Classification and survival prediction for early-stage lung adenocarcinoma and squamous cell carcinoma patients

Immune-active tumor-adjacent tissues are associated with favorable prognosis in stage I lung squamous cell carcinoma

The immunogenomic features of tumor-adjacent lungs (TALs) in stage I lung squamous cell carcinoma (LUSC) are not clear. Multiomics analyses of tumor tissues and paired TALs from 59 stage I LUSC patients were performed. Compared to tumors, TALs exhibited a better-preserved immune contexture indicated by upregulation of immune pathways, increased immune infiltration, and higher expression of immune effector molecules. Notably, TALs had no mutations in PTEN and KEAP1, a lower incidence of human leukocyte antigen (HLA) loss and higher expression of HLA class I genes, major histocompatibility complex (MHC) I chaperones, and interferon (IFN)-γ-associated genes. Digital spatial profiling validated the generally higher immune infiltration in TALs and revealed a higher level of immune heterogeneity in LUSC tumors. Importantly, patients with higher immune infiltration in TALs had significantly longer survival, while high immune heterogeneity was associated with inferior patient survival. Our work can be considered in the selection of patients for adjuvant therapy, especially immunotherapy.

Artificial intelligence-based non-small cell lung cancer transcriptome RNA-sequence analysis technology selection guide

The incidence and mortality rates of lung cancer are high worldwide, where non-small cell lung cancer (NSCLC) accounts for more than 85% of lung cancer cases. Recent non-small cell lung cancer research has been focused on analyzing patient prognosis after surgery and identifying mechanisms in connection with clinical cohort and ribonucleic acid (RNA) sequencing data, including single-cell ribonucleic acid (scRNA) sequencing data. This paper investigates statistical techniques and artificial intelligence (AI) based non-small cell lung cancer transcriptome data analysis methods divided into target and analysis technology groups. The methodologies of transcriptome data were schematically categorized so researchers can easily match analysis methods according to their goals. The most widely known and frequently utilized transcriptome analysis goal is to find essential biomarkers and classify carcinomas and cluster NSCLC subtypes. Transcriptome analysis methods are divided into three major categories: Statistical analysis, machine learning, and deep learning. Specific models and ensemble techniques typically used in NSCLC analysis are summarized in this paper, with the intent to lay a foundation for advanced research by converging and linking the various analysis methods available.

The value of AGR2 and KRT5 as an immunomarker combination in distinguishing lung squamous cell carcinoma from adenocarcinoma

With the advancement of tumor subtype-specific treatments, precise histopathologic distinction between adenocarcinoma (ADC) and squamous cell carcinoma (SCC) is of significant clinical importance. Nevertheless, the current markers are insufficiently precise in poorly differentiated tissue. This study aimed to establish a histology-specific immunomarker combination to subclassify non-small cell lung cancer (NSCLC) specimens. Based on previous work, we assessed the differential expression of anterior gradient 2 (AGR2) and keratin 5 (KRT5) in ADC and SCC by analyzing public datasets and postoperative specimens. Subsequently, we established a train set (n = 188) and a validation set (n = 42) comprised of NSCLC surgical specimens for training and verifying the subtype-identification capabilities of the two biomarkers separately and in combination, and contrasted the diagnostic utility of AGR2-KRT5 with that of the classic immunomarker combination, TTF1-P40. Differential expression of the two genes was statistically significant in ADC and SCC samples, both at the mRNA and protein levels. The specificity and sensitivity of AGR2 to detect ADC in the training set were 97.0% and 94.4%, while the sensitivity and specificity of KRT5 to determine SCC were 93.9% and 98.9%, respectively. The accuracies of AGR2-KRT5 in ADC, SCC, and across all samples were 93.3%, 92.0% and 92.6% respectively. In the validation cohort, the predictive accuracy of AGR2-KRT5 was up to 100% for ADC and 86.7% for SCC. Compared with TTF1-P40 in ADC samples, AGR2-KRT5 had 8.4% higher accuracy. In summary, the AGR2-KRT5 immunomarker combination reliably distinguished SCC from ADC, and was more accurate than TTF1-P40 in ADC.

Lung squamous cell carcinoma and lung adenocarcinoma differential gene expression regulation through pathways of Notch, Hedgehog, Wnt, and ErbB signalling

Lung malignancies comprise lethal and aggressive tumours that remain the leading cancer-related death cause worldwide. Regarding histological classification, lung squamous cell carcinoma (LUSC) and adenocarcinoma (LUAD) account for the majority of cases. Surgical resection and various combinations of chemo- and radiation therapies are the golden standards in the treatment of lung cancers, although the five-year survival rate remains very poor. Notch, Hedgehog, Wnt and Erbb signalling are evolutionarily conserved pathways regulating pivotal cellular processes such as differentiation, proliferation, and angiogenesis during embryogenesis and post-natal life. However, to date, there is no study comprehensively revealing signalling networks of these four pathways in LUSC and LUAD. Therefore, the aim of the present study was the investigation profiles of downstream target genes of pathways that differ between LUSC and LUAD biology. Our results showed a few co-expression modules, identified through weighted gene co-expression network analysis (WGCNA), which significantly differentiated downstream signaling of Notch, ErbB, Hedgehog, and Wnt in LUSC and LUAD. Among co-expressed genes essential regulators of the cell cycle, DNA damage response, apoptosis, and proliferation have been found. Most of them were upregulated in LUSC compared to LUAD. In conclusion, identified downstream networks revealed distinct biological mechanisms underlying cancer development and progression in LUSC and LUAD that may diversify the clinical outcome of the disease.

A risk score system based on DNA methylation levels and a nomogram survival model for lung squamous cell carcinoma

Lung squamous cell carcinoma (LSCC) is one of the primary types of non-small cell lung carcinoma, and patients with recurrent LSCC usually have a poor prognosis. The present study was conducted to build a risk score (RS) system for LSCC. Methylation data on LSCC (training set) and on head and neck squamous cell carcinoma (validation set 2) were obtained from The Cancer Genome Atlas database, and GSE39279 (validation set 1) was retrieved from the Gene Expression Omnibus database. Differentially methylated protein-coding genes (DMGs)/long non-coding RNAs (DM-lncRNAs) between recurrence-associated samples and nonrecurrence samples were screened out using the limma package, and their correlation analysis was conducted using the cor.test() function. Following identification of the optimal combinations of DMGs or DM-lncRNAs using the penalized package in R, RS systems were built, and the system with optimal performance was selected. Using the rms package, a nomogram survival model was then constructed. For the differentially expressed genes (DEGs) between the high- and low-risk groups, pathway enrichment analysis was performed by Gene Set Enrichment Analysis. There were 335 DMGs and DM-lncRNAs in total. Following screening out of the top 10 genes (aldehyde dehydrogenase 7 family member A1, chromosome 8 open reading frame 48, cytokine-like 1, heat shock protein 90 alpha family class A member 1, isovaleryl-CoA dehydrogenase, phosphodiesterase 3A, PNMA family member 2, SAM domain, SH3 domain and nuclear localization signals 1, thyroid hormone receptor interactor 13 and zinc finger protein 878) and 6 top lncRNAs, RS systems were constructed. According to Kaplan-Meier analysis, the DNA methylation level-based RS system exhibited the best performance. In combination with independent clinical prognostic factors, a nomogram survival model was built and successfully predicted patient survival. Furthermore, 820 DEGs between the high- and low-risk groups were identified, and 3 pathways were identified to be enriched in this gene set. The 10-DMG methylation level-based RS system and the nomogram survival model may be applied for predicting the outcomes of patients with LSCC.

A qualitative transcriptional signature for the histological reclassification of lung squamous cell carcinomas and adenocarcinomas

Background

Targeted therapy for non-small cell lung cancer is histology dependent. However, histological classification by routine pathological assessment with hematoxylin-eosin staining and immunostaining for poorly differentiated tumors, particularly those from small biopsies, is still challenging. Additionally, the effectiveness of immunomarkers is limited by technical inconsistencies of immunostaining and lack of standardization for staining interpretation.

Results

Using gene expression profiles of pathologically-determined lung adenocarcinomas and squamous cell carcinomas, denoted as pADC and pSCC respectively, we developed a qualitative transcriptional signature, based on the within-sample relative gene expression orderings (REOs) of gene pairs, to distinguish ADC from SCC. The signature consists of two genes, KRT5 and AGR2, which has the stable REO pattern of KRT5 > AGR2 in pSCC and KRT5 < AGR2 in pADC. In the two test datasets with relative unambiguous NSCLC types, the apparent accuracy of the signature were 94.44 and 98.41%, respectively. In the other integrated dataset for frozen tissues, the signature reclassified 4.22% of the 805 pADC patients as SCC and 12% of the 125 pSCC patients as ADC. Similar results were observed in the clinical challenging cases, including FFPE specimens, mixed tumors, small biopsy specimens and poorly differentiated specimens. The survival analyses showed that the pADC patients reclassified as SCC had significantly shorter overall survival than the signature-confirmed pADC patients (log-rank p = 0.0123, HR = 1.89), consisting with the knowledge that SCC patients suffer poor prognoses than ADC patients. The proliferative activity, subtype-specific marker genes and consensus clustering analyses also supported the correctness of our signature.

Conclusions

The non-subjective qualitative REOs signature could effectively distinguish ADC from SCC, which would be an auxiliary test for the pathological assessment of the ambiguous cases.

Construction of subtype-specific prognostic gene signatures for early-stage non-small cell lung cancer using meta feature selection methods

Feature selection in the framework of meta-analyses (meta feature selection), combines meta-analysis with a feature selection process and thus allows meta-analysis feature selection across multiple datasets. In the present study, a meta feature selection procedure that fitted a multiple Cox regression model to estimate the effect size of a gene in individual studies and to identify the overall effect of the gene using a meta-analysis model was proposed. The method was used to identify prognostic gene signatures for lung adenocarcinoma and lung squamous cell carcinoma. Furthermore, redundant gene elimination (RGE) is of crucial importance during feature selection, and is also essential for a meta feature selection process. The current study demonstrated that the proposed meta feature selection procedure with RGE outperforms that without RGE in terms of predictive ability, model parsimony and biological interpretation.

A Proposal to Reflect Survival Difference and Modify the Staging System for Lung Adenocarcinoma and Squamous Cell Carcinoma: Based on the Machine Learning

Objective: To propose modifications to refine prognostication over anatomic extent of the current tumor, node, and metastasis (TNM) staging system of non-small cell lung cancer (NSCLC) for a better distinction, and reflect survival differences of lung adenocarcinoma and squamous cell carcinoma. Study Design: Three large cohorts were included in this study. The training cohort consisted of 124,788 patients in the Surveillance, Epidemiology, and End Results (SEER) database (2006-2015). The validation cohort consisted of 4,247 patients from the Zhongshan Hospital, Fudan University (FDZSH; 2005-2014), and People's Hospital, Peking University (PKUPH; 2000-2017). The algorithm generated a hierarchical clustering model based on the unsupervised learning for survival data using Kaplan-Meier curves and log-rank test statistics for recursive partitioning and selection of the principal groupings. Results: In the modified staging system, adenocarcinoma cases are usually at a lower stage than the squamous cell carcinoma cases of the same TNM, reflecting a better outcome of adenocarcinoma than that of squamous cell carcinoma. The C-index of the modified staging system was significantly superior to that of the staging system [SEER cohort: 0.722, 95% CI, (0.721-0.723) vs. 0.643, 95% CI, (0.640-0.647); FDZSH cohort: 0.720, 95% CI, (0.709-0.731) vs. 0.519, 95% CI, (0.450-0.586); and PKUPH cohort: 0.730, 95% CI, (0.705-0.735) vs. 0.728, 95% CI, (0.703-0.753)]. Conclusion: Survival differences between lung adenocarcinoma and squamous cell carcinoma have been reflected accurately and reliably in the modified staging system based on the machine learning. It may refine prognostication over anatomic extent.

Identification of monotonically differentially expressed genes for non-small cell lung cancer

Background

Monotonically expressed genes (MEGs) are genes whose expression values increase or decrease monotonically as a disease advances or time proceeds. Non-small cell lung cancer (NSCLC) is a multistage progression process resulting from genetic sequences mutations, the identification of MEGs for NSCLC is important.

Results

With the aid of a feature selection algorithm capable of identifying MEGs – the MFSelector method – two sets of potential MEGs were selected in this study: the MEGs across the different pathologic stages and the MEGs across the risk levels of death for the NSCLC patients at early stages. For the lung adenocarcinoma (AC) subtypes no statistically significant MEGs were identified across pathologic stages, however dozens of MEGs were identified across the risk levels of death. By contrast, for the squamous cell lung carcinoma (SCC) there were no statistically significant MEGs as either stage or risk level advanced.

Conclusions

The pathologic stage of non-small cell lung cancer patients at early stages has no prognostic value, making the identification of prognostic gene signatures for them more meaningful and highly desirable.

Prognostic significance of the combination of preoperative fibrinogen and the neutrophil-lymphocyte ratio in patients with non-small cell lung cancer following surgical resection

The purpose of the present study was to evaluate the prognostic value of preoperative coagulation factor levels (including fibrinogen and D-dimer) and inflammatory indicators in patients with non-small cell lung cancer (NSCLC). The medical records of 456 patients with NSCLC who had undergone curative resection were retrospectively analysed. The recommended cut-off values for preoperative fibrinogen, neutrophil-lymphocyte ratio (NLR), platelet-lymphocyte ratio and lymphocyte-monocyte ratio were determined using receiver operating characteristic curve analyses. The associations between preoperative fibrinogen or D-dimer levels and clinicopathological variables were analysed using the χ² test. Univariate Kaplan-Meier analysis and a multivariate Cox proportional hazards model were applied to identify which prognostic variables were significantly associated with overall survival (OS) rates. Multivariate analyses revealed that lymph node metastasis (P<0.001), preoperative fibrinogen (P=0.024) and NLR (P=0.028) were effective independent prognostic variables associated with OS. Based on this result, a novel, single inflammation-based combination of fibrinogen and NLR (COF-NLR) score was proposed for the determination of prognosis. Patients with elevated fibrinogen and NLR levels were allocated a score of 2 (n=136), and those that demonstrated elevated levels of one or neither were allocated a score of 1 (n=152) or 0 (n=168), respectively. The 5-year OS rates were significantly poorer for patients with COF-NLR=2 compared with those with COF-NLR=1 or 0 (23.5% vs. 34.2% vs. 50.0%, P<0.001). A subgroup analysis demonstrated that the prognostic significance of COF-NLR was independent of histological subtype, lymph node metastasis and pathological stage. Therefore, COF-NLR has potential as a novel and useful blood marker for predicting tumour progression and the postoperative survival of patients with NSCLC. It may assist clinicians in risk stratification, prognosis predictions and facilitating individualised treatment.

Identification of subtype-specific prognostic signatures using Cox models with redundant gene elimination

Lung cancer (LC) is a leading cause of cancer-associated mortalities worldwide. Adenocarcinoma (AC) and squamous cell carcinoma (SCC) account for ~70% of all cases of LC. Since AC and SCC are two distinct diseases, their corresponding prognostic genes associated with patient survival time are expected to be different. To date, only a few studies have distinguished patients with good prognosis from those with poor prognosis for each specific subtype. In the present study, the Cox filter model, a feature selection algorithm that identifies subtype-specific prognostic genes to incorporate pathway information and eliminate redundant genes, was adopted. By applying the proposed model to data on non-small cell lung cancer (NSCLC), it was demonstrated that both redundant gene elimination and search space restriction can improve the predictive capacity and the model stability of resulting prognostic gene signatures. To conclude, a pre-filtering procedure that incorporates pathway information for screening likely irrelevant genes prior to complex downstream analysis is recommended. Furthermore, a feature selection algorithm that considers redundant gene elimination may be preferable to one without such a consideration.