The genomic alterations of lung adenocarcinoma and lung squamous cell carcinoma can explain the differences of their overall survival rates

Clinicopathological characteristics and prognosis of synchronous brain metastases from non-small cell lung cancer compared with metachronous brain metastases

Purpose

Brain metastasis (BM) from non-small cell lung cancer (NSCLC) is a serious complication severely affecting patients' prognoses. We aimed to compare the clinicopathological features and prognosis of synchronous and metachronous BM from NSCLC.

Methods

Clinical data of 461 patients with brain metastases from NSCLC who visited the Cancer Hospital of China Medical University from 2005 to 2017 were retrospectively collected. We analyzed the pathophysiological characteristics of synchronous and metachronous BM from NSCLC and survival rates of the patients. Propensity score matching analysis was used to reduce bias between groups. In addition, we used the Kaplan-Meier method for survival analysis, log-rank test to compare survival rates, and Cox proportional hazards regression model for multivariate prognosis analysis.

Results

Among 461 patients with BM, the number of people who met the inclusion criteria was 400 cases, and after 1:2 propensity score matching,130 had synchronous BM and 260 had metachronous BM. The survival time was longer for metachronous BM in driver mutation-negative patients with squamous cell carcinoma than synchronous BM. Conversely, metachronous and synchronous BM with gene mutations and adenocarcinoma showed no differences in survival time. Multivariate analysis showed that metachronous BM was an independent prognostic factor for overall survival. Furthermore, the pathological type squamous cell carcinoma and Karnofsky Performance Status score <80 were independent risk factors affecting overall survival.

Conclusion

BM status is an independent factor influencing patient outcome. Moreover, synchronous and metachronous BM from NSCLC differ in gene mutation profile, pathological type, and disease progression and hence require different treatments.

Deciphering a cell death-associated signature for predicting prognosis and response to immunotherapy in lung squamous cell carcinoma

BACKGROUND

Lung squamous cell carcinoma (LUSC) is a subtype of non-small cell carcinoma, accounting for about 30% of all lung cancers. Yet, the evaluation of prognostic outcome and therapy response of patients with LUSC remains to be resolved. This study aimed to explore the prognostic value of cell death pathways and develop a cell death-associated signature for predicting prognosis and guiding treatment in LUSC.

METHODS

Transcriptome profiles and corresponding clinical information of LUSC patients were gathered from The Cancer Genome Atlas (TCGA-LUSC, n = 493) and Gene Expression Omnibus database (GSE74777, n = 107). The cell death-related genes including autophagy (n = 348), apoptosis (n = 163), and necrosis (n = 166) were retrieved from the Kyoto Encyclopedia of Genes and Genomes and Gene Ontology databases. In the training cohort (TCGA-LUSC), LASSO Cox regression was used to construct four prognostic signatures of respective autophagy, apoptosis, and necrosis pathway and genes of three pathways. After comparing the four signatures, the cell death index (CDI), the signature of combined genes, was further validated in the GSE74777 dataset. We also investigated the clinical significance of the CDI signature in predicting the immunotherapeutic response of LUSC patients.

RESULTS

The CDI signature was significantly associated with the overall survival of LUSC patients in the training cohort (HR, 2.13; 95% CI, 1.62‒2.82; P < 0.001) and in the validation cohort (HR, 1.94; 95% CI, 1.01‒3.72; P = 0.04). The differentially expressed genes between the high- and low-risk groups contained cell death-associated cytokines and were enriched in immune-associated pathways. We also found a higher infiltration of naive CD4⁺ T cells, monocytes, activated dendritic cells, neutrophils, and lower infiltration of plasma cells and resting memory CD4⁺ T cells in the high-risk group. Tumor stemness indices, mRNAsi and mDNAsi, were both negatively correlated with the risk score of the CDI. Moreover, LUSC patients in the low-risk group are more likely to respond to immunotherapy than those in the high-risk group (P = 0.002).

CONCLUSIONS

This study revealed a reliable cell death-associated signature (CDI) that closely correlated with prognosis and the tumor microenvironment in LUSC, which may assist in predicting the prognosis and response to immunotherapy for patients with LUSC.

Genomic landscape of locally advanced rectal adenocarcinoma: Comparison between before and after neoadjuvant chemoradiation and effects of genetic biomarkers on clinical outcomes and tumor response

PURPOSE

To explore genomic biomarkers in rectal cancer by performing whole-exome sequencing.

MATERIALS AND METHODS

Pre-chemoradiation (CRT) biopsy and post-CRT surgical specimens were obtained from 27 patients undergoing neoadjuvant CRT followed by definitive resection. Exomes were sequenced to a mean coverage of 30×. Somatic single-nucleotide variants (SNVs) and insertions/deletions (indels) were identified. Tumor mutational burden was defined as the number of SNVs or indels. Mutational signatures were extracted and fitted to COSMIC reference signatures. Tumor heterogeneity was quantified with a mutant-allele tumor heterogeneity (MATH) score. Genetic biomarkers and frequently occurred copy number alterations (CNAs) were compared between pre- and post-CRT specimens. Their associations with tumor regression grade (TRG) and clinical outcomes were explored.

RESULTS

Top five mutated genes were APC, TP53, NF1, KRAS, and NOTCH1 for pre-CRT samples and APC, TP53, NF1, CREBBP, and ATM for post-CRT samples. Several gene mutations including RUNX1, EGFR, and TP53 in pre-CRT samples showed significant association with clinical outcomes, but not with TRG. However, no such association was found in post-CRT samples. Discordance of driver mutation status was found between pre- and post-CRT samples. In tumor mutational burden analysis, higher number of SNVs or indels was associated with worse treatment outcomes. Six single-base substitution (SBS) signatures identified were SBS1, SBS30, SBS29, SBS49, SBS3, and SBS44. The MATH score decreased after CRT on paired analysis. Less than half of CNAs frequent in post-CRT samples were present in pre-CRT samples.

CONCLUSION

Pre- and post-CRT samples showed different genomic landscape. Potential genetic biomarkers of pre-CRT samples found in the current analysis call for external validation.

APOBEC3-mediated mutagenesis in cancer: causes, clinical significance and therapeutic potential

Apolipoprotein B mRNA-editing enzyme, catalytic polypeptides (APOBECs) are cytosine deaminases involved in innate and adaptive immunity. However, some APOBEC family members can also deaminate host genomes to generate oncogenic mutations. The resulting mutations, primarily signatures 2 and 13, occur in many tumor types and are among the most common mutational signatures in cancer. This review summarizes the current evidence implicating APOBEC3s as major mutators and outlines the exogenous and endogenous triggers of APOBEC3 expression and mutational activity. The review also discusses how APOBEC3-mediated mutagenesis impacts tumor evolution through both mutagenic and non-mutagenic pathways, including by inducing driver mutations and modulating the tumor immune microenvironment. Moving from molecular biology to clinical outcomes, the review concludes by summarizing the divergent prognostic significance of APOBEC3s across cancer types and their therapeutic potential in the current and future clinical landscapes.

Primary mucosal melanomas of the head and neck are characterised by overexpression of the DNA mutating enzyme APOBEC3B

AIMS

Primary head/neck mucosal melanomas (MMs) are rare and exhibit aggressive biologic behaviour and elevated mutational loads. The molecular mechanisms responsible for high genomic instability observed in head/neck MMs remain elusive. The DNA cytosine deaminase APOBEC3B (A3B) constitutes a major endogenous source of mutation in human cancer. A3B-related mutations are identified through C-to-T/-G base substitutions in 5'-TCA/T motifs. Herein, we present immunohistochemical and genomic data supportive of a role for A3B in head/neck MMs.

METHODS AND RESULTS

A3B protein levels were assessed in oral (n = 13) and sinonasal (n = 13) melanomas, and oral melanocytic nevi (n = 13) by immunohistochemistry using a custom rabbit α-A3B mAb (5210-87-13). Heterogeneous, selective-to-diffuse, nuclear only, A3B immunopositivity was observed in 12 of 13 (92.3%) oral melanomas (H-score range = 9-72, median = 40) and 8 of 13 (62%) sinonasal melanomas (H-score range = 1-110, median = 24). Two cases negative for A3B showed prominent cytoplasmic staining consistent with A3G. A3B protein levels were significantly higher in oral and sinonasal MMs than intraoral melanocytic nevi (P < 0.0001 and P = 0.0022, respectively), which were A3B-negative (H-score range = 1-8, median = 4). A3B levels, however, did not differ significantly between oral and sinonasal tumours (P > 0.99). NGS performed in 10 sinonasal MMs revealed missense NRAS mutations in 50% of the studied cases and one each KIT and HRAS mutations. Publicly available whole-genome sequencing (WGS) data disclosed that the number of C-to-T mutations and APOBEC3 enrichment score were markedly elevated in head/neck MMs (n = 2).

CONCLUSION

The above data strongly indicate a possible role for the mutagenic enzyme A3B in head/neck melanomagenesis, but not benign melanocytic neoplasms.

Effect of sample size on prognostic genes analysis in non-small cell lung cancer

The identification of prognostic genes can help in the clinical management of non-small cell lung cancer (NSCLC). However, there is little overlap in the prognostic genes identified in different NSCLC studies. One reason for this may be the inadequate sample size. Here, the effect of sample size on prognostic genes analysis was investigated based on 515 stage II/III NSCLC cases from two cohorts detected by whole-exome sequencing. Prognostic genes analysis was repeatedly performed 100 times for each sample size level using random resampling methods. In stage II lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) cases from the TCGA Pan-Lung Cancer cohort, the number of statistically significant prognostic genes first increased with sample size in a power law, then fluctuated steadily, and finally decreased slightly. The power law growth curves were also observed in stage III LUAD and LUSC cases from the TCGA Pan-Lung Cancer cohort and stage III Chinese LUAD cases from the OncoSG cohort. The correlation R² of the fitted power law growth curves were all greater than 0.99. In addition, at the sample size level where the number of prognostic genes peaked, the mean proportion of true prognostic genes in patients with stage II LUAD and LUSC was 28.32% and 23.12%, which could partly explain the little overlap in prognostic genes between reports. In conclusion, the number of prognostic genes takes a power law growth with the sample size in NSCLC, independent of histopathological subtype, race, and stage. These results also show how sample size affects the reliability of prognostic genes and will aid trial design for genomic mutation-based prognostic studies in NSCLC.

Intrinsic and Extrinsic Transcriptional Profiles That Affect the Clinical Response to PD-1 Inhibitors in Patients with Non-Small Cell Lung Cancer

Using a machine learning method, we investigated the intrinsic and extrinsic transcriptional profiles that affect the clinical response to PD-1 inhibitors in 57 patients with non-small cell lung cancer (NSCLC). Among the top 100 genes associated with the responsiveness to PD-1 inhibitors, the proportion of intrinsic genes in lung adenocarcinoma (LUAD) (69%) was higher than in NSCLC overall (36%) and lung squamous cell carcinoma (LUSC) (33%). The intrinsic gene signature of LUAD (mean area under the ROC curve (AUC) = 0.957 and mean accuracy = 0.9) had higher predictive power than either the intrinsic gene signature of NSCLC or LUSC or the extrinsic gene signature of NSCLC, LUAD, or LUSC. The high intrinsic gene signature group had a high overall survival rate in LUAD (p = 0.034). When we performed a pathway enrichment analysis, the cell cycle and cellular senescence pathways were related to the upregulation of intrinsic genes in LUAD. The intrinsic signature of LUAD also showed a positive correlation with other immune checkpoint targets, including CD274, LAG3, and PDCD1LG2 (Spearman correlation coefficient > 0.25). PD-1 inhibitor-related intrinsic gene patterns differed significantly between LUAD and LUSC and may be a particularly useful biomarker in LUAD.

Unravelling of the comparative Transcriptomic Profile of Gallbladder Cancer using mRNA sequencing

BACKGROUND

Gallbladder cancer (GBC) represents a wide geographical diversity as well as heterogeneity in clinical and genomic landscape. There seems to be little progress in the development of diagnostic biomarkers, targeted therapies or individualized approaches to GBC management. In this study, we investigated the whole transcriptome profile of GBC patients using RNA sequencing and identified key genes and pathways associated with gallbladder cancer using bioinformatics.

METHODOLOGY

A total of 10 cases of GBC were collected and sequenced. The raw reads of the gallbladder sample was compared with the gallbladder normal control (SRA Database ID: ERX288537: HPA RNA-seq normal tissues gallbladder). Using Gene ontology analysis the differentially expressed genes were categorized into the biological pathway, cellular component, and molecular function. Pathway enrichment analyses, protein-protein interaction, transcription factor and miRNA interaction that regulate the expression of hub genes were conducted using bioinformatics tool.

RESULTS

A total of 954 differentially expressed mRNA transcripts were identified, including overexpression of REG4, TMEM238, S100A2, LYPD2, and KRT17, as well as underexpressed genes like CCKAR, IGSF10, CHRM2, CRISP3, and FGF19. Enrichment analysis showed the metabolic pathways to be the top five cancer pathways in gallbladder carcinogenesis besides PI3k-Akt signalling pathway, cAMP signalling pathway, miRNAs in cancer, and cell adhesion profile of GBC.

CONCLUSIONS

CCKAR, CDKN2A and LRRK2 were found to be most involved genes in its progression and development through different regulatory pathways. Further, most of the genes were significantly involved in PI3k-Akt, Wnt and hedgehog signaling pathways which have a key role in gallbladder cancer development.

Integrative Analysis for Identification of Therapeutic Targets and Prognostic Signatures in Non-Small Cell Lung Cancer

Differential expressions of certain genes during tumorigenesis may serve to identify novel manageable targets in the clinic. In this work with an integrated bioinformatics approach, we analyzed public microarray datasets from Gene Expression Omnibus (GEO) to explore the key differentially expressed genes (DEGs) in non-small cell lung cancer (NSCLC). We identified a total of 984 common DEGs in 252 healthy and 254 NSCLC gene expression samples. The top 10 DEGs as a result of pathway enrichment and protein–protein interaction analysis were further investigated for their prognostic performances. Among these, we identified high expressions of CDC20, AURKA, CDK1, EZH2, and CDKN2A genes that were associated with significantly poorer overall survival in NSCLC patients. On the contrary, high mRNA expressions of CBL, FYN, LRKK2, and SOCS2 were associated with a significantly better prognosis. Furthermore, our drug target analysis for these hub genes suggests a potential use of Trichostatin A, Pracinostat, TGX-221, PHA-793887, AG-879, and IMD0354 antineoplastic agents to reverse the expression of these DEGs in NSCLC patients.

Emerging Trends in the Pathological Research of Human Papillomavirus-positive Oropharyngeal Squamous Cell Carcinoma

Oropharyngeal squamous cell carcinomas (OPSCCs) have shown an alarming rate of increase in incidence over the past several decades, markedly in men. In the United States, transcriptionally-active human papillomavirus (HPV), particularly HPV 16, has become the highest contributive agent of OPSCCs, affecting approximately 16,000 people a year. Compared to patients with HPV-negative OPSCCs, patients with HPV-positive OPSCCs exhibit better health responses to chemoradiotherapy and an overall increase in long-term survival. Despite promising treatment options, many OPSCCs are discovered at an advanced stage, and ~20% of cases will recur after definitive treatment. Therefore, extensive research is ongoing to identify new targets for precision treatment and to stratify tumor prognosis. The aim of this review is to capture the most updated research on HPV-positive OPSCCs, emphasizing their relevance as potential new targets for precision medicine and survival prognosis.

What's the difference between lung adenocarcinoma and lung squamous cell carcinoma? Evidence from a retrospective analysis in a cohort of Chinese patients

BACKGROUND

Lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) are the two most common subtypes of lung cancer. Previously, they were categorized into one histological subtype known as non-small cell lung cancer (NSCLC) and often treated similarly. However, increasing evidence suggested that LUAD and LUSC should be classified and treated as different cancers. But yet, detailed differences in clinical features between LUAD and LUSC have not been well described.

METHODS

A cohort of 142 Chinese patients with 111 LUAD and 31 LUSC cases were consecutively enrolled from April 2019 to October 2020 in Hunan Provincial People's Hospital. The clinical features of the patients were retrospectively analyzed and compared in the terms of general information, clinicopathologic characteristics, imaging findings and laboratory data.

RESULTS

In comparison with LUAD, LUSC patients had a significantly higher proportion of males, smokers, drinkers, higher-stage cases. The mean tumor size in LUSC patients was significantly larger than that in LUAD patients. Compared with LUAD patients, more of patients with LUSC had cough, fever and abundant sputum symptoms. Besides that, more bacterial infections and fungal infections were found in LUSC patients than that in LUAD patients. Imaging data shows that ground-glass opacity and patchy shadows in radiological films were more frequent in LUAD patients than that in LUSC patients. In addition to initial laboratory data, LUSC patients had higher levels of leukocytes, platelets, and creatinine that of LUAD patients.

CONCLUSIONS

Together, these results suggested that there exist distinct differences between LUAD and LUSC subtypes; LUSC may be a more malignant type in comparison with LUAD. Our findings may have potential implications in clinical settings. However, further multicenter studies are needed to validate these findings in a larger sample size.

Exosome-mediated transfer of SNHG7 enhances docetaxel resistance in lung adenocarcinoma

Long noncoding RNA (lncRNA) small nucleolar RNA host gene 7 (SNHG7) has been widely reported in various cancers, including lung adenocarcinoma (LUAD). However, it is largely unknown whether SNHG7 is involved in docetaxel resistance of LUAD. In the current study, we identified the high expression of SNHG7 in docetaxel-resistant cells. Through functional assays, we determined that silencing of SNHG7 decreased IC₅₀ value of LUAD cells to docetaxel and suppressed proliferation and autophagy in LUAD cells, and reversed M2 polarization in macrophages. Mechanistically, we uncovered that SNHG7 promoted autophagy via recruiting human antigen R (HuR) to stabilize autophagy-related genes autophagy related 5 (ATG5) and autophagy related 12 (ATG12). Moreover, exosomal SNHG7 was transmitted from docetaxel-resistant LUAD cells to parental LUAD cells and thus facilitated docetaxel resistance. Additionally, exosomal SNHG7 activated the phosphatidylinositol 3-kinase (PI3K)/AKT pathway to promote M2 polarization in macrophages via recruiting cullin 4A (CUL4A) to induce ubiquitination and degradation of phosphatase and tensin homolog (PTEN). Taken together, we concluded that exosomal SNHG7 enhances docetaxel resistance of LUAD cells through inducing autophagy and macrophage M2 polarization. All findings in the study suggested that SNHG7 may be a promising target for relieving docetaxel resistance in LUAD.

An Adaptive Transfer-Learning-Based Deep Cox Neural Network for Hepatocellular Carcinoma Prognosis Prediction

Background

Predicting hepatocellular carcinoma (HCC) prognosis is important for treatment selection, and it is increasingly interesting to predict prognosis through gene expression data. Currently, the prognosis remains of low accuracy due to the high dimension but small sample size of liver cancer omics data. In previous studies, a transfer learning strategy has been developed by pre-training models on similar cancer types and then fine-tuning the pre-trained models on the target dataset. However, transfer learning has limited performance since other cancer types are similar at different levels, and it is not trivial to balance the relations with different cancer types.

Methods

Here, we propose an adaptive transfer-learning-based deep Cox neural network (ATRCN), where cancers are represented by 12 phenotype and 10 genotype features, and suitable cancers were adaptively selected for model pre-training. In this way, the pre-trained model can learn valuable prior knowledge from other cancer types while reducing the biases.

Results

ATRCN chose pancreatic and stomach adenocarcinomas as the pre-training cancers, and the experiments indicated that our method improved the C-index of 3.8% by comparing with traditional transfer learning methods. The independent tests on three additional HCC datasets proved the robustness of our model. Based on the divided risk subgroups, we identified 10 HCC prognostic markers, including one new prognostic marker, TTC36. Further wet experiments indicated that TTC36 is associated with the progression of liver cancer cells.

Conclusion

These results proved that our proposed deep-learning-based method for HCC prognosis prediction is robust, accurate, and biologically meaningful.

Dynamic nomograms combining N classification with ratio-based nodal classifications to predict long-term survival for patients with lung adenocarcinoma after surgery: a SEER population-based study

Background

The prognostic roles of three lymph node classifications, number of positive lymph nodes (NPLN), log odds of positive lymph nodes (LODDS), and lymph node ratio (LNR) in lung adenocarcinoma are unclear. We aim to find the classification with the strongest predictive power and combine it with the American Joint Committee on Cancer (AJCC) 8th TNM stage to establish an optimal prognostic nomogram.

Methods

25,005 patients with T1-4N0–2M0 lung adenocarcinoma after surgery between 2004 to 2016 from the Surveillance, Epidemiology, and End Results database were included. The study cohort was divided into training cohort (13,551 patients) and external validation cohort (11,454 patients) according to different geographic region. Univariate and multivariate Cox regression analyses were performed on the training cohort to evaluate the predictive performance of NPLN (Model 1), LODDS (Model 2), LNR (Model 3) or LODDS+LNR (Model 4) respectively for cancer-specific survival and overall survival. Likelihood-ratio χ² test, Akaike Information Criterion, Harrell concordance index, integrated discrimination improvement (IDI) and net reclassification improvement (NRI) were used to evaluate the predictive performance of the models. Nomograms were established according to the optimal models. They’re put into internal validation using bootstrapping technique and external validation using calibration curves. Nomograms were compared with AJCC 8th TNM stage using decision curve analysis.

Results

NPLN, LODDS and LNR were independent prognostic factors for cancer-specific survival and overall survival. LODDS+LNR (Model 4) demonstrated the highest Likelihood-ratio χ² test, highest Harrell concordance index, and lowest Akaike Information Criterion, and IDI and NRI values suggested Model 4 had better prediction accuracy than other models. Internal and external validations showed that the nomograms combining TNM stage with LODDS+LNR were convincingly precise. Decision curve analysis suggested the nomograms performed better than AJCC 8th TNM stage in clinical practicability.

Conclusions

We constructed online nomograms for cancer-specific survival and overall survival of lung adenocarcinoma patients after surgery, which may facilitate doctors to provide highly individualized therapy.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08410-6.

Recurrent HBV Integration Targets as Potential Drivers in Hepatocellular Carcinoma

Chronic hepatitis B virus (HBV) infection is the major etiology of hepatocellular carcinoma (HCC), frequently with HBV integrating into the host genome. HBV integration, found in 85% of HBV-associated HCC (HBV–HCC) tissue samples, has been suggested to be oncogenic. Here, we investigated the potential of HBV–HCC driver identification via the characterization of recurrently targeted genes (RTGs). A total of 18,596 HBV integration sites from our in-house study and others were analyzed. RTGs were identified by applying three criteria: at least two HCC subjects, reported by at least two studies, and the number of reporting studies. A total of 396 RTGs were identified. Among the 28 most frequent RTGs, defined as affected in at least 10 HCC patients, 23 (82%) were associated with carcinogenesis and 5 (18%) had no known function. Available breakpoint positions from the three most frequent RTGs, TERT, MLL4/KMT2B, and PLEKHG4B, were analyzed. Mutual exclusivity of TERT promoter mutation and HBV integration into TERT was observed. We present an RTG consensus through comprehensive analysis to enable the potential identification and discovery of HCC drivers for drug development and disease management.

Endogenous APOBEC3B overexpression characterizes HPV-positive and HPV-negative oral epithelial dysplasias and head and neck cancers

The DNA cytosine deaminase APOBEC3B (A3B) is a newly recognized endogenous source of mutations in a range of human tumors, including head/neck cancer. A3B inflicts C-to-T and C-to-G base substitutions in 5'-TCA/T trinucleotide motifs, contributes to accelerated rates of tumor development, and affects clinical outcomes in a variety of cancer types. High-risk human papillomavirus (HPV) infection causes A3B overexpression, and HPV-positive cervical and head/neck cancers are among tumor types with the highest degree of APOBEC signature mutations. A3B overexpression in HPV-positive tumor types is caused by the viral E6/E7 oncoproteins and may be an early off-to-on switch in tumorigenesis. In comparison, less is known about the molecular mechanisms responsible for A3B overexpression in HPV-negative head/neck cancers. Here, we utilize an immunohistochemical approach to determine whether A3B is turned from off-to-on or if it undergoes a more gradual transition to overexpression in HPV-negative head/neck cancers. As positive controls, almost all HPV-positive oral epithelial dysplasias and oropharyngeal cancers showed high levels of nuclear A3B staining regardless of diagnosis. As negative controls, A3B levels were low in phenotypically normal epithelium adjacent to cancer and oral epithelial hyperplasias. Interestingly, HPV-negative and low-grade oral epithelial dysplasias showed intermediate A3B levels, while high-grade oral dysplasias showed high A3B levels similar to oral squamous cell carcinomas. A3B levels were highest in grade 2 and grade 3 oral squamous cell carcinomas. In addition, a strong positive association was found between nuclear A3B and Ki67 scores suggesting a linkage to the cell cycle. Overall, these results support a model in which gradual activation of A3B expression occurs during HPV-negative tumor development and suggest that A3B overexpression may provide a marker for advanced grade oral dysplasia and cancer.

Combining Genetic Mutation and Expression Profiles Identifies Novel Prognostic Biomarkers of Lung Adenocarcinoma

Motivation:

Although several prognostic signatures for lung adenocarcinoma (LUAD) have been developed, they are mainly based on a single-omics data set. This article aims to develop a novel set of prognostic signatures by combining genetic mutation and expression profiles of LUAD patients.

Methods:

The genetic mutation and expression profiles, together with the clinical profiles of a cohort of LUAD patients from The Cancer Genome Atlas (TCGA), were downloaded. Patients were separated into 2 groups, namely, the high-risk and low-risk groups, according to their overall survivals. Then, differential analysis was performed to determine differentially expressed genes (DEGs) and mutated genes (DMGs) in the expression and mutation profiles, respectively, between the 2 groups. Finally, a prognostic model based on the support vector machine (SVM) algorithm was developed by combining the expression values of the DEGs and the mutation times of the DMGs.

Results:

A total of 13 DEGs and 7 DMGs were recognized between the 2 groups. Their prognostic values were validated using independent cohorts. Compared with several existing signatures, the proposed prognostic signatures exhibited better prediction performance in the testing set. In addition, it is found that 1 of the 7 DMGs, GRIN2B, is mutated much more frequently in the high-risk group, showing a potential value as a therapy target.

Conclusions:

Combining multi-omics data sets is an applicable manner to identify novel prognostic signatures and to improve the prognostic prediction for LUAD, which will be heuristic to other types of cancers.

Identification of prognostic biomarkers for major subtypes of non-small-cell lung cancer using genomic and clinical data

PURPOSE

Intra-tumor heterogeneity and high mortality among patients with non-small-cell lung carcinoma (NSCLC) emphasize the need to identify reliable prognostic markers unique to each subtype.

METHODS

In this study, univariate cox regression and prognostic index (PI)-based approaches were used to develop models for predicting NSCLC patients' subtype-specific survival.

RESULTS

Prognostic analysis of TCGA dataset identified 1334 and 2129 survival-specific genes for LUSC (488 samples) and LUAD (497 samples), respectively. Individually, 32 and 271 prognostic genes were found and validated in GSE study exclusively for LUSC and LUAD. Nearly, 9-10% of the validated genes in each subtype were already reported in multiple studies thus highlighting their importance as prognostic biomarkers. Strong literature evidence against these prognostic genes like "ELANE" (LUSC) and "AHSG" (LUAD) instigates further investigation for their therapeutic and diagnostic roles in the corresponding cohorts. Prognostic models built on five and four genes were validated for LUSC [HR = 2.10, p value = 1.86 × 10^-5] and LUAD [HR = 2.70, p value = 3.31 × 10^-7], respectively. The model based on the combination of age and tumor stage performed well in both NSCLC subtypes, suggesting that despite having distinctive histological features and treatment paradigms, some clinical features can be good prognostic predictors in both.

CONCLUSION

This study advocates that investigating the survival-specific biomarkers restricted to respective cohorts can advance subtype-specific prognosis, diagnosis, and treatment for NSCLC patients. Prognostic models and markers described for each subtype may provide insight into the heterogeneity of disease etiology and help in the development of new therapeutic approaches for the treatment of NSCLC patients.

Comprehensive Analysis of Candidate Diagnostic and Prognostic Biomarkers Associated with Lung Adenocarcinoma

Background

We aimed to screen and identify central genetic and molecular targets involved in advancement of lung adenocarcinoma (LUAD) and to perform an integrated analysis and clinical validation.

Material/Methods

The GEO2R technique was utilized to assess differentially expressed genes (DEGs) among the gene sets GSE75037, GSE85716, and GSE118370. Subsequently, gene Ontology (GO) analyses and Kyoto Encyclopedia of Genes and Genomes (KEGG) analytical methods were executed to determine related biofunctions and signaling pathways, which were annotated with tools from the Database for Annotation, Visualization and Integrated Discovery (DAVID) resource. Then, a protein-protein interaction (PPI) network complex consisting of all detected DEGs was built with the STRING web interface. Cytohubba and MCODE plug-ins for Cytoscape software and Gene Expression Profiling Interactive Analysis (GEPIA) were employed to identify the hub genes. Finally, the mRNA expression of the identified hub genes was quantitatively validated by The Cancer Genome Atlas (TCGA) database analysis and real-time quantitative polymerase chain reaction (RT-qPCR).

Results

We screened 146 upregulated DEGs and 431 downregulated DEGs with the criteria of |logFC| >1 and P<0.05, and the GO analysis indicated that DEGs were implicated in mitotic nuclear division (biological process, BP), the nucleus (cellular component, CC), and protein binding (molecular function, MF) and were associated with multiple KEGG pathways, such as the p53 signaling pathway in cancer. Then, the top 8 genes that predicted significantly different outcomes in LUAD patients were filtered from the DEGs and selected as hub genes. The TCGA database analysis and RT-qPCR results demonstrated that these genes were differentially expressed with the same trends in LUAD tissues compared with normal tissues.

Conclusions

Overall, we propose that 8 genes (PECAM1, CDK1, MKI67, SPP1, TOP2A, CHEK1, CCNB1, and RRM2) might be novel hub genes strongly associated with the progression and prognosis of LUAD.

Integrated analysis of optical mapping and whole-genome sequencing reveals intratumoral genetic heterogeneity in metastatic lung squamous cell carcinoma

Background

Intratumoral heterogeneity is a crucial factor to the outcome of patients and resistance to therapies, in which structural variants play an indispensable but undiscovered role.

Methods

We performed an integrated analysis of optical mapping and whole-genome sequencing on a primary tumor (PT) and matched metastases including lymph node metastasis (LNM) and tumor thrombus in the pulmonary vein (TPV). Single nucleotide variants, indels and structural variants were analyzed to reveal intratumoral genetic heterogeneity among tumor cells in different sites.

Results

Our results demonstrated there were less nonsynonymous somatic variants shared with PT in LNM than in TPV, while there were more structural variants shared with PT in LNM than in TPV. More private variants and its affected genes associated with tumorigenesis and progression were identified in TPV than in LNM. It should be noticed that optical mapping detected an average of 77.1% (74.5-78.5%) large structural variants (>5,000 bp) not detected by whole-genome sequencing and identified several structural variants private to metastases.

Conclusions

Our study does demonstrate structural variants, especially large structural variants play a crucial role in intratumoral genetic heterogeneity and optical mapping could make up for the deficiency of whole-genome sequencing to identify structural variants.

A nomogram for the prediction of overall survival in patients with stage II and III non-small cell lung cancer using a population-based study

As a malignant tumor with poor prognosis, accurate and effective treatment of non-small cell lung cancer (NSCLC) is crucial. To predict overall survival in patients with stage II and III NSCLC, a nomogram was constructed using data from the Surveillance, Epidemiology and End Results database. Eligible patients with NSCLC with available clinical information diagnosed between January 1, 2010 and November 31, 2015 were selected from the database, and the data were randomly divided into a training set and a validation set. Univariate and multivariate Cox regression analyses were used to identify prognostic factors with a threshold of P<0.05, and a nomogram was constructed. Harrell's concordance indexes and calibration plots were used to verify the predictive power of the model. Risk group stratification by stage was also performed. A total of 15,344 patients with stage II and III NSCLC were included in the study. The 3- and 5-year survival rates were 0.382 and 0.278, respectively. The training and validation sets comprised 10,744 and 4,600 patients, respectively. Age, sex, race, marital status, histology, grade, Tumor-Node-Metastasis T and N stage, surgery type, extent of lymph node dissection, radiation therapy and chemotherapy were identified as prognostic factors for the construction of the nomogram. The nomogram exhibited a clinical predictive ability of 0.719 (95% CI, 0.718–0.719) in the training set and 0.721 (95% CI, 0.720–0.722) in the validation set. The predicted calibration curve was similar to the standard curve. In addition, the nomogram was able to divide the patients into groups according to stage IIA, IIB, IIIA, and IIIB NSCLC. Thus, the nomogram provided predictive results for stage II and III NSCLC patients and accurately determined the 3- and 5-year overall survival of patients.

Identification and validation of key genes associated with non-small-cell lung cancer

Non-small-cell lung cancer (NSCLC) is one of the main causes of death induced by cancer globally. However, the molecular aberrations in NSCLC patients remain unclearly. In the present study, four messenger RNA microarray datasets (GSE18842, GSE40275, GSE43458, and GSE102287) were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) between NSCLC tissues and adjacent lung tissues were obtained from GEO2R and the overlapping DEGs were identified. Moreover, functional and pathway enrichment were performed by Funrich, while the protein-protein interaction (PPI) network construction were obtained from STRING and hub genes were visualized and identified by Cytoscape software. Furthermore, validation, overall survival (OS) and tumor staging analysis of selected hub genes were performed by GEPIA. A total of 367 DEGs (95 upregulated and 272 downregulated) were obtained through gene integration analysis. The PPI network consisted of 94 nodes and 1036 edges in the upregulated DEGs and 272 nodes and 464 edges in the downregulated DEGs, respectively. The PPI network identified 46 upregulated and 27 downregulated hub genes among the DEGs, and six (such as CENPE, NCAPH, MYH11, LRRK2, HSD17B6, and A2M) of that have not been identified to be associated with NSCLC so far. Moreover, the expression differences of the mentioned hub genes were consistent with that in lung adenocarcinoma and lung squamous cell carcinoma in the TCGA database. Further analysis showed that all the six hub genes were associated with tumor staging except MYH11, while only the upregulated DEG CENPE was associated with the worse OS of patients with NSCLC. In conclusion, the current study showed that CENPE, NCAPH, MYH11, LRRK2, HSD17B6, and A2M might be the key genes contributed to tumorigenesis or tumor progression in NSCLC, further functional study is needed to explore the involved mechanisms.

Prognostic Role of Circulating miRNAs in Early-Stage Non-Small Cell Lung Cancer

Non-small cell lung cancer (NSCLC) is the primary cause of cancer-related death worldwide, with a low 5-year survival rate even in fully resected early-stage disease. Novel biomarkers to identify patients at higher risk of relapse are needed. We studied the prognostic value of 84 circulating microRNAs (miRNAs) in 182 patients with resected early-stage NSCLC (99 adenocarcinoma (ADC), 83 squamous cell carcinoma (SCC)) from whom peripheral blood samples were collected pre-surgery. miRNA expression was analyzed in relation to disease-free survival (DFS) and overall survival (OS). In univariable analyses, five miRNAs (miR-26a-5p, miR-126-3p, miR-130b-3p, miR-205-5p, and miR-21-5p) were significantly associated with DFS in SCC, and four (miR-130b-3p, miR-26a-5p, miR-126-3p, and miR-205-5p) remained significantly associated with OS. In ADC, miR-222-3p, miR-22-3p, and mir-93-5p were significantly associated with DFS, miR-22-3p remaining significant for OS. Given the high-dimensionality of the dataset, multivariable models were obtained using a regularized Cox regression including all miRNAs and clinical covariates. After adjustment for disease stage, only miR-126-3p showed an independent prognostic role, with higher values associated with longer DFS in SCC patients. With regard to ADC and OS, no miRNA remained significant in multivariable analysis. Further investigation into the role of miR-126 as a prognostic marker in early-stage NSCLC is warranted.