Classification of Non-Small Cell Lung Cancer Using Significance Analysis of Microarray-Gene Set Reduction Algorithm

Targeting the COMMD4-H2B protein complex in lung cancer

BACKGROUND

Lung cancer is the biggest cause of cancer-related deaths worldwide. Non-small cell lung cancer (NSCLC) accounts for 85-90% of all lung cancers. Identification of novel therapeutic targets are required as drug resistance impairs chemotherapy effectiveness. COMMD4 is a potential NSCLC therapeutic target. The aims of this study were to investigate the COMMD4-H2B binding pose and develop a short H2B peptide that disrupts the COMMD4-H2B interaction and mimics COMMD4 siRNA depletion.

METHODS

Molecular modelling, in vitro binding and site-directed mutagenesis were used to identify the COMMD4-H2B binding pose and develop a H2B peptide to inhibit the COMMD4-H2B interaction. Cell viability, DNA repair and mitotic catastrophe assays were performed to determine whether this peptide can specially kill NSCLC cells.

RESULTS

Based on the COMMD4-H2B binding pose, we have identified a H2B peptide that inhibits COMMD4-H2B by directly binding to COMMD4 on its H2B binding binding site, both in vitro and in vivo. Treatment of NSCLC cell lines with this peptide resulted in increased sensitivity to ionising radiation, increased DNA double-strand breaks and induction of mitotic catastrophe in NSCLC cell lines.

CONCLUSIONS

Our data shows that COMMD4-H2B represents a novel potential NSCLC therapeutic target.

An immune-related nomogram model that predicts the overall survival of patients with lung adenocarcinoma

Background

Lung adenocarcinoma accounts for approximately 40% of all primary lung cancers; however, the mortality rates remain high. Successfully predicting progression and overall (OS) time will provide clinicians with more options to manage this disease.

Methods

We analyzed RNA sequencing data from 510 cases of lung adenocarcinoma from The Cancer Genome Atlas database using CIBERSORT, ImmuCellAI, and ESTIMATE algorithms. Through these data we constructed 6 immune subtypes and then compared the difference of OS, immune infiltration level and gene expression between these immune subtypes. Also, all the subtypes and immune cells infiltration level were used to evaluate the relationship with prognosis and we introduced lasso-cox method to constructe an immune-related prognosis model. Finally we validated this model in another independent cohort.

Results

The C3 immune subtype of lung adenocarcinoma exhibited longer survival, whereas the C1 subtype was associated with a higher mutation rate of MUC17 and FLG genes compared with other subtypes. A multifactorial correlation analysis revealed that immune cell infiltration was closely associated with overall survival. Using data from 510 cases, we constructed a nomogram prediction model composed of clinicopathologic factors and immune signatures. This model produced a C-index of 0.73 and achieved a C-index of 0.844 using a validation set.

Conclusions

Through this study we constructed an immune related prognosis model to instruct lung adenocarcinoma’s OS and validated its value in another independent cohost. These results will be useful in guiding treatment for lung adenocarcinoma based on tumor immune profiles.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12890-022-01902-6.

A Holistic Performance Comparison for Lung Cancer Classification Using Swarm Intelligence Techniques

In the field of bioinformatics, feature selection in classification of cancer is a primary area of research and utilized to select the most informative genes from thousands of genes in the microarray. Microarray data is generally noisy, is highly redundant, and has an extremely asymmetric dimensionality, as the majority of the genes present here are believed to be uninformative. The paper adopts a methodology of classification of high dimensional lung cancer microarray data utilizing feature selection and optimization techniques. The methodology is divided into two stages; firstly, the ranking of each gene is done based on the standard gene selection techniques like Information Gain, Relief–F test, Chi-square statistic, and T-statistic test. As a result, the gathering of top scored genes is assimilated, and a new feature subset is obtained. In the second stage, the new feature subset is further optimized by using swarm intelligence techniques like Grasshopper Optimization (GO), Moth Flame Optimization (MFO), Bacterial Foraging Optimization (BFO), Krill Herd Optimization (KHO), and Artificial Fish Swarm Optimization (AFSO), and finally, an optimized subset is utilized. The selected genes are used for classification, and the classifiers used here are Naïve Bayesian Classifier (NBC), Decision Trees (DT), Support Vector Machines (SVM), and K-Nearest Neighbour (KNN). The best results are shown when Relief-F test is computed with AFSO and classified with Decision Trees classifier for hundred genes, and the highest classification accuracy of 99.10% is obtained.

COMMD1, from the Repair of DNA Double Strand Breaks, to a Novel Anti-Cancer Therapeutic Target

Lung cancer has the highest incidence and mortality among all cancers, with non-small cell lung cancer (NSCLC) accounting for 85-90% of all lung cancers. Here we investigated the function of COMMD1 in the repair of DNA double strand breaks (DSBs) and as a prognostic and therapeutic target in NSCLC. COMMD1 function in DSB repair was investigated using reporter assays in COMMD1-siRNA-depleted cells. The role of COMMD1 in NSCLC was investigated using bioinformatic analysis, qRT-PCR and immunoblotting of control and NSCLC cells, tissue microarrays, cell viability and cell cycle experiments. DNA repair assays demonstrated that COMMD1 is required for the efficient repair of DSBs and reporter assays showed that COMMD1 functions in both non-homologous-end-joining and homologous recombination. Bioinformatic analysis showed that COMMD1 is upregulated in NSCLC, with high levels of COMMD1 associated with poor patient prognosis. COMMD1 mRNA and protein were upregulated across a panel of NSCLC cell lines and siRNA-mediated depletion of COMMD1 decreased cell proliferation and reduced cell viability of NSCLC, with enhanced death after exposure to DNA damaging-agents. Bioinformatic analyses demonstrated that COMMD1 levels positively correlate with the gene ontology DNA repair gene set enrichment signature in NSCLC. Taken together, COMMD1 functions in DSB repair, is a prognostic maker in NSCLC and is potentially a novel anti-cancer therapeutic target for NSCLC.

Lung cancer histology classification from CT images based on radiomics and deep learning models

Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are frequent reported cases of non-small cell lung cancer (NSCLC), responsible for a large fraction of cancer deaths worldwide. In this study, we aim to investigate the potential of NSCLC histology classification into AC and SCC by applying different feature extraction and classification techniques on pre-treatment CT images. The employed image dataset (102 patients) was taken from the publicly available cancer imaging archive collection (TCIA). We investigated four different families of techniques: (a) radiomics with two classifiers (kNN and SVM), (b) four state-of-the-art convolutional neural networks (CNNs) with transfer learning and fine tuning (Alexnet, ResNet101, Inceptionv3 and InceptionResnetv2), (c) a CNN combined with a long short-term memory (LSTM) network to fuse information about the spatial coherency of tumor's CT slices, and (d) combinatorial models (LSTM + CNN + radiomics). In addition, the CT images were independently evaluated by two expert radiologists. Our results showed that the best CNN was Inception (accuracy = 0.67, auc = 0.74). LSTM + Inception yielded superior performance than all other methods (accuracy = 0.74, auc = 0.78). Moreover, LSTM + Inception outperformed experts by 7-25% (p < 0.05). The proposed methodology does not require detailed segmentation of the tumor region and it may be used in conjunction with radiological findings to improve clinical decision-making. Lung cancer histology classification from CT images based on CNN + LSTM.

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

Background

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

Methods

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

Results

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

Conclusions

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

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.

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.

Incorporating Pathway Information into Feature Selection towards Better Performed Gene Signatures

To analyze gene expression data with sophisticated grouping structures and to extract hidden patterns from such data, feature selection is of critical importance. It is well known that genes do not function in isolation but rather work together within various metabolic, regulatory, and signaling pathways. If the biological knowledge contained within these pathways is taken into account, the resulting method is a pathway-based algorithm. Studies have demonstrated that a pathway-based method usually outperforms its gene-based counterpart in which no biological knowledge is considered. In this article, a pathway-based feature selection is firstly divided into three major categories, namely, pathway-level selection, bilevel selection, and pathway-guided gene selection. With bilevel selection methods being regarded as a special case of pathway-guided gene selection process, we discuss pathway-guided gene selection methods in detail and the importance of penalization in such methods. Last, we point out the potential utilizations of pathway-guided gene selection in one active research avenue, namely, to analyze longitudinal gene expression data. We believe this article provides valuable insights for computational biologists and biostatisticians so that they can make biology more computable.

A longitudinal feature selection method identifies relevant genes to distinguish complicated injury and uncomplicated injury over time

BACKGROUND

Feature selection and gene set analysis are of increasing interest in the field of bioinformatics. While these two approaches have been developed for different purposes, we describe how some gene set analysis methods can be utilized to conduct feature selection.

METHODS

We adopted a gene set analysis method, the significance analysis of microarray gene set reduction (SAMGSR) algorithm, to carry out feature selection for longitudinal gene expression data.

RESULTS

Using a real-world application and simulated data, it is demonstrated that the proposed SAMGSR extension outperforms other relevant methods. In this study, we illustrate that a gene's expression profiles over time can be regarded as a gene set and then a suitable gene set analysis method can be utilized directly to select relevant genes associated with the phenotype of interest over time.

CONCLUSIONS

We believe this work will motivate more research to bridge feature selection and gene set analysis, with the development of novel algorithms capable of carrying out feature selection for longitudinal gene expression data.

GFPT2-Expressing Cancer-Associated Fibroblasts Mediate Metabolic Reprogramming in Human Lung Adenocarcinoma

Metabolic reprogramming of the tumor microenvironment is recognized as a cancer hallmark. To identify new molecular processes associated with tumor metabolism, we analyzed the transcriptome of bulk and flow-sorted human primary non-small cell lung cancer (NSCLC) together with ¹⁸FDG-PET scans, which provide a clinical measure of glucose uptake. Tumors with higher glucose uptake were functionally enriched for molecular processes associated with invasion in adenocarcinoma and cell growth in squamous cell carcinoma (SCC). Next, we identified genes correlated to glucose uptake that were predominately overexpressed in a single cell-type comprising the tumor microenvironment. For SCC, most of these genes were expressed by malignant cells, whereas in adenocarcinoma, they were predominately expressed by stromal cells, particularly cancer-associated fibroblasts (CAF). Among these adenocarcinoma genes correlated to glucose uptake, we focused on glutamine-fructose-6-phosphate transaminase 2 (GFPT2), which codes for the glutamine-fructose-6-phosphate aminotransferase 2 (GFAT2), a rate-limiting enzyme of the hexosamine biosynthesis pathway (HBP), which is responsible for glycosylation. GFPT2 was predictive of glucose uptake independent of GLUT1, the primary glucose transporter, and was prognostically significant at both gene and protein level. We confirmed that normal fibroblasts transformed to CAF-like cells, following TGFβ treatment, upregulated HBP genes, including GFPT2, with less change in genes driving glycolysis, pentose phosphate pathway, and TCA cycle. Our work provides new evidence of histology-specific tumor stromal properties associated with glucose uptake in NSCLC and identifies GFPT2 as a critical regulator of tumor metabolic reprogramming in adenocarcinoma.Significance: These findings implicate the hexosamine biosynthesis pathway as a potential new therapeutic target in lung adenocarcinoma. Cancer Res; 78(13); 3445-57. ©2018 AACR.

Corrigendum to "Classification of Non-Small Cell Lung Cancer Using Significance Analysis of Microarray-Gene Set Reduction Algorithm"

[This corrects the article DOI: 10.1155/2016/2491671.].

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

Non-small cell lung cancer (NSCLC) is a leading cause of cancer-associated mortality worldwide. Adenocarcinoma (AC) and squamous cell carcinoma (SCC) are two primary histological subtypes of NSCLC, accounting for ~70% of lung cancer cases. Increasing evidence suggests that AC and SCC differ in the composition of genes and molecular characteristics. Previous research has focused on distinguishing AC from SCC or predicting the NSCLC patient survival rates using gene expression profiles, usually with the aid of a feature selection method. The present study conducted a pre-filtering to identify the genes that have significant expression values and a high connection with other genes in the gene network, and then used the radial coordinate visualization method to identify relevant genes. By applying the proposed procedure to NSCLC data, it was demonstrated that there is a clear segmentation between AC and SCC, however not between patients with a good prognosis and bad prognosis. The focus of discriminating AC and SCC differs from survival prediction and there are almost no overlaps between the two gene signatures. Overall, a supervised learning method is preferred and future studies aiming to identify prognostic gene signatures with an increased prediction efficiency are required.