic factors. An equation for risk score was thus constructed from the final multivariate Cox model. The model performance was tested in the sequestered TCGA testing dataset and validated in external tissue microarray datasets (GSE4573, GSE31210 and GSE50081), demonstrating its efficacy in stratifying patients into high- and low-risk groups with significant survival difference both in the whole set (including stage IA and IB) and in the stage IA only subgroup of each set. The prognostic power remains significant after adjusting for standard clinical factors. When benchmarked against other prominent gene-signature based prognostic models, the model outperformed the rest in the TCGA testing dataset and in predicting long-term risk at eight years in all three validation datasets.

CONCLUSION

The 12-gene prognostic model may serve as a useful complementary clinical risk-stratification tool for stage I and especially stage IA lung squamous cell carcinoma patients to guide clinical decision making.

Defining COMMD4 as an anti-cancer therapeutic target and prognostic factor in non-small cell lung cancer

Background

Non-small cell lung cancers (NSCLC) account for 85–90% of all lung cancers. As drug resistance critically impairs chemotherapy effectiveness, there is great need to identify new therapeutic targets. The aims of this study were to investigate the prognostic and therapeutic potential of the copper-metabolism-domain-protein, COMMD4, in NSCLC.

Methods

The expression of COMMD4 in NSCLC was investigated using bioinformatic analysis, immunoblotting of immortalised human bronchial epithelial (HBEC) and NSCLC cell lines, qRT-PCR and immunohistochemistry of tissue microarrays. COMMD4 function was additionally investigated in HBEC and NSCLC cells depleted of COMMD4, using small interfering RNA sequences.

Results

Bioinformatic analysis and in vitro analysis of COMMD4 transcripts showed that COMMD4 levels were upregulated in NSCLC and elevated COMMD4 was associated with poor prognosis in adenocarcinoma (ADC). Immunoblotting demonstrated that COMMD4 expression was upregulated in NSCLC cells and siRNA-depletion of COMMD4, decreased cell proliferation and reduced cell viability. Cell death was further enhanced after exposure to DNA damaging agents. COMMD4 depletion caused NSCLC cells to undergo mitotic catastrophe and apoptosis.

Conclusions

Our data indicate that COMMD4 may function as a prognostic factor in ADC NSCLC. Additionally, COMMD4 is a potential therapeutic target for NSCLC, as its depletion induces cancer cell death.

A 5-microRNA signature identified from serum microRNA profiling predicts survival in patients with advanced stage non-small cell lung cancer

Circulating microRNAs (miRNAs) are potential biomarkers for cancer diagnosis, screening and prognosis. This study aimed to identify serum miRNAs as predictors of survival in patients with advanced non-small cell lung cancer (NSCLC). We profiled serum miRNAs in a pilot set of four patients with good survival (>24 months) and four patients with poor survival (<6 months). We selected 140 stably detectable miRNAs and 42 miRNAs reported in literature for further analysis. Expression of these 182 miRNAs was measured using high-throughput polymerase chain reaction assay, and their association with 3-year survival in the discovery (n = 345) and validation (n = 177) cohorts was assessed. Five serum miRNAs (miR-191, miR-28-3p, miR-145, miR-328 and miR-18a) were significantly associated with 3-year overall survival in both cohorts. A combined 5-miRNA risk score was created to assess the cumulative impact of these miRNAs on risk of death. Quartile analysis of the risk score showed significant association with 3-year death risk, with a 4.6-, 6.8- and 9.3-month reduction in median survival time for the second, third and fourth quartiles, respectively. Survival tree analysis also identified distinct risk groups with different 3-year survival durations. Data from The Cancer Genome Atlas revealed all five miRNAs were differentially expressed (P < 0.0001) in paired tumor and normal tissues. Pathway analysis indicated that target genes of these five miRNAs were mainly enriched in inflammatory/immune response pathways and pathways implicated in resistance to chemoradiotherapy and/or targeted therapy. Our results suggested that the 5-miRNA signature could serve as a prognostic predictor in patients with advanced NSCLC.

Identification of a Sixteen-gene Prognostic Biomarker for Lung Adenocarcinoma Using a Machine Learning Method

Objectives: Lung adenocarcinoma (LUAD) accounts for a majority of cancer-related deaths worldwide annually. The identification of prognostic biomarkers and prediction of prognosis for LUAD patients is necessary. Materials and Methods: In this study, LUAD RNA-Seq data and clinical data from the Cancer Genome Atlas (TCGA) were divided into TCGA cohort I (n = 338) and II (n = 168). The cohort I was used for model construction, and the cohort II and data from Gene Expression Omnibus (GSE72094 cohort, n = 393; GSE11969 cohort, n = 149) were utilized for validation. First, the survival-related seed genes were selected from the cohort I using the machine learning model (random survival forest, RSF), and then in order to improve prediction accuracy, the forward selection model was utilized to identify the prognosis-related key genes among the seed genes using the clinically-integrated RNA-Seq data. Second, the survival risk score system was constructed by using these key genes in the cohort II, the GSE72094 cohort and the GSE11969 cohort, and the evaluation metrics such as HR, p value and C-index were calculated to validate the proposed method. Third, the developed approach was compared with the previous five prediction models. Finally, bioinformatics analyses (pathway, heatmap, protein-gene interaction network) have been applied to the identified seed genes and key genes. Results and Conclusion: Based on the RSF model and clinically-integrated RNA-Seq data, we identified sixteen key genes that formed the prognostic gene expression signature. These sixteen key genes could achieve a strong power for prognostic prediction of LUAD patients in cohort II (HR = 3.80, p = 1.63e-06, C-index = 0.656), and were further validated in the GSE72094 cohort (HR = 4.12, p = 1.34e-10, C-index = 0.672) and GSE11969 cohort (HR = 3.87, p = 6.81e-07, C-index = 0.670). The experimental results of three independent validation cohorts showed that compared with the traditional Cox model and the use of standalone RNA-Seq data, the machine-learning-based method effectively improved the prediction accuracy of LUAD prognosis, and the derived model was also superior to the other five existing prediction models. KEGG pathway analysis found eleven of the sixteen genes were associated with Nicotine addiction. Thirteen of the sixteen genes were reported for the first time as the LUAD prognosis-related key genes. In conclusion, we developed a sixteen-gene prognostic marker for LUAD, which may provide a powerful prognostic tool for precision oncology.

Weighted gene expression profiles identify diagnostic and prognostic genes for lung adenocarcinoma and squamous cell carcinoma

OBJECTIVE

To construct a diagnostic signature to distinguish lung adenocarcinoma from lung squamous cell carcinoma and a prognostic signature to predict the risk of death for patients with nonsmall-cell lung cancer, with satisfactory predictive performances, good stabilities, small sizes and meaningful biological implications.

METHODS

Pathway-based feature selection methods utilize pathway information as a priori to provide insightful clues on potential biomarkers from the biological perspective, and such incorporation may be realized by adding weights to test statistics or gene expression values. In this study, weighted gene expression profiles were generated using the GeneRank method and then the LASSO method was used to identify discriminative and prognostic genes.

RESULTS

The five-gene diagnostic signature including keratin 5 (KRT5), mucin 1 (MUC1), triggering receptor expressed on myeloid cells 1 (TREM1), complement C3 (C3) and transmembrane serine protease 2 (TMPRSS2) achieved a predictive error of 12.8% and a Generalized Brier Score of 0.108, while the five-gene prognostic signature including alcohol dehydrogenase 1C (class I), gamma polypeptide (ADH1C), alpha-2-glycoprotein 1, zinc-binding (AZGP1), clusterin (CLU), cyclin dependent kinase 1 (CDK1) and paternally expressed 10 (PEG10) obtained a log-rank P-value of 0.03 and a C-index of 0.622 on the test set.

CONCLUSIONS

Besides good predictive capacity, model parsimony and stability, the identified diagnostic and prognostic genes were highly relevant to lung cancer. A large-sized prospective study to explore the utilization of these genes in a clinical setting is warranted.

Somatic Alteration Burden Involving Non-Cancer Genes Predicts Prognosis in Early-Stage Non-Small Cell Lung Cancer

The burden of somatic mutations and neoantigens has been associated with improved survival in cancer treated with immunotherapies, especially non-small cell lung cancer (NSCLC). However, there is uncertainty about their effect on outcome in early-stage untreated cases. We posited that the burden of mutations in a specific set of genes may also contribute to the prognosis of early NSCLC patients. From a small cohort of 36 NSCLC cases, we were able to identify somatic mutations and copy number alterations in 865 genes that contributed to patient overall survival. Simply, the number of altered genes (NAG) among these 865 genes was associated with longer disease-free survival (hazard ratio (HR) = 0.153, p = 1.48 × 10^-4). The gene expression signature distinguishing patients with high/low NAG was also prognostic in three independent datasets. Patients with a high NAG could be further stratified based on the presence of immunogenic mutations, revealing a further subgroup of stage I NSCLC with even better prognosis (85% with >5 years survival), and associated with cytotoxic T-cell expression. Importantly, 95% of the highly-altered genes lacked direct relation to cancer, but were implicated in pathways regulating cell proliferation, motility and immune response.

A robust six-gene prognostic signature for prediction of both disease-free and overall survival in non-small cell lung cancer

Background

The high mortality of patients with non-small cell lung cancer (NSCLC) emphasizes the necessity of identifying a robust and reliable prognostic signature for NSCLC patients. This study aimed to identify and validate a prognostic signature for the prediction of both disease-free survival (DFS) and overall survival (OS) of NSCLC patients by integrating multiple datasets.

Methods

We firstly downloaded three independent datasets under the accessing number of GSE31210, GSE37745 and GSE50081, and then performed an univariate regression analysis to identify the candidate prognostic genes from each dataset, and identified the gene signature by overlapping the candidates. Then, we built a prognostic model to predict DFS and OS using a risk score method. Kaplan–Meier curve with log-rank test was used to determine the prognostic significance. Univariate and multivariate Cox proportional hazard regression models were implemented to evaluate the influences of various variables on DFS and OS. The robustness of the prognostic gene signature was evaluated by re-sampling tests based on the combined GEO dataset (GSE31210, GSE37745 and GSE50081). Furthermore, a The Cancer Genome Atlas (TCGA)-NSCLC cohort was utilized to validate the prediction power of the gene signature. Finally, the correlation of the risk score of the gene signature and the Gene set variation analysis (GSVA) score of cancer hallmark gene sets was investigated.

Results

We identified and validated a six-gene prognostic signature in this study. This prognostic signature stratified NSCLC patients into the low-risk and high-risk groups. Multivariate regression and stratification analyses demonstrated that the six-gene signature was an independent predictive factor for both DFS and OS when adjusting for other clinical factors. Re-sampling analysis implicated that this six-gene signature for predicting prognosis of NSCLC patients is robust. Moreover, the risk score of the gene signature is correlated with the GSVA score of 7 cancer hallmark gene sets.

Conclusion

This study provided a robust and reliable gene signature that had significant implications in the prediction of both DFS and OS of NSCLC patients, and may provide more effective treatment strategies and personalized therapies.

Electronic supplementary material

The online version of this article (10.1186/s12967-019-1899-y) contains supplementary material, which is available to authorized users.

GradientScanSurv-An exhaustive association test method for gene expression data with censored survival outcome

Accurate assessment of the association between continuous variables such as gene expression and survival is a critical aspect of precision medicine. In this report, we provide a review of some of the available survival analysis and validation tools by referencing published studies that have utilized these tools. We have identified pitfalls associated with the assumptions inherent in those applications that have the potential to impact scientific research through their potential bias. In order to overcome these pitfalls, we have developed a novel method that enables the logrank test method to handle continuous variables that comprehensively evaluates survival association with derived aggregate statistics. This is accomplished by exhaustively considering all the cutpoints across the full expression gradient. Direct side-by-side comparisons, global ROC analysis, and evaluation of the ability to capture relevant biological themes based on current understanding of RAS biology all demonstrated that the new method shows better consistency between multiple datasets of the same disease, better reproducibility and robustness, and better detection power to uncover biological relevance within the selected datasets over the available survival analysis methods on univariate gene expression and penalized linear model-based methods.

Consensus of gene expression phenotypes and prognostic risk predictors in primary lung adenocarcinoma

Transcriptional profiling of lung adenocarcinomas has identified numerous gene expression phenotype (GEP) and risk prediction (RP) signatures associated with patient outcome. However, classification agreement between signatures, underlying transcriptional programs, and independent signature validation are less studied. We classified 2395 transcriptional adenocarcinoma profiles, assembled from 17 public cohorts, using 11 GEP and seven RP signatures, finding that 16 signatures were associated with patient survival in the total cohort and in multiple individual cohorts. For significant signatures, total cohort hazard ratios were ~2 in univariate analyses (mean=1.95, range=1.4-2.6). Strong classification agreement between signatures was observed, especially for predicted low-risk patients by adenocarcinoma-derived signatures. Expression of proliferation-related genes correlated strongly with GEP subtype classifications and RP scores, driving the gene signature association with prognosis. A three-group consensus definition of samples across 10 GEP classifiers demonstrated aggregation of samples with specific smoking patterns, gender, and EGFR/KRAS mutations, while survival differences were only significant when patients were divided into low- or high-risk. In summary, our study demonstrates a consensus between GEPs and RPs in lung adenocarcinoma through a common underlying transcriptional program. This consensus generalizes reported problems with current signatures in a clinical context, stressing development of new adenocarcinoma-specific single sample predictors for clinical use.

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.

Prognostic and predictive biomarkers post curative intent therapy

Large-scale screening trials have demonstrated that early diagnosis of lung cancer results in a significant reduction in lung cancer mortality. Despite improvements in detecting more lung cancers at early stages, the 5-year survival rates of lung cancers diagnosed before widespread disease is only 30-50%. High rates of recurrence, despite early diagnosis, suggest the need to improve treatment strategies based on the likelihood of recurrence in patient subsets, as well as explore the role of predictive markers for therapy selection in the adjuvant setting. In the era of personalized medicine, there have been a wide array of molecular alterations and signatures studied for their potential prognostic and predictive utility, however most have failed to translate into clinical tools. This review will discuss progress made in clinical management of lung cancer, and recent progress in the development of patient selection tools for the refinement of early stage lung cancers.

A potential prognostic biomarker SPC24 promotes tumorigenesis and metastasis in lung cancer

RESULTS

SPC24 is over-expressed in clinical lung adenocarcinoma samples, and high level of SPC24 is associated with advanced stages of lung tumors. Knocking down SPC24 repressed cell growth and promoted apoptosis. SPC24 deficiency reduced cancer cell migration as well. E-cadherin, one of the epithelial-mesenchymal transition markers, was up-regulated in the knockdown cells, along with down-regulation of N-cadherin and Vimentin. Oncomine expression analyses further confirmed that high level of SPC24 is associated with tumors from smokers, recurrent patients, or patients with shorter survivals.

PURPOSE AND METHODS

To reveal the role of SPC24, an important component of kinetochore, in the tumorigenesis of lung cancer, we performed Oncomine and immunohistochemistry (IHC) analyses for SPC24 in human lung adenocarcinoma tumors. We knocked down SPC24 in two non-small cell lung cancer (NSCLC) cell lines, PC9 and A549, by siRNA and evaluated cell proliferation, apoptosis, and migration in the SPC24-deficient cells. Using a mouse xenograft model, we compared in vivo tumor growth of the knockdown and control cells. We further performed multiple Oncomine expression analyses for SPC24 in various lung cancer datasets with important clinical characteristics and risk factors, including survival, recurrence, and smoking status.

CONCLUSIONS

SPC24 is a novel oncogene of lung cancer, and can serve as a promising prognostic biomarker to differentiate lung tumors that have various clinicopathological characteristics. The findings of the current study will benefit the diagnosis, management, and targeted therapy of lung cancer.

Towards Prognostic Profiling of Non-Small Cell Lung Cancer: New Perspectives on the Relevance of Polo-Like Kinase 1 Expression, the TP53 Mutation Status and Hypoxia

Background: Currently, prognosis of non-small cell lung cancer (NSCLC) patients is based on clinicopathological factors, including TNM stage. However, there are considerable differences in patient outcome within a similar staging group, even when patients received identical treatments. In order to improve prognostic predictions and to guide treatment options, additional parameters influencing outcome are required. Polo-like kinase 1 (Plk1), a master regulator of mitotic cell division and the DNA damage response, is considered as a new potential biomarker in this research area. While several studies reported Plk1 overexpression in a broad range of human malignancies, inconsistent results were published regarding the clinical significance hereof. A prognostic panel, consisting of Plk1 and additional biomarkers that are related to the Plk1 pathway, might further improve prediction of patient prognosis. Methods: In this study, we evaluated for the first time the prognostic value of Plk1 mRNA and protein expression in combination with the TP53 mutation status (next generation sequencing), induction of apoptotic cell death (immunohistochemistry for cleaved caspase 3) and hypoxia (immunohistochemistry for carbonic anhydrase IX (CA IX)) in 98 NSCLC adenocarcinoma patients. Results: Both Plk1 mRNA and protein expression and CA IX protein levels were upregulated in the majority of tumor samples. Plk1 mRNA and protein expression levels were higher in TP53 mutant samples, suggesting that Plk1 overexpression is, at least partially, the result of loss of functional p53 (<0.05). Interestingly, the outcome of patients with both Plk1 mRNA and CA IX protein overexpression, who also harbored a TP53 mutation, was much worse than that of patients with aberrant expression of only one of the three markers (p=0.001). Conclusion: The combined evaluation of Plk1 mRNA expression, CA IX protein expression and TP53 mutations shows promise as a prognostic panel in NSCLC patients. Moreover, these results pave the way for new combination strategies with Plk1 inhibitors.

Development of a RNA-Seq Based Prognostic Signature in Lung Adenocarcinoma

Background

Precision therapy for lung cancer will require comprehensive genomic testing to identify actionable targets as well as ascertain disease prognosis. RNA-seq is a robust platform that meets these requirements, but microarray-derived prognostic signatures are not optimal for RNA-seq data. Thus, we undertook the first prognostic analysis of lung adenocarcinoma RNA-seq data and generated a prognostic signature.

Methods

Lung adenocarcinoma RNA-seq and clinical data from The Cancer Genome Atlas (TCGA) were divided chronologically into training (n = 255) and validation (n = 157) cohorts. In the training cohort, prognostic association was assessed by univariate Cox analysis. A prognostic signature was built with stepwise multivariable Cox analysis. Outcomes by risk group, stage, and mutation status were analyzed with Kaplan-Meier and multivariable Cox analyses. All the statistical tests were two-sided.

Results

In the training cohort, 96 genes had prognostic association with P values of less than or equal to 1.00x10^-4, including five long noncoding RNAs (lncRNAs). Stepwise regression generated a four-gene signature, including one lncRNA. Signature high-risk cases had worse overall survival (OS) in the TCGA validation cohort (hazard ratio [HR] = 3.07, 95% confidence interval [CI] = 2.00 to 14.62) and a University of Michigan institutional cohort (n = 67; HR = 2.05, 95% CI = 1.18 to 4.55), and worse metastasis-free survival in the TCGA validation cohort (HR = 3.05, 95% CI = 2.31 to 13.37). The four-gene prognostic signature also statistically significantly stratified overall survival in important clinical subsets, including stage I (HR = 2.78, 95% CI = 1.91 to 11.13), EGFR wild-type (HR = 3.01, 95% CI = 1.73 to 14.98), and EGFR mutant (HR = 8.99, 95% CI = 62.23 to 141.44). The four-gene prognostic signature also stood out on top when compared with other prognostic signatures.

Conclusions

Here, we present the first RNA-seq prognostic signature for lung adenocarcinoma that can provide a powerful prognostic tool for precision oncology as part of an integrated RNA-seq clinical sequencing program.

Down-regulation of SerpinB2 is associated with gefitinib resistance in non-small cell lung cancer and enhances invadopodia-like structure protrusions

The failure of targeted therapy due to the resistance to epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs), such as gefitinib, is considered a major problem in the treatment of non-small cell lung cancer (NSCLC) patients. SerpinB2, a component of the urokinase plasminogen activator (uPA) system, has been recognized as a biomarker for the progression and metastasis of lung cancer. Nevertheless, the relationship between SerpinB2 and EGFR-TKI resistance has not been elucidated. Here, we report that SerpinB2 is down-regulated in gefitinib-resistant (H292-Gef) cells compared to gefitinib-sensitive (H292) cells. The low SerpinB2 levels in H292-Gef cells were also associated with an enhancement in invasiveness and increase in the length of invadopodia-like structures in the cells. The effect on invasiveness and gefitinib sensitivity was confirmed by knockdown and overexpression of SerpinB2. In addition, the possibility to overcome the resistance through the up-regulation of SerpinB2 was supported by employing an antitumor agent yuanhuadine (YD). Treatment with YD effectively elevated SerpinB2 levels and suppressed invasive properties in H292-Gef cells. Collectively, these findings demonstrate the prospective role of SerpinB2 as a novel biomarker for acquired gefitinib resistance and a potential target for NSCLC treatment.

Exploring the role of sphingolipid machinery during the epithelial to mesenchymal transition program using an integrative approach

The epithelial to mesenchymal transition (EMT) program is activated in epithelial cancer cells and facilitates their ability to metastasize based on enhanced migratory, proliferative, anti-apoptotic, and pluripotent capacities. Given the fundamental impact of sphingolipid machinery to each individual process, the sphingolipid-related mechanisms might be considered among the most prominent drivers/players of EMT; yet, there is still limited knowledge. Given the complexity of the interconnected sphingolipid system, which includes distinct sphingolipid mediators, their synthesizing enzymes, receptors and transporters, we herein apply an integrative approach for assessment of the sphingolipid-associated mechanisms underlying EMT program. We created the sphingolipid-/EMT-relevant 41-gene/23-gene signatures which were applied to denote transcriptional events in a lung cancer cell-based EMT model. Based on defined 35-gene sphingolipid/EMT-attributed signature of regulated genes, we show close associations between EMT markers, genes comprising the sphingolipid network at multiple levels and encoding sphingosine 1-phosphate (S1P)-/ceramide-metabolizing enzymes, S1P and lysophosphatidic acid (LPA) receptors and S1P transporters, pluripotency genes and inflammation-related molecules, and demonstrate the underlying biological pathways and regulators. Mass spectrometry-based sphingolipid analysis revealed an EMT-attributed shift towards increased S1P and LPA accompanied by reduced ceramide levels. Notably, using transcriptomics data across various cell-based perturbations and neoplastic tissues (24193 arrays), we identified the sphingolipid/EMT signature primarily in lung adenocarcinoma tissues; besides, bladder, colorectal and prostate cancers were among the top-ranked. The findings also highlight novel regulatory associations between influenza virus and the sphingolipid/EMT-associated mechanisms. In sum, data propose the multidimensional contribution of sphingolipid machinery to pathological EMT and may yield new biomarkers and therapeutic targets.

[The Current Immunohistochemistry Markers in the Resected Tissues of Non-small Cell Lung Cancer Could Not Predict Prognosis]

背景与目的

在非小细胞肺癌（non-small cell lung cancer, NSCLC）切除组织中寻找可预测远期生存的免疫组化指标一直备受关注。本研究旨在回顾评价单中心历史上曾选用的免疫组化指标与NSCLC预后之间的关系。

方法

2008年-2013年我院单一手术组切除NSCLC 722例，选用的免疫组化指标共12个，在随访良好的前瞻性数据基础上，行单因素生存分析及多因素风险回归模型评价这些指标的表达在NSCLC切除生存中的意义。

结果

曾选用的12个免疫组化分子分别为：血小板衍生生长因子受体（platelet-derived growth factor receptor, PDGFR）（n=460）、切除修复交叉互补1（excision repair cross complementing 1, ERCC1）（n=461）、表皮生长因子受体（epithelial growth factor receptor, EGFR）（n=460）、人血管内皮生长因子受体3（vascular endothelial growth factor receptor 3, VEGFR3）（n=451）、NM23（n=359）、MRP（n=351）、P170（n=353）、TS（n=431）、Tubulin（n=307）、核糖核苷酸还原酶M1（ribonucleotide reductase M1, RRM1）（n=381）、环氧酶2（cyclooxygenase 2, COX2）（n=364）和TOPII（n=235）。单因素分析显示仅有VEGFR3的表达与生存有关，阳性表达者与阴性表达者的5年生存率分别为77.6%与65.0%（P=0.042）。但多因素分析表明VEGFR3不是NSCLC独立的预后因素。

结论

本组所选用的免疫组织化学指标不能预测切除后的NSCLC患者的生存。

The Contrasting Role of p16Ink4A Patterns of Expression in Neuroendocrine and Non-Neuroendocrine Lung Tumors: A Comprehensive Analysis with Clinicopathologic and Molecular Correlations

Lung cancer encompasses a constellation of malignancies with no validated prognostic markers. p16Ink4A expression has been reported in different subtypes of lung cancers; however, its prognostic value is controversial. Here, we sought to investigate the clinical significance of p16Ink4A immunoexpression according to specific staining patterns and its operational implications. A total of 502 tumors, including 277 adenocarcinomas, 84 squamous cell carcinomas, 22 large cell carcinomas, 47 typical carcinoids, 12 atypical carcinoids, 28 large cell neuroendocrine carcinomas, and 32 small cell carcinomas were reviewed and subjected to immunohistochemical analysis for p16Ink4A and Ki67. The spectrum of p16Ink4A expression was annotated for each case as negative, sporadic, focal, or diffuse. Expression at immunohistochemical level showed intra-tumor homogeneity, regardless tumor histotype. Enrichments in cells expressing p16Ink4A were observed from lower- to higher-grade neuroendocrine malignancies, whereas a decrease was seen in poorly and undifferentiated non-neuroendocrine carcinomas. Tumor proliferation indices were higher in neuroendocrine tumors expressing p16Ink4A while non-neuroendocrine malignancies immunoreactive for p16Ink4A showed a decrease in Ki67-positive cells. Quantitative statistical analyses including each histotype and the p16Ink4A status confirmed the independent prognostic role of p16Ink4A expression, being a high-risk indicator in neuroendocrine tumors and a marker of good prognosis in non-neuroendocrine lung malignancies. In this study, we provide circumstantial evidence to suggest that the routinary assessment of p16Ink4A expression using a three-tiered scoring algorithm, even in a small biopsy, may constitute a reliable, reproducible, and cost-effective substrate for a more accurate risk stratification of each individual patient.

Current and future targeted therapies for non-small-cell lung cancers with aberrant EGF receptors

Expression of the EGF receptors (EGFRs) is abnormally high in many types of cancer, including 25% of lung cancers. Successful treatments target mutations in the EGFR tyrosine kinase domain with EGFR tyrosine kinase inhibitors (TKIs). However, almost all patients develop resistance to this treatment, and acquired resistance to first-generation TKI has prompted the clinical development of a second generation of EGFR TKI. Because of the development of resistance to treatment of TKIs, there is a need to collect genomic information about EGFR levels in non-small-cell lung cancer patients. Herein, we focus on current molecular targets that have therapies available as well as other targets for which therapies will be available in the near future.

Predicting Prognosis of Early-Stage Non-Small Cell Lung Cancer Using the GeneFx® Lung Signature

Use of adjuvant chemotherapy remains a complex decision in the treatment of early stage non-small cell lung cancer (NSCLC), with risk of recurrence being the primary indicator (i.e. adjuvant chemotherapy is considered for patients at high risk of recurrence but may not be beneficial for patients at low risk). However, although several clinical and pathological factors are typically considered when assessing the risk of recurrence, none are significantly associated with clinical outcome with the exception of tumor size. GeneFx® Lung (Helomics™ Corporation, Pittsburgh, PA) is a multi-gene RNA expression signature that classifies early stage NSCLC patients as high-risk or low-risk for disease recurrence. GeneFx Lung risk category has been shown to be significantly associated with overall survival in several independent clinical studies. The published literature regarding the analytical validity, clinical validity and clinical utility of GeneFx Lung is summarized herein.