Analysis of gene expression profiles of non-small cell lung cancer at different stages reveals significantly altered biological functions and candidate genes

Transcriptome profiling and metabolic pathway analysis towards reliable biomarker discovery in early-stage lung cancer

Earlier diagnosis of lung cancer is crucial for reducing mortality and morbidity in high-risk patients. Liquid biopsy is a critical technique for detecting the cancer earlier and tracking the treatment outcomes. However, noninvasive biomarkers are desperately needed due to the lack of therapeutic sensitivity and early-stage diagnosis. Therefore, we have utilized transcriptomic profiling of early-stage lung cancer patients to discover promising biomarkers and their associated metabolic functions. Initially, PCA highlights the diversity level of gene expression in three stages of lung cancer samples. We have identified two major clusters consisting of highly variant genes among the three stages. Further, a total of 7742, 6611, and 643 genes were identified as DGE for stages I-III respectively. Topological analysis of the protein-protein interaction network resulted in seven candidate biomarkers such as JUN, LYN, PTK2, UBC, HSP90AA1, TP53, and UBB cumulatively for the three stages of lung cancers. Gene enrichment and KEGG pathway analyses aid in the comprehension of pathway mechanisms and regulation of identified hub genes in lung cancer. Importantly, the medial survival rates up to ~ 70 months were identified for hub genes during the Kaplan-Meier survival analysis. Moreover, the hub genes displayed the significance of risk factors during gene expression analysis using TIMER2.0 analysis. Therefore, we have reason that these biomarkers may serve as a prospective targeting candidate with higher treatment efficacy in early-stage lung cancer patients.

Specific feature recognition on group specific networks (SFR-GSN): a biomarker identification model for cancer stages

BACKGROUND AND OBJECTIVE

Accurate identification of cancer stages is challenging due to the complexity and heterogeneity of the disease. Current clinical diagnosis methods primarily rely on phenotypic observations, which may not capture early molecular-level changes accurately.

METHODS

In this study, a novel biomarker recognition method was proposed tailored for cancer stages by considering the change of gene expression relationships. Utilizing the sample-specific information and protein-protein interaction networks, the group specific networks were constructed to address the limited specificity of potential biomarkers. Then, a specific feature recognition method was proposed based on these group specific networks, which employed the random forest algorithm for initial screening followed by a recursive feature elimination process to identify the optimal biomarker subset. During exploring optimal results, a strategy termed the Cost-Benefit Ratio, was devised to facilitate the identification of stage-specific biomarkers.

RESULTS

Comparative experiments were conducted on lung adenocarcinoma and breast cancer datasets to validate the method's efficacy and generalizability. The results showed that the identified biomarkers were highly stage-specific, and the F1 scores for predicting cancer stages were significantly improved. For the lung adenocarcinoma dataset, the F1 score reached 97.68%, and for the breast cancer dataset, it achieved 96.87%. These results significantly surpassed those of three conventional methods in terms of F1 scores. Moreover, from the perspective of biological functions, the biomarkers were proved playing an important role in cancer stage-evolution.

CONCLUSION

The proposed method demonstrated its effectiveness in identifying stage-related biomarkers. By using these biomarkers as features, accurate prediction of cancer stages was achieved. Furthermore, the method exhibited potential for biomarker identification in subtype analyses, offering novel perspectives for cancer prognosis.

Differentially Expressed Genes Involved in Primary Resistance to Immunotherapy in Patients with Advanced-Stage Pulmonary Cancer

In the last few years, nivolumab has become the standard of care for advanced-stage lung cancer patients. Unfortunately, up to 60% of patients do not respond to this treatment. In our study, we identified variations in gene expression related to primary resistance to immunotherapy. Bronchoscopy biopsies were obtained from advanced non-small cell lung cancer (NSCLC) patients previously characterized as responders or non-responders after nivolumab treatment. Ten tumor biopsies (from three responders and seven non-responders) were analyzed by the differential expression of 760 genes using the NanoString nCounter platform. These genes are known to be involved in the response to anti-PD1/PD-L1 therapy. All the patients were treated with nivolumab. Examining the dysregulated expression of 24 genes made it possible to predict the response to nivolumab treatment. Supervised analysis of the gene expression profile (GEP) revealed that responder patients had significantly higher levels of expression of CXCL11, NT5E, KLRK1, CD3G, GZMA, IDO1, LCK, CXCL9, GNLY, ITGAL, HLA-DRB1, CXCR6, IFNG, CD8A, ITK, B2M, HLA-B, and HLA-A than did non-responder patients. In contrast, PNOC, CD19, TP73, ARG1, FCRL2, and PTGER1 genes had significantly lower expression levels than non-responder patients. These findings were validated as predictive biomarkers in an independent series of 201 patients treated with nivolumab (22 hepatocellular carcinomas, 14 non-squamous cell lung carcinomas, 5 head and neck squamous cell carcinomas, 1 ureter/renal pelvis carcinoma, 120 melanomas, 4 bladder carcinomas, 31 renal cell carcinomas, and 4 squamous cell lung carcinomas). ROC curve analysis showed that the expression levels of ITK, NT5E, ITGAL, and CD8A were the best predictors of response to nivolumab. Further, 13/24 genes showed an adverse impact on overall survival (OS) in an independent, large series of patients with NSCLC (2166 cases). In summary, we found a strong association between the global GEP of advanced NSCLC and the response to nivolumab. The classification of NSCLC patients based on GEP enabled us to identify those patients who genuinely benefited from treatment with immune checkpoint inhibitors (ICIs). We also demonstrated that abnormal expression of most of the markers comprising the genomic signature has an adverse influence on OS, making them significant markers for therapeutic decision-making. Additional prospective studies in larger series of patients are required to confirm the clinical utility of these biomarkers.

Characteristics of the microbiome in lung adenocarcinoma tissue from patients in Kunming city of southwestern China

The purpose of this study is to identify the characteristics of microbial communities in the lung cancer tissues from patients in Kunming sity of southwestern China and to compare the microbial differences at different clinical stages of lung cancer to uncover potential microbial biomarkers. In total, 40 tissue samples of primary lung adenocarcinoma were collected and further performed by 16S rRNA gene sequencing. The subjects were grouped according to TNM stages (T and N group), clinical stage, and smoke status, and the microbial differences in each group were compared. Analysis of sequence data to determine beta diversity, the UniFrac distance was calculated by QIIME and visualized by principal coordinate analysis (PCoA) using R (version 2.15.3). Microbiome abundance and diversity between different groups were calculated by t test or Wilcoxon rank sum test and drawn by R. The linear discriminant analysis effect size (LEfSe) method was utilized to compare relative abundances of all bacterial taxa between groups. A total of 951 OTUs were identified in the cancer tissues. No significant difference has been found in the alpha diversity within all the groups. Beta diversity significantly differed in the N, T, and clinical stage groups. By LEfSe analysis, eight differential taxa including Bifidobacterium were identified in the N group. In the T1 and T2 group, the LEfSe result identified five phyla and ten genera. The differential genera were Moraxella, Dolosigranulum, unidentified_Corynebacteriaceae, and Citrobacter in the T2 group and Bifidobacterium, Alistipes, Akkermansia, Blautia, Lactobacillus, as well as Faecalibaculum in the T1 group. Differential bacterial composition and abundance were also observed in the clinical stage group. This study confirmed that by 16S rRNA sequencing, we identified the dominant microbe of lung cancer tissue in different groups. Bifidobacterium may play an essential role in lymph node metastasis and tumor progression, providing a specific potential microbial biomarker for lung adenocarcinoma. PCR products were subject to vertical electrophoresis on 2% agarose gels, and a colloid recovery kit (Qiagen, Valencia, CA) was applied to recover the target bands. Libraries were generated by the TruSeq DNA PCR-Free Sample Preparation Kit (Illumina, San Diego, USA), and the concentrations were quantitated with a Qubit fluorometer. Finally, the qualified libraries were sequenced by NovaSeq6000 (Illumina).

ACT001 inhibits the proliferation of non-small cell lung cancer cells by upregulating NKTR expression

BACKGROUND

Lung cancer, the primary cause of cancer-related deaths worldwide, is diagnosed at an advanced stage and has a poor prognosis. Non-small cell lung cancer (NSCLC) is a major histological type of lung malignancy. This study investigated the effect of ACT001, a novel sesquiterpene lactone derivative, on the proliferation of NSCLC cells and explored the underlying mechanism.

METHODS

The effect of ACT001 on cell proliferation was examined by clone formation and MTT assay. Differentially expressed genes and enrichment pathways were analyzed by RNA-seq. Flow cytometry and cell cycle-related protein expression analysis were performed to study the cell cycle. Phosphorylated AKT was detected to explore the mechanism in natural killer cell triggering receptor (NKTR) KD cells with AKT activator and/or inhibitor. The therapeutic effect of ACT001 in vivo was studied in the xenograft tumor model.

RESULTS

ACT001 inhibited the proliferation and G1/S transition in NSCLC cell lines. By RNA-seq analysis, NKTR may be the target of ACT001. Moreover, knockdown NKTR promoted cell proliferation and reversed the effects of ACT001. In addition, ACT001 inhibited AKT phosphorylation, but NKTR knockdown promoted AKT phosphorylation.

CONCLUSION

Our results suggested NKTR may be the target of ACT001 in NSCLC. ACT001 holds promise as a novel method for the treatment of NSCLC.

Sterculic Acid Alters Adhesion Molecules Expression and Extracellular Matrix Compounds to Regulate Migration of Lung Cancer Cells

Sterculic acid (SA) is a cyclopropenoid fatty acid isolated from Sterculia foetida seeds. This molecule is a well-known inhibitor of SCD1 enzyme, also known as ∆9-desaturase, which main function is related to lipid metabolism. However, recent studies have demonstrated that it also modifies many other pathways and the underlying gene expression. SCD overexpression, or up-regulated activity, has been associated with tumor aggressiveness and poor prognosis in many cancer types. Scd1 down-regulation, with different inhibitors or molecular strategies, reduces tumor cell survival and cell proliferation, as well as the chemoresistance associated with cancer stem cell presence. However, SA effects over cancer cell migration and extracellular matrix or adhesion molecules have not been described in cancer cells up to now. We used different migration assays and qPCR gene expression analysis to evaluate the effects of SA treatment in cancer cells. The results reveal that SA induces tumoral cell death at high doses, but we also observed that lower SA-treatments induce cell adhesion-migration capacity reduction as a result of modifications in the expression of genes related to integrins and extracellular matrix compounds. Overall, the functional and transcriptomic findings suggest that SA could represent a new inhibitor activity of epithelial to mesenchymal transition.

Overcoming the Challenges of High Quality RNA Extraction from Core Needle Biopsy

The use of gene expression profiling (GEP) in cancer management is rising, as GEP can be used for disease classification and diagnosis, tailoring treatment to underlying genetic determinants of pharmacological response, monitoring of therapy response, and prognosis. However, the reliability of GEP heavily depends on the input of RNA in sufficient quantity and quality. This highlights the need for standard procedures to ensure best practices for RNA extraction from often small tumor biopsies with variable tissue handling. We optimized an RNA extraction protocol from fresh-frozen (FF) core needle biopsies (CNB) from breast cancer patients and from formalin-fixed paraffin-embedded (FFPE) tissue when FF CNB did not yield sufficient RNA. Methods to avoid ribonucleases andto homogenize or to deparaffinize tissues and the impact of tissue composition on RNA extraction were studied. Additionally, RNA’s compatibility with the nanoString nCounter^® technology was studied. This technology platform enables GEP using small RNA fragments. After optimization of the protocol, RNA of high quality and sufficient quantity was obtained from FF CNB in 92% of samples. For the remaining 8% of cases, FFPE material prepared by the pathology department was used for RNA extraction. Both resulting RNA end products are compatible with the nanoString nCounter^® technology.

RHBDD1 silencing inhibited cell growth and invasion of non-small cell lung cancer by mediating ZEB1/PI3K/AKT signaling pathway

Rhomboid domain containing 1 (RHBDD1) gene, which was reported to be upregulated in human several cancer, was associated with carcinogenesis. However, the potential biological function of RHBDD1 in non-small cell lung cancer (NSCLC) carcinogenesis remains still not known. In this study, we aimed to investigate the role of RHBDD1 and its underlying molecular mechanism in NSCLC. The gene RHBDD1 expression was detected in NSCLC tissues and matched nontumor adjacent tissues. In vitro experiments, NSCLC cell lines (A549, H1650, H358 and H1299) were performed to investigate the biological function of RHBDD1 and its molecular mechanism. Our findings showed that the mRNA and protein expression levels of RHBDD1 were notably increased in human NSCLC tissues and cell lines, especially in A549 and H1650 cells. Moreover, silencing of RHBDD1 by RNAi notably inhibited NSCLC cell proliferation and increased cell apoptosis. Caspase-3/7 activity was remarkably increased in cells treated with RHBDD1 siRNA. RHBDD1 silencing notably reduced the number of invading cells. Furthermore, our findings showed that silencing of RHBDD1 notably inhibited the mRNA and protein expression levels of ZEB1 in A549 and H1650 cells. The phosphorylation of PI3K and AKT was also remarkably decreased by RHBDD1 silencing. ZEB1/AKT overexpression reversed the effect of RHBDD1 silencing on NSCLC cell growth and invasion. Taken together, our findings indicated that RHBDD1 silencing inhibited cell growth and invasion of non-small cell lung cancer by mediating ZEB1/PI3K/AKT signaling pathway, implying that RHBDD1 was possibly a potential diagnostic and therapeutic target for NSCLC treatment.

Support Vector Machine for Lung Adenocarcinoma Staging Through Variant Pathways

Lung adenocarcinoma (LUAD) is one of the most common malignant tumors. How to effectively diagnose LUAD at an early stage and make an accurate judgement of the occurrence and progression of LUAD are still the focus of current research. Support vector machine (SVM) is one of the most effective methods for diagnosing LUAD of different stages. The study aimed to explore the dynamic change of differentially expressed genes (DEGs) in different stages of LUAD, and to assess the risk of LUAD through DEGs enriched pathways and establish a diagnostic model based on SVM method. Based on TMN stages and gene expression profiles of 517 samples in TCGA-LUAD database, coefficient of variation (CV) combined with one-way analysis of variance (ANOVA) were used to screen out feature genes in different TMN stages after data standardization. Unsupervised clustering analysis was conducted on samples and feature genes. The feature genes were analyzed by Pearson correlation coefficient to construct a co-expression network. Fisher exact test was conducted to verify the most enriched pathways, and the variation of each pathway in different stages was analyzed. SVM networks were trained and ROC curves were drawn based on the predicted results so as to evaluate the predictive effectiveness of the SVM model. Unsupervised hierarchical clustering analysis results showed that almost all the samples in stage III/IV were clustered together, while samples in stage I/II were clustered together. The correlation of feature genes in different stages was different. In addition, with the increase of malignant degree of lung cancer, the average shortest path of the network gradually increased, while the closeness centrality gradually decreased. Finally, four feature pathways that could distinguish different stages of LUAD were obtained and the ability was tested by the SVM model with an accuracy of 91%. Functional level differences were quantified based on the expression of feature genes in lung cancer patients of different stages, so as to help the diagnosis and prediction of lung cancer. The accuracy of our model in differentiating between stage I/II and stage III/IV could reach 91%.

Identification of LBX2 as a novel causal gene of lung adenocarcinoma

Background

Lung adenocarcinoma (LUAD) is the most predominant histological type of lung cancer with a poor prognosis. In this study, we demonstrate that LBX2 regulates cell proliferation, migration and invasion and the potential molecular mechanism in LUAD.

Methods

The Cancer Genome Atlas dataset was accessed to screen for novel genes and immunohistochemistry (IHC) assays were performed to determine the association between LBX2 expression and clinicopathological features of LUAD. 5‐ethynyl‐2′‐deoxyuridine, colony formation and Real Time xCELLigence analysis system were used to evaluate the cell proliferation abilities of LUAD. Wound healing, transwell and Matrigel assays were used to detect cell migration and invasion capacities. Xenograft tumor models were used to assess the oncogenic role of LBX2 in vivo.

Results

We found that LBX2 was hyperexpressed in LUAD and correlated with clinicopathological features and poor prognosis in LUAD patients. Knockdown of LBX2 inhibited cell proliferation, migration and invasion of LUAD, whereas ectopic expression of LBX2 enhanced tumor growth, migration, and invasion. We further found that LBX2 might participate in epithelial‐to‐mesenchymal transition (EMT) progression and influence EMT‐related gene expression.

Conclusions

The current study suggests that LBX2 plays an oncogenic role in LUAD and may participate in tumor proliferation, migration, and invasion through EMT progression.

Key points

LncRNA LBX2-AS1 facilitates abdominal aortic aneurysm through miR-4685-5p/LBX2 feedback loop

Long noncoding RNAs (LncRNAs) are involved in multiple processes of human malignancy, and emerge as crucial molecules in RNA biology. However, the function of lncRNAs has not been well illustrated in abdominal aortic aneurysm (AAA). In this research, the effects of dysregulated ladybird homeobox 2 antisense RNA 1 (LBX2-AS1) or ladybird homeobox 2 (LBX2) on vascular smooth muscle cell (VSMC) biological processes were surveyed via cell counting kit-8 (CCK-8), methyl thiazolyl tetrazolium (MTT), terminal-deoxynucleoitidyl transferase mediated nick end labeling (TUNEL) and caspase-3 activity assays. LBX2-AS1 and LBX2 both possessed pro-apoptosis and anti-proliferation functions in AAA. Mechanically, the regulation role of LBX2-AS1 on miR-4685-5p or that of miR-4685-5p on LBX2 was investigated by quantitative real-time polymerase chain reaction (qRT-PCR). Additionally, the competing endogenous RNA (ceRNA) network was confirmed by luciferase reporter, RNA pull-down, and RNA immunoprecipitation (RIP) assays. LBX2-AS1 sequestered miR-4685-5p to release LBX2 expression via ceRNA mechanism. Further, LBX2 could act as a transcriptional activator of LBX2-AS1. A positive feedback loop was formed by LBX2-AS1, miR-4685-5p and LBX2, deteriorating AAA formation and progression. To sum up, our data suggested that LBX2-AS1, miR-4685-5p and LBX2 constituted a positive feedback loop in promoting AAA development, implying a potential usage of LBX2-AS1/miR-4685-5p/LBX2 axis in AAA management.

Overexpression of LBX2 associated with tumor progression and poor prognosis in colorectal cancer

Colorectal cancer (CRC) is one of the most common carcinomas with high morbidity and mortality worldwide. However, the underlying molecular mechanisms of CRC are unclear. The aim of the present study was to establish the role that overexpression of LBX2 serves in CRC and to investigate the associated biological pathways. The difference in the expression levels of LBX2 between CRC tissues and adjacent normal colorectal tissues was assessed using the Oncomine database and Tumor Immune Estimation Resource. The expression levels of LBX2 and its prognostic significance in CRC were analyzed using a t-test and χ² test using data from The Cancer Genome Atlas database. The Kaplan-Meier method and Cox regression analysis were used to estimate the prognostic value of LBX2 in CRC. Furthermore, the potential mechanisms of LBX2 dysregulation and its underlying molecular mechanisms in CRC were investigated using Gene Set Enrichment Analysis (GSEA). LBX2 expression levels were significantly upregulated in CRC samples compared with corresponding normal colorectal tissues (P<0.05). LBX2 upregulation was correlated with advanced tumor stage (III or IV), vascular invasion, lymphatic invasion and the male sex (all P<0.05). Kaplan-Meier analyses showed that high expression levels of LBX2 were associated with a less favorable overall survival (OS) and disease-free survival (DFS) in CRC (all P<0.05). Multivariate analyses further confirmed that LBX2 upregulation was an independent indicator of less favorable OS and DFS (all P<0.05). In addition, LBX2 DNA hypomethylation and microRNA (miR)-378a-3p downregulation correlated with LBX2 upregulation in CRC (all P<0.05). The downregulation of miR-378a-3p in CRC was also significantly associated with less favorable OS and DFS, as demonstrated using Kaplan-Meier analyses (all P<0.05). Moreover, GSEA indicated that ‘VEGF signaling’, ‘Cell adhesion molecules CAMs’, ‘Toll-like receptor signaling’ and ‘Natural killer cell-mediated cytotoxicity’ signaling pathways were enriched in the high LBX2 expressing cohort (all P<0.05). Thus, overexpression of LBX2 may be associated with the development of CRC and may serve as a novel prognostic marker and therapeutic target in CRC. The mechanisms of LBX2 upregulation in CRC are possibly associated with LBX2 DNA hypomethylation and miR-378a-3p downregulation. The potential mechanisms of LBX2 upregulation in CRC might be regulated via the ‘Cell adhesion molecules CAMs’, ‘Toll-like receptor signaling’ and ‘Natural killer cell-mediated cytotoxicity’ signaling pathways.

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.

Stoichiogenomics reveal oxygen usage bias, key proteins and pathways associated with stomach cancer

Stomach cancer involves hypoxia-specific microenvironments. Stoichiogenomics explores environmental resource limitation on biological macromolecules in terms of element usages. However, the patterns of oxygen usage by proteins and the ways that proteins adapt to a cancer hypoxia microenvironment are still unknown. Here we compared the oxygen and carbon contents ([C]) between proteomes of stomach cancer (hypoxia) and two stomach glandular cells (normal). Key proteins, genome locations, pathways, and functional dissection associated with stomach cancer were also studied. An association of oxygen content ([O]) and protein expression level was revealed in stomach cancer and stomach glandular cells. For differentially expressed proteins (DEPs), oxygen contents in the up regulated proteins were3.2%higherthan that in the down regulated proteins in stomach cancer. A total of 1,062 DEPs were identified; interestingly none of these proteins were coded on Y chromosome. The up regulated proteins were significantly enriched in pathways including regulation of actin cytoskeleton, cardiac muscle contraction, pathway of progesterone-mediated oocyte maturation, etc. Functional dissection of the up regulated proteins with high oxygen contents showed that most of them were cytoskeleton, cytoskeleton associated proteins, cyclins and signaling proteins in cell cycle progression. Element signature of resource limitation could not be detected in stomach cancer for oxygen, just as what happened in plants and microbes. Unsaved use of oxygen by the highly expressed proteins was adapted to the rapid growth and fast division of the stomach cancer cells. In addition, oxygen usage bias, key proteins and pathways identified in this paper laid a foundation for application of stoichiogenomics in precision medicine.