Prediction of distant recurrence in resected stage I and II lung adenocarcinoma

A model for prediction of recurrence of non-small cell lung cancer based on clinical data and CT imaging characteristics

OBJECTIVES

To establish a model for prediction of recurrence of non-small cell lung cancer (NSCLC) based on clinical data and computed tomography (CT) imaging characteristics.

METHODS

A total of 695 patients with surgically resected NSCLC confirmed by pathology at three centers were retrospectively investigated. 626 patients from center 1 were randomly divided into two sets in a ratio of 7:3 (training set, n = 438; testing set, n = 188), 69 patients from center 2 and 3 were assigned in the external validation set. Univariate and binary logistic regression analyses of clinical and CT imaging features determined the independent risk factors used to construct the model. The receiver-operating characteristic curve nomogram and decision curves analysis were used to evaluate the predictive ability of the model.

RESULTS

The mean patient age was 63.3 ± 10.1 years, and 44.7 % (311/695) were male. The univariate and binary logistic regression analyses identified four independent risk factors (age, tumor markers, consolidation/tumor ratio, and pleural effusion), which were used to construct the prediction model. In the training set, the model had an area under the curve of 0.857, an accuracy of 71.7 %, a sensitivity of 88.1 %, and a specificity of 70.0 %; in the testing set, the respective values were 0.867, 75.5 %, 94.4 %, and 73.5 %; in the external validation set, the respective values were 0.852, 79.7 %, 83.3 %, 78.9 %.

CONCLUSION

A prediction model based on clinical data and CT imaging characteristics showed excellent efficiency in prediction of recurrence of NSCLC. Clinical use of this model could be useful for selection of appropriate treatment options.

The molecular features of lung cancer stem cells in dedifferentiation process-driven epigenetic alterations

Cancer stem cells (CSCs) may be dedifferentiated somatic cells following oncogenic processes, representing a subpopulation of cells able to promote tumor growth with their capacities for proliferation and self-renewal, inducing lineage heterogeneity, which may be a main cause of resistance to therapies. It has been shown that the "less differentiated process" may have an impact on tumor plasticity, particularly when non-CSCs may dedifferentiate and become CSC-like. Bidirectional interconversion between CSCs and non-CSCs has been reported in other solid tumors, where the inflammatory stroma promotes cell reprogramming by enhancing Wnt signaling through nuclear factor kappa B activation in association with intracellular signaling, which may induce cells' pluripotency, the oncogenic transformation can be considered another important aspect in the acquisition of "new" development programs with oncogenic features. During cell reprogramming, mutations represent an initial step toward dedifferentiation, in which tumor cells switch from a partially or terminally differentiated stage to a less differentiated stage that is mainly manifested by re-entry into the cell cycle, acquisition of a stem cell-like phenotype, and expression of stem cell markers. This phenomenon typically shows up as a change in the form, function, and pattern of gene and protein expression, and more specifically, in CSCs. This review would highlight the main epigenetic alterations, major signaling pathways and driver mutations in which CSCs, in tumors and specifically, in lung cancer, could be involved, acting as key elements in the differentiation/dedifferentiation process. This would highlight the main molecular mechanisms which need to be considered for more tailored therapies.

Stephania tetrandra and Its Active Compound Coclaurine Sensitize NSCLC Cells to Cisplatin through EFHD2 Inhibition

BACKGROUND

Adjuvant chemotherapy, particularly cisplatin, is recommended for non-small cell lung carcinoma (NSCLC) patients at high risk of recurrence. EF-hand domain-containing protein D2 (EFHD2) has been recently shown to increase cisplatin resistance and is significantly associated with recurrence in early-stage NSCLC patients. Natural products, commonly used as phytonutrients, are also recognized for their potential as pharmaceutical anticancer agents.

RESULT

In this study, a range of Chinese herbs known for their antitumor or chemotherapy-enhancing properties were evaluated for their ability to inhibit EFHD2 expression in NSCLC cells. Among the herbs tested, Stephania tetrandra (S. tetrandra) exhibited the highest efficacy in inhibiting EFHD2 and sensitizing cells to cisplatin. Through LC-MS identification and functional assays, coclaurine was identified as a key molecule in S. tetrandra responsible for EFHD2 inhibition. Coclaurine not only downregulated EFHD2-related NOX4-ABCC1 signaling and enhanced cisplatin sensitivity, but also suppressed the stemness and metastatic properties of NSCLC cells. Mechanistically, coclaurine disrupted the interaction between the transcription factor FOXG1 and the EFHD2 promoter, leading to a reduction in EFHD2 transcription. Silencing FOXG1 further inhibited EFHD2 expression and sensitized NSCLC cells to cisplatin.

CONCLUSIONS

S. tetrandra and its active compound coclaurine may serve as effective adjuvant therapies to improve cisplatin efficacy in the treatment of NSCLC.

Evaluation of pre-diagnostic blood protein measurements for predicting survival after lung cancer diagnosis

BACKGROUND

To evaluate whether circulating proteins are associated with survival after lung cancer diagnosis, and whether they can improve prediction of prognosis.

METHODS

We measured up to 1159 proteins in blood samples from 708 participants in 6 cohorts. Samples were collected within 3 years prior to lung cancer diagnosis. We used Cox proportional hazards models to identify proteins associated with overall mortality after lung cancer diagnosis. To evaluate model performance, we used a round-robin approach in which models were fit in 5 cohorts and evaluated in the 6th cohort. Specifically, we fit a model including 5 proteins and clinical parameters and compared its performance with clinical parameters only.

FINDINGS

There were 86 proteins nominally associated with mortality (p < 0.05), but only CDCP1 remained statistically significant after accounting for multiple testing (hazard ratio per standard deviation: 1.19, 95% CI: 1.10-1.30, unadjusted p = 0.00004). The external C-index for the protein-based model was 0.63 (95% CI: 0.61-0.66), compared with 0.62 (95% CI: 0.59-0.64) for the model with clinical parameters only. Inclusion of proteins did not provide a statistically significant improvement in discrimination (C-index difference: 0.015, 95% CI: -0.003 to 0.035).

INTERPRETATION

Blood proteins measured within 3 years prior to lung cancer diagnosis were not strongly associated with lung cancer survival, nor did they importantly improve prediction of prognosis beyond clinical information.

FUNDING

No explicit funding for this study. Authors and data collection supported by the US National Cancer Institute (U19CA203654), INCA (France, 2019-1-TABAC-01), Cancer Research Foundation of Northern Sweden (AMP19-962), and Swedish Department of Health Ministry.

The lung microbiome, peripheral gene expression, and recurrence-free survival after resection of stage II non-small cell lung cancer

BACKGROUND

Cancer recurrence after tumor resection in early-stage non-small cell lung cancer (NSCLC) is common, yet difficult to predict. The lung microbiota and systemic immunity may be important modulators of risk for lung cancer recurrence, yet biomarkers from the lung microbiome and peripheral immune environment are understudied. Such markers may hold promise for prediction as well as improved etiologic understanding of lung cancer recurrence.

METHODS

In tumor and distant normal lung samples from 46 stage II NSCLC patients with curative resection (39 tumor samples, 41 normal lung samples), we conducted 16S rRNA gene sequencing. We also measured peripheral blood immune gene expression with nanoString®. We examined associations of lung microbiota and peripheral gene expression with recurrence-free survival (RFS) and disease-free survival (DFS) using 500 × 10-fold cross-validated elastic-net penalized Cox regression, and examined predictive accuracy using time-dependent receiver operating characteristic (ROC) curves.

RESULTS

Over a median of 4.8 years of follow-up (range 0.2-12.2 years), 43% of patients experienced a recurrence, and 50% died. In normal lung tissue, a higher abundance of classes Bacteroidia and Clostridia, and orders Bacteroidales and Clostridiales, were associated with worse RFS, while a higher abundance of classes Alphaproteobacteria and Betaproteobacteria, and orders Burkholderiales and Neisseriales, were associated with better RFS. In tumor tissue, a higher abundance of orders Actinomycetales and Pseudomonadales were associated with worse DFS. Among these taxa, normal lung Clostridiales and Bacteroidales were also related to worse survival in a previous small pilot study and an additional independent validation cohort. In peripheral blood, higher expression of genes TAP1, TAPBP, CSF2RB, and IFITM2 were associated with better DFS. Analysis of ROC curves revealed that lung microbiome and peripheral gene expression biomarkers provided significant additional recurrence risk discrimination over standard demographic and clinical covariates, with microbiome biomarkers contributing more to short-term (1-year) prediction and gene biomarkers contributing to longer-term (2-5-year) prediction.

CONCLUSIONS

We identified compelling biomarkers in under-explored data types, the lung microbiome, and peripheral blood gene expression, which may improve risk prediction of recurrence in early-stage NSCLC patients. These findings will require validation in a larger cohort.

Use of deep learning to predict postoperative recurrence of lung adenocarcinoma from preoperative CT

PURPOSE

Although surgery is the primary treatment for lung cancer, some patients experience recurrence at a certain rate. If postoperative recurrence can be predicted early before treatment is initiated, it may be possible to provide individualized treatment for patients. Thus, in this study, we propose a computer-aided diagnosis (CAD) system that predicts postoperative recurrence from computed tomography (CT) images acquired before surgery in patients with lung adenocarcinoma using a deep convolutional neural network (DCNN).

METHODS

This retrospective study included 150 patients who underwent curative surgery for primary lung adenocarcinoma. To create original images, the tumor part was cropped from the preoperative contrast-enhanced CT images. The number of input images to the DCNN was increased to 3000 using data augmentation. We constructed a CAD system by transfer learning using a pretrained VGG19 model. Tenfold cross-validation was performed five times. Cases with an average identification rate of 0.5 or higher were determined to be a recurrence.

RESULTS

The median duration of follow-up was 73.2 months. The results of the performance evaluation showed that the sensitivity, specificity, and accuracy of the proposed method were 0.75, 0.87, and 0.82, respectively. The area under the receiver operating characteristic curve was 0.86.

CONCLUSION

We demonstrated the usefulness of DCNN in predicting postoperative recurrence of lung adenocarcinoma using preoperative CT images. Because our proposed method uses only CT images, we believe that it has the advantage of being able to assess postoperative recurrence on an individual patient basis, both preoperatively and noninvasively.

Dysregulated Immune and Metabolic Microenvironment Is Associated with the Post-Operative Relapse in Stage I Non-Small Cell Lung Cancer

Simple Summary

The underlying mechanism of post-operative relapse of non-small cell lung cancer (NSCLC) remained poorly understood. This study highlights that both tumors and adjacent tissues from stage I NSCLC with relapse show a dysregulated immune and metabolic environment. Immune response shifts from an active state in primary tumors to a suppressive state in recurrent tumors. A model based on the enriched biological features in the primary tumors with relapse could effectively predict recurrence for stage I NSCLC. These results provide insights into the underpinning of the post-operative relapse and suggest that identifying NSCLC patients with a high risk of relapse could help the clinical decision of applying appropriate therapeutic interventions.

Abstract

The underlying mechanism of post-operative relapse of non-small cell lung cancer (NSCLC) remains poorly understood. We enrolled 57 stage I NSCLC patients with or without relapse and performed whole-exome sequencing (WES) and RNA sequencing (RNA-seq) on available primary and recurrent tumors, as well as on matched tumor-adjacent tissues (TATs). The WES analysis revealed that primary tumors from patients with relapse were enriched with USH2A mutation and 2q31.1 amplification. RNA-seq data showed that the relapse risk was associated with aberrant immune response and metabolism in the microenvironment of primary lesions. TATs from the patients with relapse showed an immunosuppression state. Moreover, recurrent lesions exhibited downregulated immune response compared with their paired primary tumors. Genomic and transcriptomic features were further subjected to build a prediction model classifying patients into groups with different relapse risks. We show that the recurrence risk of stage I NSCLC could be ascribed to the altered immune and metabolic microenvironment. TATs might be affected by cancer cells and facilitate the invasion of tumors. The immune microenvironment in the recurrent lesions is suppressed. Patients with a high risk of relapse need active post-operative intervention.

Cancer Stem Cells (CSCs), Circulating Tumor Cells (CTCs) and Their Interplay with Cancer Associated Fibroblasts (CAFs): A New World of Targets and Treatments

Simple Summary

The world of small molecules in solid tumors as cancer stem cells (CSCs), circulating tumor cells (CTCs) and cancer-associated fibroblasts (CAFs) continues to be under-debated, but not of minor interest in recent decades. One of the main problems in regard to cancer is the development of tumor recurrence, even in the early stages, in addition to drug resistance and, consequently, ineffective or an incomplete response against the tumor. The findings behind this resistance are probably justified by the presence of small molecules such as CSCs, CTCs and CAFs connected with the tumor microenvironment, which may influence the aggressiveness and the metastatic process. The mechanisms, connections, and molecular pathways behind them are still unknown. Our review would like to represent an important step forward to highlight the roles of these molecules and the possible connections among them.

Abstract

The importance of defining new molecules to fight cancer is of significant interest to the scientific community. In particular, it has been shown that cancer stem cells (CSCs) are a small subpopulation of cells within tumors with capabilities of self-renewal, differentiation, and tumorigenicity; on the other side, circulating tumor cells (CTCs) seem to split away from the primary tumor and appear in the circulatory system as singular units or clusters. It is becoming more and more important to discover new biomarkers related to these populations of cells in combination to define the network among them and the tumor microenvironment. In particular, cancer-associated fibroblasts (CAFs) are a key component of the tumor microenvironment with different functions, including matrix deposition and remodeling, extensive reciprocal signaling interactions with cancer cells and crosstalk with immunity. The settings of new markers and the definition of the molecular connections may present new avenues, not only for fighting cancer but also for the definition of more tailored therapies.

Grey wolf assisted dragonfly-based weighted rule generation for predicting heart disease and breast cancer

Disease prediction plays a significant role in the life of people, as predicting the threat of diseases is necessary for citizens to live life in a healthy manner. The current development of data mining schemes has offered several systems that concern on disease prediction. Even though the disease prediction system includes more advantages, there are still many challenges that might limit its realistic use, such as the efficiency of prediction and information protection. This paper intends to develop an improved disease prediction model, which includes three phases: Weighted Coalesce rule generation, Optimized feature extraction, and Classification. At first, Coalesce rule generation is carried out after data transformation that involves normalization and sequential labeling. Here, rule generation is done based on the weights (priority level) assigned for each attribute by the expert. The support of each rule is multiplied with the proposed weighted function, and the resultant weighted support is compared with the minimum support for selecting the rules. Further, the obtained rule is subject to the optimal feature selection process. The hybrid classifiers that merge Support Vector Machine (SVM), and Deep Belief Network (DBN) takes the role of classification, which characterizes whether the patient is affected with the disease or not. In fact, the optimized feature selection process depends on a new hybrid optimization algorithm by linking the Grey Wolf Optimization (GWO) with Dragonfly Algorithm (DA) and hence, the presented model is termed as Grey Wolf Levy Updated-DA (GWU-DA). Here, the heart disease and breast cancer data are taken, where the efficiency of the proposed model is validated by comparing over the state-of-the-art models. From the analysis, the proposed GWU-DA model for accuracy is 65.98 %, 53.61 %, 42.27 %, 35.05 %, 34.02 %, 11.34 %, 13.4 %, 10.31 %, 9.28 % and 9.89 % better than CBA + CPAR, MKL + ANFIS, RF + EA, WCBA, IQR + KNN + PSO, NL-DA + SVM + DBN, AWFS-RA, HCS-RFRS, ADS-SM-DNN and OSSVM-HGSA models at 60th learning percentage.

Potential of the cell-free blood-based biomarker uroplakin 2 RNA to detect recurrence after surgical resection of lung adenocarcinoma

Lung adenocarcinoma (LUAD) is the most common subtype of lung cancer, and ~30% of patients with LUAD develop cancer recurrence after surgery. The present study aimed to identify and validate biomarkers that may be used to monitor recurrence following LUAD surgery. Data from patients with LUAD were downloaded from The Cancer Genome Atlas database and postoperative recurrence samples were selected. Subsequently, weighted gene co-expression network analysis (WGCNA) was subsequently performed to identify key co-expression gene modules. Additionally, enrichment analysis of the key gene modules was performed using the Database for Annotation, Visualization and Integrated Discovery. Furthermore, survival analysis was performed on the most notable biomarker, uroplakin 2 (UPK2), which was downloaded from the Oncomine database, and its effect on prognosis was assessed. WGCNA identified 39 gene modules, of which one was most associated with recurrence. Among them, UPK2, kelch domain containing 3, galanin receptor 2 and tyrosinase-related protein 1 served a central role in the co-expression network and were significantly associated with the survival of patients. A total of 132 blood samples were collected from patients with LUAD with free UPK2 in the plasma. The expression levels of UPK2 relative to GADPH were 0.1623 and 0.2763 in non-relapsed and relapsed patients, respectively. Receiver operating characteristic curve analysis was used to detect free UPK2 mRNA in the blood in order to monitor postoperative recurrence, resulting in an area under the curve of 0.767 and a 95% CI of 0.675-0.858. Patients with high free UPK2 mRNA expression had unfavorable survival outcomes compared with those with low UPK2 expression. Therefore, free UPK2 mRNA expression in the plasma may have the potential to act as an indicator of postoperative recurrence in patients with early stage LUAD.

Eight-gene signature predicts recurrence in lung adenocarcinoma

BACKGROUND

Recurrence significantly influences the survival in patients with lung adenocarcinoma (LUAD). However, there are less gene signatures that predict recurrence risk of LUAD.

OBJECTIVE

We performed this study to construct a model to predict risk of recurrence in LUAD.

METHODS

RNA-seq data from 426 patients with LUAD were downloaded from The Cancer Genome Atlas (TCGA) and were randomly assigned into the training (n= 213) and validation set (n= 213). Differentially expressed genes (DEGs) between recurrent and non-recurrent tumors in the training set were identified. Recurrence-associated DEGs were selected using multivariate Cox regression analysis. The recurrence risk model that identifies patients at low and high risk for recurrence was constructed, followed by the validation of its performance in the validation set and a microarray dataset.

RESULTS

In total, 378 DEGs, including 20 recurrence-associated DEGs, were identified between the recurrent and non-recurrent tumors in the training set. The signatures of 8 genes (including AZGP1, INPP5J, MYBPH, SPIB, GUCA2A, HTR1B, SLC15A1 and TNFSF11) were used to construct the prognostic model to assess the risk of recurrence. This model indicated that patients with high risk scores had shorter recurrence-free survival time compared with patients with low risk scores. ROC curve analysis of this model showed it had high predictive accuracy (AUC > 0.8) to predict LUAD recurrence in the TCGA cohort (the training and validation sets) and GSE50081 dataset. This prognostic model showed high predictive power and performance in predicting recurrence in LUAD.

CONCLUSION

We concluded that this model might be of great value for evaluating the risk of recurrence of LUAD in clinics.

New Perspectives in Different Gene Expression Profiles for Early and Locally Advanced Non-Small Cell Lung Cancer Stem Cells

INTRODUCTION

Lung cancer is one of the most common cancers in the world, causing over 1.7 million deaths in 2018. Thus far, no effective treatments against lung cancer for advanced stages have been found. For early stages, although surgery is considered the gold standard treatment, 30-55% of patients develop recurrence within the first 5 years of surgery. Our aim is to assess whether cancer stem cells (CSC) display overexpression of a pool of genes that were previously identified for adenocarcinoma recurrence in patients with early and locally advanced stages of non-small cell lung cancer (NSCLC).

METHODS

This cross-sectional study was carried out by harvesting surgical tumor specimens obtained from patients harboring early (I-II) and locally advanced (IIIA) stages of NSCLC. For each patient, cell sorting was performed to identify and isolate the ALDHhigh (CSC) and ALDHlow (cancer cells) populations. The mRNA expressions of 31 recurrence-related genes (target genes) in both ALDHhigh and ALDHlow populations were then assessed and compared.

RESULTS

Surgical specimens were obtained from 22 patients harboring NSCLC. Sixteen (51.6%) out of 31 recurrence-related genes were significantly overexpressed in ALDHhigh cells in the early stages and 9 (29.0%) were overexpressed in the locally advanced stages of NSCLC. Overall, the relative mRNA expressions for these recurrence-related genes were higher in early-stage patients. The average fold change, considering all 31 recurrence-related genes together, was 4.5 (95% CI = 3.1-6.3) in early-stage patients and 1.6 (95% CI = 1.2-2.2) in locally advanced-stage patients.

CONCLUSIONS

Our study represents the first attempt toward identifying genes associated with recurrence that are overexpressed in cancer stem cells in patients with early and locally advanced stages of NSCLC. This finding may contribute to the identification of new target therapies tailored for NSCLC stages.

Artificial intelligence prediction model for overall survival of clear cell renal cell carcinoma based on a 21-gene molecular prognostic score system

We developed and validated a new prognostic model for predicting the overall survival in clear cell renal cell carcinoma (ccRCC) patients. In this study, artificial intelligence (AI) algorithms including random forest and neural network were trained to build a molecular prognostic score (mPS) system. Afterwards, we investigated the potential mechanisms underlying mPS by assessing gene set enrichment analysis, mutations, copy number variations (CNVs) and immune cell infiltration. A total of 275 prognosis-related genes were identified, which were also differentially expressed between ccRCC patients and healthy controls. We then constructed a universal mPS system that depends on the expression status of only 21 of these genes by applying AI-based algorithms. Then, the mPS were validated by another independent cohort and demonstrated to be applicable to ccRCC subsets. Furthermore, a nomogram comprising the mPS score and several independent variables was established and proved to effectively predict ccRCC patient prognosis. Finally, significant differences were identified regarding the pathways, mutated genes, CNVs and tumor-infiltrating immune cells among the subgroups of ccRCC stratified by the mPS system. The AI-based mPS system can provide critical prognostic prediction for ccRCC patients and may be useful to inform treatment and surveillance decisions before initial intervention.

A novel lncRNA, loc107985872, promotes lung adenocarcinoma progression via the notch1 signaling pathway with exposure to traffic-originated PM2.5 organic extract

PM2.5 pollution is an important and urgent problem in China that can increase mortality and hospital admissions. Traffic-originated PM2.5 organic component (tPo) mainly contains polycyclic aromatic hydrocarbons (PAHs). Research has shown that PAHs can promote invasion, metastasis, and cancer stem cell properties in lung adenocarcinoma cells, but the exact toxicological mechanism is unknown. In the present study, we investigated the effect of lncRNAs on the progression of lung adenocarcinoma induced by tPo and the underlying mechanisms mediated by lncRNA-signaling pathway interactions. We found that chronic tPo treatment upregulated the expression of loc107985872, which further promoted cell invasion and migration, EMT and cancer stem cell properties via notch1 pathway in lung adenocarcinoma cells. Meanwhile, activation of the notch1 signaling pathway through loc107985872 might be associated with abnormally high expression of its upstream proteins, such as ADAM17, PSEN1 and DLL1. Moreover, tPo exposure induced EMT and the acquisition of cancer stem cell-like properties via the notch1 signaling pathway in vivo. In summary, loc107985872 upregulated by tPo promoted lung adenocarcinoma progression via the notch1 signaling pathway.

Weight optimized neural network for heart disease prediction using hybrid lion plus particle swarm algorithm

Heart disease remains one of the significantcauses ofmortality and morbidity amongst the world's population. Predicting heart disease is considered as one of the vital issues in clinical data analysis. Since the number of data is rising gradually, it is muchcomplicatedforanalyzing and processing, and especially, it becomes difficult to maintain the e-healthcare data. Moreover, the prediction model under machine learning seems to be anessentialfacet in this research area. In this scenario, this paper aims to propose a new heart disease prediction model with the inclusion of specificprocesses like Feature Extraction, Record, Attribute minimization, and Classification. Initially, both statistical and higher-order statistical features are extracted under feature extraction. Subsequently, the record and attribute minimization carried out, where Component Analysis PCA plays its major role in solving the "curse of dimensionality."Finally, the prediction process takes place by the Neural Network (NN) model that intake the dimensionally reduced features. Moreover, the major intention of this paper deals with the accurate prediction. Hence, it is planned to influence the utility of meta-heuristic algorithms for the weight optimization of NN. This paper introduces a new hybrid algorithm termed Particle Swarm Optimization (PSO) merged LA update (PM-LU) algorithm that solves the above-mentioned optimization crisis, which hybrids the concept of Lion Algorithm (LA) and PSO algorithm. Finally, the efficiency of proposed work is compared over other conventional approaches and its superiority is proven with respect to certain performance measures. From the analysis, the presented PM-LU-NN scheme with regards to accuracy is 3.85%, 12.5%, 12.5%, 3.85%, and 7.41% better than LM-NN, WOA-NN, FF-NN, PSO-NN and LA-NN algorithms.

Detection of skin cancer with adaptive fuzzy classifier using improved whale optimization

Skin cancer is considered as a well-known type of cancer globally, and its occurrence has been found to be raised in current days. Researchers state that the disease requires early prediction so that the identification of precise signs will make it simple for the dermatologists and clinicians. This disorder has been established to be unpredictable. Hence, this paper intends to develop an efficient skin cancer detection scheme, which classifies the nature of cancer, whether it is normal, benign or malignant. Accordingly, the skin image which is given as input is segmented using k-means clustering model and the features are extracted from segmented image using Local Vector Pattern (LVP). Moreover, the extracted features are subjected to fuzzy classifier for recognizing the cancer. In addition, the limits of membership functions are optimally selected by improved Whale Optimization Algorithm (WOA). Thus, the proposed scheme is termed as Improved Selection of Encircling and Spiral updating position of WO-based Fuzzy Classifier (ISESW-FC). From the optimized output, the type of skin cancer image can be determined, whether it is normal, benign or malignant. The performance of proposed model is compared over other conventional methods, and its efficiency is proved by means of Type I and Type II measures.

A Grading System for Invasive Pulmonary Adenocarcinoma: A Proposal From the International Association for the Study of Lung Cancer Pathology Committee

INTRODUCTION

A grading system for pulmonary adenocarcinoma has not been established. The International Association for the Study of Lung Cancer pathology panel evaluated a set of histologic criteria associated with prognosis aimed at establishing a grading system for invasive pulmonary adenocarcinoma.

METHODS

A multi-institutional study involving multiple cohorts of invasive pulmonary adenocarcinomas was conducted. A cohort of 284 stage I pulmonary adenocarcinomas was used as a training set to identify histologic features associated with patient outcomes (recurrence-free survival [RFS] and overall survival [OS]). Receiver operating characteristic curve analysis was used to select the best model, which was validated (n = 212) and tested (n = 300, including stage I-III) in independent cohorts. Reproducibility of the model was assessed using kappa statistics.

RESULTS

The best model (area under the receiver operating characteristic curve [AUC] = 0.749 for RFS and 0.787 for OS) was composed of a combination of predominant plus high-grade histologic pattern with a cutoff of 20% for the latter. The model consists of the following: grade 1, lepidic predominant tumor; grade 2, acinar or papillary predominant tumor, both with no or less than 20% of high-grade patterns; and grade 3, any tumor with 20% or more of high-grade patterns (solid, micropapillary, or complex gland). Similar results were seen in the validation (AUC = 0.732 for RFS and 0.787 for OS) and test cohorts (AUC = 0.690 for RFS and 0.743 for OS), confirming the predictive value of the model. Interobserver reproducibility revealed good agreement (k = 0.617).

CONCLUSIONS

A grading system based on the predominant and high-grade patterns is practical and prognostic for invasive pulmonary adenocarcinoma.

EFHD2 contributes to non-small cell lung cancer cisplatin resistance by the activation of NOX4-ROS-ABCC1 axis

Recurrence and metastasis remain the major cause of cancer mortality. Even for early-stage lung cancer, adjuvant chemotherapy yields merely slight increase to patient survival. EF-hand domain-containing protein D2 (EFHD2) has recently been implicated in recurrence of patients with stage I lung adenocarcinoma. In this study, we investigated the correlation between EFHD2 and chemoresistance in non-small cell lung cancer (NSCLC). High expression of EFHD2 was significantly associated with poor overall survival of NSCLC patients with chemotherapy in in silica analysis. Ectopic EFHD2 overexpression increased cisplatin resistance, whereas EFHD2 knockdown improved chemoresponse. Mechanistically, EFHD2 induced the production of NADPH oxidase 4 (NOX4) and in turn the increase of intracellular reactive oxygen species (ROS), consequently activating membrane expression of the ATP-binding cassette subfamily C member 1 (ABCC1) for drug efflux. Non-steroidal anti-inflammatory drug (NSAID) ibuprofen suppressed EFHD2 expression by leading to the proteasomal and lysosomal degradation of EFHD2 through a cyclooxygenase (COX)-independent mechanism. Combining ibuprofen with cisplatin enhanced antitumor responsiveness in a murine xenograft model in comparison with the individual treatment. In conclusion, we demonstrate that EFHD2 promotes chemoresistance through the NOX4-ROS-ABCC1 axis and therefore developing EFHD2-targeting strategies may offer a new avenue to improve adjuvant chemotherapy of lung cancer.

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EFHD2 increases resistance of lung cancer to cisplatin.

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EFHD2 enhances the NOX4-ROS-ABCC1signalingfor cisplatin efflux.

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Ibuprofen suppresses EFHD2 through both proteasomal and lysosomal degradationmechanisms

Identification of an early diagnostic biomarker of lung adenocarcinoma based on co-expression similarity and construction of a diagnostic model

Background

The purpose of this study was to achieve early and accurate diagnosis of lung cancer and long-term monitoring of the therapeutic response.

Methods

We downloaded GSE20189 from GEO database as analysis data. We also downloaded human lung adenocarcinoma RNA-seq transcriptome expression data from the TCGA database as validation data. Finally, the expression of all of the genes underwent z test normalization. We used ANOVA to identify differentially expressed genes specific to each stage, as well as the intersection between them. Two methods, correlation analysis and co-expression network analysis, were used to compare the expression patterns and topological properties of each stage. Using the functional quantification algorithm, we evaluated the functional level of each significantly enriched biological function under different stages. A machine-learning algorithm was used to screen out significant functions as features and to establish an early diagnosis model. Finally, survival analysis was used to verify the correlation between the outcome and the biomarkers that we found.

Results

We screened 12 significant biomarkers that could distinguish lung cancer patients with diverse risks. Patients carrying variations in these 12 genes also presented a poor outcome in terms of survival status compared with patients without variations.

Conclusions

We propose a new molecular-based noninvasive detection method. According to the expression of the stage-specific gene set in the peripheral blood of patients with lung cancer, the difference in the functional level is quantified to realize the early diagnosis and prediction of lung cancer.