Impact of RNA degradation on gene expression profiling

Background

Cancer is a heterogeneous disease caused by genomic aberrations and characterized by significant variability in clinical outcomes and response to therapies. Several subtypes of common cancers have been identified based on alterations of individual cancer genes, such as HER2, EGFR, and others. However, cancer is a complex disease driven by the interaction of multiple genes, so the copy number status of individual genes is not sufficient to define cancer subtypes and predict responses to treatments. A classification based on genome-wide copy number patterns would be better suited for this purpose.

Method

To develop a more comprehensive cancer taxonomy based on genome-wide patterns of copy number abnormalities, we designed an unsupervised classification algorithm that identifies genomic subgroups of tumors. This algorithm is based on a modified genomic Non-negative Matrix Factorization (gNMF) algorithm and includes several additional components, namely a pilot hierarchical clustering procedure to determine the number of clusters, a multiple random initiation scheme, a new stop criterion for the core gNMF, as well as a 10-fold cross-validation stability test for quality assessment.

Result

We applied our algorithm to identify genomic subgroups of three major cancer types: non-small cell lung carcinoma (NSCLC), colorectal cancer (CRC), and malignant melanoma. High-density SNP array datasets for patient tumors and established cell lines were used to define genomic subclasses of the diseases and identify cell lines representative of each genomic subtype. The algorithm was compared with several traditional clustering methods and showed improved performance. To validate our genomic taxonomy of NSCLC, we correlated the genomic classification with disease outcomes. Overall survival time and time to recurrence were shown to differ significantly between the genomic subtypes.

Conclusions

We developed an algorithm for cancer classification based on genome-wide patterns of copy number aberrations and demonstrated its superiority to existing clustering methods. The algorithm was applied to define genomic subgroups of three cancer types and identify cell lines representative of these subgroups. Our data enabled the assembly of representative cell line panels for testing drug candidates.     

Molecular Subtypes in Head and Neck Cancer Exhibit Distinct Patterns of Chromosomal Gain and Loss of Canonical Cancer Genes

Head and neck squamous cell carcinoma (HNSCC) is a frequently fatal heterogeneous disease. Beyond the role of human papilloma virus (HPV), no validated molecular characterization of the disease has been established. Using an integrated genomic analysis and validation methodology we confirm four molecular classes of HNSCC (basal, mesenchymal, atypical, and classical) consistent with signatures established for squamous carcinoma of the lung, including deregulation of the KEAP1/NFE2L2 oxidative stress pathway, differential utilization of the lineage markers SOX2 and TP63, and preference for the oncogenes PIK3CA and EGFR. For potential clinical use the signatures are complimentary to classification by HPV infection status as well as the putative high risk marker CCND1 copy number gain. A molecular etiology for the subtypes is suggested by statistically significant chromosomal gains and losses and differential cell of origin expression patterns. Model systems representative of each of the four subtypes are also presented.     

Doubly penalized buckley-james method for survival data with high-dimensional covariates.

Recent interest in cancer research focuses on predicting patients' survival by investigating gene expression profiles based on microarray analysis. We propose a doubly penalized Buckley-James method for the semiparametric accelerated failure time model to relate high-dimensional genomic data to censored survival outcomes, which uses the elastic-net penalty that is a mixture of L1- and L2-norm penalties. Similar to the elastic-net method for a linear regression model with uncensored data, the proposed method performs automatic gene selection and parameter estimation, where highly correlated genes are able to be selected (or removed) together. The two-dimensional tuning parameter is determined by generalized crossvalidation. The proposed method is evaluated by simulations and applied to the Michigan squamous cell lung carcinoma study.     

A Pilot Proteogenomic Study with Data Integration Identifies MCT1 and GLUT1 as Prognostic Markers in Lung Adenocarcinoma

We performed a pilot proteogenomic study to compare lung adenocarcinoma to lung squamous cell carcinoma using quantitative proteomics (6-plex TMT) combined with a customized Affymetrix GeneChip. Using MaxQuant software, we identified 51,001 unique peptides that mapped to 7,241 unique proteins and from these identified 6,373 genes with matching protein expression for further analysis. We found a minor correlation between gene expression and protein expression; both datasets were able to independently recapitulate known differences between the adenocarcinoma and squamous cell carcinoma subtypes. We found 565 proteins and 629 genes to be differentially expressed between adenocarcinoma and squamous cell carcinoma, with 113 of these consistently differentially expressed at both the gene and protein levels. We then compared our results to published adenocarcinoma versus squamous cell carcinoma proteomic data that we also processed with MaxQuant. We selected two proteins consistently overexpressed in squamous cell carcinoma in all studies, MCT1 (SLC16A1) and GLUT1 (SLC2A1), for further investigation. We found differential expression of these same proteins at the gene level in our study as well as in other public gene expression datasets. These findings combined with survival analysis of public datasets suggest that MCT1 and GLUT1 may be potential prognostic markers in adenocarcinoma and druggable targets in squamous cell carcinoma. Data are available via ProteomeXchange with identifier PXD002622.     

True 3q Chromosomal Amplification in Squamous Cell Lung Carcinoma by FISH and aCGH Molecular Analysis: Impact on Targeted Drugs

Squamous lung carcinoma lacks specific “ad hoc” therapies. Amplification of chromosome 3q is the most common genomic aberration and this region harbours genes having role as novel targets for therapeutics. There is no standard definition on how to score and report 3q amplification. False versus true 3q chromosomal amplification in squamous cell lung carcinoma may have tremendous impact on trials involving drugs which target DNA zones mapping on 3q. Forty squamous lung carcinomas were analyzed by FISH to assess chromosome 3q amplification. aCGH was performed as gold-standard to avoid false positive amplifications. Three clustered patterns of fluorescent signals were observed. Eight cases out of 40 (20%) showed ≥8 3q signals. Twenty out of 40 (50%) showed from 3 to 7 signals. The remaining showed two fluorescent signals (30%). When corrected by whole chromosome 3 signals, only cases with ≥8 signals maintained a LSI 3q/CEP3 ratio >2. Only the cases showing 3q amplification by aCGH (+3q25.3−3q27.3) showed ≥8 fluorescent signals at FISH evidencing a 3q/3 ratio >2. The remaining cases showed flat genomic portrait at aCGH on chromosome 3. We concluded that: 1) absolute copy number of 3q chromosomal region may harbour false positive interpretation of 3q amplification in squamous cell carcinoma; 2) a case results truly “amplified for chromosome 3q” when showing ≥8 fluorescent 3q signals; 3) trials involving drugs targeting loci on chromosome 3q in squamous lung carcinoma therapy have to consider false versus true 3q chromosomal amplification.     

基于多组学数据的早期宫颈鳞状细胞癌分类

从分子层面对泛癌进行研究已经得到了很大的进展，但是对宫颈鳞状细胞癌的分子分类研究仍然需要更多的探索. 为了找到宫颈鳞状细胞癌潜在的子类，本文提出了一个基于多维组学数据的癌症亚型分类分析流程. 通过统计学方法对癌症基因组图谱（The Cancer Genome Atlas，TCGA）宫颈鳞状细胞癌的mRNA表达数据、小分子核糖核酸（microRNA，miRNA）表达数据、DNA甲基化数据以及拷贝数变异数据4个维度包含的分子进行筛选，然后对筛选后的分类特征进行整合聚类，进一步筛选能够区分不同子类的关键分类特征，并使用这些关键分类特征建立宫颈鳞状细胞癌分类模型. 本研究为宫颈鳞状细胞癌分子层面子类的识别提供了分析流程，得到了两个临床生存水平具有显著性差异的宫颈鳞状细胞癌子类，并确定了8个宫颈鳞状细胞癌的关键分类特征. 本研究中识别的宫颈鳞状细胞癌子类和关键分类特征为宫颈鳞状细胞癌早期分类及分类标志物的鉴定提供了重要参考.     

Doubly Penalized Buckley–James Method for Survival Data with High-Dimensional Covariates

Summary .   Recent interest in cancer research focuses on predicting patients' survival by investigating gene expression profiles based on microarray analysis. We propose a doubly penalized Buckley–James method for the semiparametric accelerated failure time model to relate high-dimensional genomic data to censored survival outcomes, which uses the elastic-net penalty that is a mixture of L ₁- and L ₂-norm penalties. Similar to the elastic-net method for a linear regression model with uncensored data, the proposed method performs automatic gene selection and parameter estimation, where highly correlated genes are able to be selected (or removed) together. The two-dimensional tuning parameter is determined by generalized crossvalidation. The proposed method is evaluated by simulations and applied to the Michigan squamous cell lung carcinoma study.     

The 3-dimensional organization of the nucleoli of benign and malignant tumor cells from different human organs]

The method of ultrathin serial sections was used to perform a comparative ultrastructural and 3-dimensional analysis of nucleoli for the following variants of human tumours: benign (fibroadenoma) and malignant (infiltrating ductal carcinoma) tumours of one organ (mammary gland); malignant tumours of epidermal genesis in different organs (squamous cell carcinomas of skin, larynx, lung, gullet, uterus); two forms of malignant tumours (squamous cell and small cell carcinomas) of one organ (lung). The spatial models of nucleoli in these tumour cells are given. The specific signs in architecture of tumour nucleoli was found. Nucleoli of fibroadenomas have well pronounced 1-4 fibrillar centres forming a united system with a lacunar component and intranucleolar chromatin. Unlike benign tumour cells, nucleoli of infiltrating ductal carcinomas are characterized by large, prominent nucleoli containing giant, multiform fibrillar centres with a complicated surface, a well developed granular component and an unusually organized lacunar system. In squamous cell carcinomas of various localization, active, hypertrophied nucleoli with pseudonucleolonemal organization were found. The small cell carcinoma of lung differs from the squamous cell cancer of the same organ by dense, fibrillar nucleoli with a small amount of granular component located on the periphery of the nucleolar body. Nucleolar type reflecting the functional state of malignization process may serve as an additional diagnostic criterion for tumour identification.     

Array-comparative genomic hybridization reveals loss of SOCS6 is associated with poor prognosis in primary lung squamous cell carcinoma

Background

Primary tumor recurrence commonly occurs after surgical resection of lung squamous cell carcinoma (SCC). Little is known about the genes driving SCC recurrence.

Methods

We used array comparative genomic hybridization (aCGH) to identify genes affected by copy number alterations that may be involved in SCC recurrence. Training and test sets of resected primary lung SCC were assembled. aCGH was used to determine genomic copy number in a training set of 62 primary lung SCCs (28 with recurrence and 34 with no evidence of recurrence) and the altered copy number of candidate genes was confirmed by quantitative PCR (qPCR). An independent test set of 72 primary lung SCCs (20 with recurrence and 52 with no evidence of recurrence) was used for biological validation. mRNA expression of candidate genes was studied using qRT-PCR. Candidate gene promoter methylation was evaluated using methylation microarrays and Sequenom EpiTYPER analysis.

Results

18q22.3 loss was identified by aCGH as being significantly associated with recurrence (p = 0.038). Seven genes within 18q22.3 had aCGH copy number loss associated with recurrence but only SOCS6 copy number was both technically replicated by qPCR and biologically validated in the test set. SOCS6 copy number loss correlated with reduced mRNA expression in the study samples and in the samples with copy number loss, there was a trend for increased methylation, albeit non-significant. Overall survival was significantly poorer in patients with SOCS6 loss compared to patients without SOCS6 loss in both the training (30 vs. 43 months, p = 0.023) and test set (27 vs. 43 months, p = 0.010).

Conclusion

Reduced copy number and mRNA expression of SOCS6 are associated with disease recurrence in primary lung SCC and may be useful prognostic biomarkers.     

A monoclonal antibody (Po66) directed against human lung squamous cell carcinoma immunolocalization of tumour xenografts in nude mice

Summary Po66, a mouse IgG1 monoclonal antibody, was produced by immunization against a patient lung squamous cell carcinoma. The tissue reactivity of the antibody was measured by a radioimmunological assay with enzymatically dissociated cells, by an immunofluorescence test on frozen tissue sections and by peroxidase-staining of paraffin sections. The antibody bound to lung squamous cell carcinoma, oesophagous carcinoma and, inconsistantly to lung adenocarcinoma but not to the other tumours tested. Some normal tissues also reacted positively, in particular bronchial serous glands, oesophagus epithelium and renal distal and collecting tubules. In normal and malignant tissues showing epithelioid differentiation, Po66 bound to the intermediate maturation area. The antigen immunoprecipitated by Po66 from lung squamous cell carcinoma appeared as a single band with a molecular weight 47000 to 50000 daltons. Purified monoclonal antibody Po66 and an unrelated IgG1 immunoglobulin were labelled with radioactive iodine and injected i. v. into nude mice bearing subcutaneous xenografts of human lung squamous cell carcinoma. The localization index in the tumour was 3.3. Antibody labelled with ¹³¹I allowed gamma-scintigraphic imaging of the xenografts which were clearly outlined by days 9 to 11.This work was supported by Association pour la Recherche sur le Cancer     

MicroRNA-218 Is Deleted and Downregulated in Lung Squamous Cell Carcinoma

MicroRNAs (miRNAs) are a family of small, non-coding RNA species functioning as negative regulators of multiple target genes including tumour suppressor genes and oncogenes. Many miRNA gene loci are located within cancer-associated genomic regions. To identify potential new amplified oncogenic and/or deleted tumour suppressing miRNAs in lung cancer, we inferred miRNA gene dosage from high dimensional arrayCGH data. From miRBase v9.0 (http://microrna.sanger.ac.uk), 474 human miRNA genes were physically mapped to regions of chromosomal loss or gain identified from a high-resolution genome-wide arrayCGH study of 132 primary non-small cell lung cancers (NSCLCs) (a training set of 60 squamous cell carcinomas and 72 adenocarcinomas). MiRNAs were selected as candidates if their immediately flanking probes or host gene were deleted or amplified in at least 25% of primary tumours using both Analysis of Copy Errors algorithm and fold change (≥±1.2) analyses. Using these criteria, 97 miRNAs mapped to regions of aberrant copy number. Analysis of three independent published lung cancer arrayCGH datasets confirmed that 22 of these miRNA loci showed directionally concordant copy number variation. MiR-218, encoded on 4p15.31 and 5q35.1 within two host genes (SLIT2 and SLIT3), in a region of copy number loss, was selected as a priority candidate for follow-up as it is reported as underexpressed in lung cancer. We confirmed decreased expression of mature miR-218 and its host genes by qRT-PCR in 39 NSCLCs relative to normal lung tissue. This downregulation of miR-218 was found to be associated with a history of cigarette smoking, but not human papilloma virus. Thus, we show for the first time that putative lung cancer-associated miRNAs can be identified from genome-wide arrayCGH datasets using a bioinformatics mapping approach, and report that miR-218 is a strong candidate tumour suppressing miRNA potentially involved in lung cancer.     

Copy Number Analysis Identifies Novel Interactions Between Genomic Loci in Ovarian Cancer

Ovarian cancer is a heterogeneous disease displaying complex genomic alterations, and consequently, it has been difficult to determine the most relevant copy number alterations with the scale of studies to date. We obtained genome-wide copy number alteration (CNA) data from four different SNP array platforms, with a final data set of 398 ovarian tumours, mostly of the serous histological subtype. Frequent CNA aberrations targeted many thousands of genes. However, high-level amplicons and homozygous deletions enabled filtering of this list to the most relevant. The large data set enabled refinement of minimal regions and identification of rare amplicons such as at 1p34 and 20q11. We performed a novel co-occurrence analysis to assess cooperation and exclusivity of CNAs and analysed their relationship to patient outcome. Positive associations were identified between gains on 19 and 20q, gain of 20q and loss of X, and between several regions of loss, particularly 17q. We found weak correlations of CNA at genomic loci such as 19q12 with clinical outcome. We also assessed genomic instability measures and found a correlation of the number of higher amplitude gains with poorer overall survival. By assembling the largest collection of ovarian copy number data to date, we have been able to identify the most frequent aberrations and their interactions.     

PLASQ: a generalized linear model-based procedure to determine allelic dosage in cancer cells from SNP array data

Human cancer is largely driven by the acquisition of mutations. One class of such mutations is copy number polymorphisms, comprised of deviations from the normal diploid two copies of each autosomal chromosome per cell. We describe a probe-level allele-specific quantitation (PLASQ) procedure to determine copy number contributions from each of the parental chromosomes in cancer cells from single-nucleotide polymorphism (SNP) microarray data. Our approach is based upon a generalized linear model that takes advantage of a novel classification of probes on the array. As a result of this classification, we are able to fit the model to the data using an expectation-maximization algorithm designed for the purpose. We demonstrate a strong model fit to data from a variety of cell types. In normal diploid samples, PLASQ is able to genotype with very high accuracy. Moreover, we are able to provide a generalized genotype in cancer samples (e.g. CCCCT at an amplified SNP). Our approach is illustrated on a variety of lung cancer cell lines and tumors, and a number of events are validated by independent computational and experimental means. An R software package containing the methods is freely available.     

Allele-specific amplification in cancer revealed by SNP array analysis

Amplification, deletion, and loss of heterozygosity of genomic DNA are hallmarks of cancer. In recent years a variety of studies have emerged measuring total chromosomal copy number at increasingly high resolution. Similarly, loss-of-heterozygosity events have been finely mapped using high-throughput genotyping technologies. We have developed a probe-level allele-specific quantitation procedure that extracts both copy number and allelotype information from single nucleotide polymorphism (SNP) array data to arrive at allele-specific copy number across the genome. Our approach applies an expectation-maximization algorithm to a model derived from a novel classification of SNP array probes. This method is the first to our knowledge that is able to (a) determine the generalized genotype of aberrant samples at each SNP site (e.g., CCCCT at an amplified site), and (b) infer the copy number of each parental chromosome across the genome. With this method, we are able to determine not just where amplifications and deletions occur, but also the haplotype of the region being amplified or deleted. The merit of our model and general approach is demonstrated by very precise genotyping of normal samples, and our allele-specific copy number inferences are validated using PCR experiments. Applying our method to a collection of lung cancer samples, we are able to conclude that amplification is essentially monoallelic, as would be expected under the mechanisms currently believed responsible for gene amplification. This suggests that a specific parental chromosome may be targeted for amplification, whether because of germ line or somatic variation. An R software package containing the methods described in this paper is freely available at http://genome.dfci.harvard.edu/~tlaframb/PLASQ.     

Integrative genomic analysis of small-cell lung carcinoma reveals correlates of sensitivity to bcl-2 antagonists and uncovers novel chromosomal gains

Cancer is a highly heterogeneous disease in terms of the genetic profile and the response to therapeutics. An early identification of a genomic marker in drug discovery may help select patients that would respond to treatment in clinical trials. Here we suggest coupling compound screening with comparative genomic hybridization analysis of the model systems for early discovery of genomic biomarkers. A Bcl-2 antagonist, ABT-737, has recently been discovered and shown to induce regression of solid tumors, but its activity is limited to a fraction of small-cell lung carcinoma (SCLC) models tested. We used comparative genomic hybridization on high-density single-nucleotide polymorphism genotyping arrays to carry out a genome-wide analysis of 23 SCLC cell lines sensitive and resistant to ABT-737. The screen revealed a number of novel recurrent gene copy number abnormalities, which were also found in an independent data set of 19 SCLC tumors and confirmed by real-time quantitative PCR. A previously unknown amplification was identified on 18q and associated with the sensitivity of SCLC cell lines to ABT-737 and another Bcl-2 antagonist. The region of gain contains Bcl-2 and NOXA, two apoptosis-related genes. Expression microarray profiling showed that the genes residing in the amplified region of 18q are also overexpressed in the sensitive lines relative to the resistant lines. Fluorescence in situ hybridization analysis of tumors revealed that Bcl-2 gain is a frequent event in SCLC. Our findings suggest that 18q21-23 copy number will be a clinically relevant predictor for sensitivity of SCLC to Bcl-2 family inhibitors. The 18q21-23 genomic marker may have a broader application in cancer because Bcl-2 is associated with apoptosis evasion and chemoresistance.     

Integrated genome-wide gene expression map and high-resolution analysis of aberrant chromosomal regions in squamous cell lung cancer

The recognition of recurrent aberrant regions in cancer is important to the discovery of candidate cancer related genes. Here we first constructed a genome-wide gene expression map of squamous lung carcinoma from the Stanford Microarray Database. High-resolution detection of aberrant chromosomal regions was performed by using moving-median method. 84% (27 of 32) of our results were consistent with the previous studies of comparative genomic hybridization or loss of heterozygosity. One overrepresented region in Xq28 was newly discovered to be related to squamous cell lung carcinoma. These observations could be of great interest for further studies.     

Exhaled Breath Analysis for Lung Cancer Detection Using Ion Mobility Spectrometry

Background

Conventional methods for lung cancer detection including computed tomography (CT) and bronchoscopy are expensive and invasive. Thus, there is still a need for an optimal lung cancer detection technique.

Methods

The exhaled breath of 50 patients with lung cancer histologically proven by bronchoscopic biopsy samples (32 adenocarcinomas, 10 squamous cell carcinomas, 8 small cell carcinomas), were analyzed using ion mobility spectrometry (IMS) and compared with 39 healthy volunteers. As a secondary assessment, we compared adenocarcinoma patients with and without epidermal growth factor receptor (EGFR) mutation.

Results

A decision tree algorithm could separate patients with lung cancer including adenocarcinoma, squamous cell carcinoma and small cell carcinoma. One hundred-fifteen separated volatile organic compound (VOC) peaks were analyzed. Peak-2 noted as n-Dodecane using the IMS database was able to separate values with a sensitivity of 70.0% and a specificity of 89.7%. Incorporating a decision tree algorithm starting with n-Dodecane, a sensitivity of 76% and specificity of 100% was achieved. Comparing VOC peaks between adenocarcinoma and healthy subjects, n-Dodecane was able to separate values with a sensitivity of 81.3% and a specificity of 89.7%. Fourteen patients positive for EGFR mutation displayed a significantly higher n-Dodecane than for the 14 patients negative for EGFR (p<0.01), with a sensitivity of 85.7% and a specificity of 78.6%.

Conclusion

In this prospective study, VOC peak patterns using a decision tree algorithm were useful in the detection of lung cancer. Moreover, n-Dodecane analysis from adenocarcinoma patients might be useful to discriminate the EGFR mutation.     

Impact on disease development, genomic location and biological function of copy number alterations in non-small cell lung cancer

Lung cancer, of which more than 80% is non-small cell, is the leading cause of cancer-related death in the United States. Copy number alterations (CNAs) in lung cancer have been shown to be positionally clustered in certain genomic regions. However, it remains unclear whether genes with copy number changes are functionally clustered. Using a dense single nucleotide polymorphism array, we performed genome-wide copy number analyses of a large collection of non-small cell lung tumors (n = 301). We proposed a formal statistical test for CNAs between different groups (e.g., non-involved lung vs. tumors, early vs. late stage tumors). We also customized the gene set enrichment analysis (GSEA) algorithm to investigate the overrepresentation of genes with CNAs in predefined biological pathways and gene sets (i.e., functional clustering). We found that CNAs events increase substantially from germline, early stage to late stage tumor. In addition to genomic position, CNAs tend to occur away from the gene locations, especially in germline, non-involved tissue and early stage tumors. Such tendency decreases from germline to early stage and then to late stage tumors, suggesting a relaxation of selection during tumor progression. Furthermore, genes with CNAs in non-small cell lung tumors were enriched in certain gene sets and biological pathways that play crucial roles in oncogenesis and cancer progression, demonstrating the functional aspect of CNAs in the context of biological pathways that were overlooked previously. We conclude that CNAs increase with disease progression and CNAs are both positionally and functionally clustered. The potential functional capabilities acquired via CNAs may be sufficient for normal cells to transform into malignant cells.     

术前预后营养指数对肺鳞状细胞癌患者术后复发及死亡的预测价值分析

摘要 目的：探讨术前预后营养指数（PNI）与肺鳞状细胞癌患者预后的关系及对术后复发、死亡的预测效能。方法：纳入2017年1月-2019年1月在我院接受治疗的78例肺鳞状细胞癌患者,所有患者均具有完整的临床资料及病理信息,对其进行门诊复查随访3年,除去失访病例共纳入76例患者资料,期间共有43例患者复发、37例患者死亡;按照复发及死亡情况将该76例患者分别分为复发组（n=43）及未复发组（n=33）,死亡组（n=37）及存活组（n=39）,分别使用单因素和多因素Logistic回归分析影响肺鳞状细胞癌患者复发及死亡的独立危险因素;采用受试者工作特征（ROC）曲线分别分析PNI在肺鳞状细胞癌患者术后复发及死亡的预测效能及最佳截断值。结果：单因素分析显示,TNM分期、吸烟年限、糖尿病、家族史、PNI是影响肺鳞状细胞癌患者术后复发的相关因素（P＜0.05）;性别、年龄、TNM分期、BMI、吸烟史、吸烟年限及PNI是影响肺鳞状细胞癌患者术后死亡的相关因素（P＜0.05）。多因素Logistic回归模型分析显示,TNM分期为Ⅲ期、吸烟年限较长、家族史是引发肺鳞状细胞癌患者术后复发的独立危险因素,PNI为保护因素（P＜0.05）;另外男性、年龄较大、TNM分期为Ⅲ期、吸烟年限较长是引发肺鳞状细胞癌患者术后死亡的独立危险因素,PNI为保护因素（P＜0.05）;ROC分析显示PNI在预测肺鳞状细胞癌患者术后复发的曲线下面积为0.726,敏感度为0.814,特异度为0.667,最佳截断值为48;PNI在预测肺鳞状细胞癌患者术后存活的曲线下面积为0.787,敏感度为0.838,特异度为0.718,最佳截断值为50。结论：PNI对肺鳞状细胞癌患者术后复发及生存均具有较高的预测效能,提高PNI水平对改善肺鳞状细胞癌患者的预后具有积极作用。