Prediction and Analysis of Skin Cancer Progression using Genomics Profiles of Patients

Abstract The metastatic Skin Cutaneous Melanoma (SKCM) has been associated with diminished survival rates and high mortality rates worldwide. Thus, segregating metastatic melanoma from the primary tumors is crucial to employ an optimal therapeutic strategy for the prolonged survival of patients. The SKCM mRNA, miRNA and methylation data of TCGA is comprehensively analysed to recognize key genomic features that can segregate metastatic and primary tumors. Further, machine learning models have been developed using selected features to distinguish the same. The Support Vector Classification with Weight (SVC-W) model developed using the expression of 17 mRNAs achieved Area under the Receiver Operating Characteristic (AUROC) curve of 0.95 and an accuracy of 89.47% on an independent validation dataset. This study reveals the genes C7, MMP3, KRT14, LOC642587, CASP7, S100A7 and miRNAs hsa-mir-205 and hsa-mir-203b as the key genomic features that may substantially contribute to the oncogenesis of melanoma. Our study also proposes genes ESM1, NFATC3, C7orf4, CDK14, ZNF827, and ZSWIM7 as novel putative markers for cutaneous melanoma metastasis. The major prediction models and analysis modules to predict metastatic and primary tumor samples of SKCM are available from a webserver, CancerSPP (http://webs.iiitd.edu.in/raghava/cancerspp/).

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Prediction and analysis of skin cancer progression using genomics profiles of patients

10.1101/393454 ◽

2018 ◽

Cited By ~ 1

Author(s):

Sherry Bhalla ◽

Harpreet Kaur ◽

Anjali Dhall ◽

Gajendra P. S. Raghava

Keyword(s):

Cancer Progression ◽

Primary Tumor ◽

Prediction Models ◽

Simple Expression ◽

The Cancer Genome Atlas ◽

Machine Learning Techniques ◽

Validation Dataset ◽

Metastatic Tumors ◽

Primary Tumors ◽

Genomic Features

AbstractMetastatic state of the Skin Cutaneous Melanoma (SKCM) has led to high mortality rate worldwide. Previously, various studies have revealed the association of the metastatic melanoma with the diminished survival rate in comparison to primary tumors. Thus, prediction of melanoma at primary tumor state is crucial to employ optimal therapeutic strategy for prolonged survival of patients. The RNA, miRNA and methylation data of The Cancer Genome Atlas (TCGA) cohort of SKCM is comprehensively analysed to recognize key genomic features that can categorize various states of metastatic tumors from primary tumors with high precision. Subsequently, various prediction models were developed using filtered genomic features implementing various machine learning techniques to classify these primary tumors from metastatic tumors. The SVC model (with class weight and RBF kernel) developed using 17 mRNA features achieved maximum MCC 0.73 with sensitivity, specificity and accuracy 89.19%, 90.48% and 89.47% respectively on independent validation dataset. Our study reveals that gene expression based features performs better than features obtained from miRNA profiling and epigenomic profiling. Our analysis shows that the expression of genesC7, MMP3, KRT14, KRT17, MASP1, and miRNA hsa-mir-205 and hsa-mir-203a are among the key genomic features that may substantially contribute to the oncogenesis of melanoma even on the basis of simple expression threshold. The major prediction models and analysis modules to predict metastatic and primary tumor samples of SKCM are available from a webserver, CancerSPP (http://webs.iiitd.edu.in/raghava/cancerspp/).

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Dissecting the Lymphatic System to Predict Melanoma Metastasis

Frontiers in Oncology ◽

10.3389/fonc.2020.576190 ◽

2020 ◽

Vol 10 ◽

Author(s):

Rishi Suresh ◽

Arturas Ziemys ◽

Ashley M. Holder

Keyword(s):

Cutaneous Melanoma ◽

Lymphatic System ◽

Survival Rates ◽

Metastatic Potential ◽

The United States ◽

Molecular Characteristics ◽

High Survival ◽

Melanoma Metastasis ◽

Metastatic Risk ◽

Ajcc Staging

Melanoma is the most lethal form of skin cancer in the United States. Current American Joint Committee on Cancer (AJCC) staging uses Breslow depth and ulceration as the two primary tumor factors that predict metastatic risk in cutaneous melanoma. Early disease stages are generally associated with high survival rates. However, in some cases, patients with thin melanomas develop advanced disease, suggesting other factors may contribute to the metastatic potential of an individual patient’s melanoma. This review focuses on the role of the lymphatic system in the metastasis of cutaneous melanoma, from recent discoveries in mechanisms of lymphangiogenesis to elements of the lymphatic system that ultimately may aid clinicians in determining which patients are at highest risk. Ultimately, this review highlights the need to integrate pathological, morphological, and molecular characteristics of lymphatics into a “biomarker” for metastatic potential.

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Extracellular miRNAs as Biomarkers of Head and Neck Cancer Progression and Metastasis

International Journal of Molecular Sciences ◽

10.3390/ijms20194799 ◽

2019 ◽

Vol 20 (19) ◽

pp. 4799 ◽

Cited By ~ 3

Author(s):

Zuzanna Nowicka ◽

Konrad Stawiski ◽

Bartłomiej Tomasik ◽

Wojciech Fendler

Keyword(s):

Head And Neck ◽

Cancer Progression ◽

Treatment Options ◽

Survival Rates ◽

Chemotherapy Resistance ◽

Second Primary ◽

Special Focus ◽

Primary Tumors ◽

Extracellular Mirnas ◽

Extracellular Mirna

Head and neck squamous cell carcinomas (HNSCCs) contribute to over 300,000 deaths every year worldwide. Although the survival rates have improved in some groups of patients, mostly due to new treatment options and the increasing percentage of human papillomavirus (HPV)-related cancers, local recurrences and second primary tumors remain a great challenge for the clinicians. Presently, there is no biomarker for patient surveillance that could help identify patients with HNSCC that are more likely to experience a relapse or early progression, potentially requiring closer follow-up or salvage treatment. MicoRNAs (miRNAs) are non-coding RNA molecules that posttranscriptionally modulate gene expression. They are highly stable and their level can be measured in biofluids including serum, plasma, and saliva, enabling quick results and allowing for repeated analysis during and after the completion of therapy. This has cemented the role of miRNAs as biomarkers with a huge potential in oncology. Since altered miRNA expression was described in HNSCC and many miRNAs play a role in radio- and chemotherapy resistance, cancer progression, and metastasis, they can be utilized as biomarkers of these phenomena. This review outlines recent discoveries in the field of extracellular miRNA-based biomarkers of HNSCC progression and metastasis, with a special focus on HPV-related cancers and radioresistance.

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Proteomic Tissue-Based Classifier for Early Prediction of Prostate Cancer Progression

Cancers ◽

10.3390/cancers12051268 ◽

2020 ◽

Vol 12 (5) ◽

pp. 1268 ◽

Cited By ~ 1

Author(s):

Yuqian Gao ◽

Yi-Ting Wang ◽

Yongmei Chen ◽

Hui Wang ◽

Denise Young ◽

...

Keyword(s):

Cancer Progression ◽

Operating Characteristic ◽

Characteristic Curve ◽

Survival Rates ◽

Standard Of Care ◽

Driver Genes ◽

Primary Tumors ◽

Entire Cohort ◽

Risk Patients ◽

Prostate Cancers

Although ~40% of screen-detected prostate cancers (PCa) are indolent, advanced-stage PCa is a lethal disease with 5-year survival rates around 29%. Identification of biomarkers for early detection of aggressive disease is a key challenge. Starting with 52 candidate biomarkers, selected from existing PCa genomics datasets and known PCa driver genes, we used targeted mass spectrometry to quantify proteins that significantly differed in primary tumors from PCa patients treated with radical prostatectomy (RP) across three study outcomes: (i) metastasis ≥1-year post-RP, (ii) biochemical recurrence ≥1-year post-RP, and (iii) no progression after ≥10 years post-RP. Sixteen proteins that differed significantly in an initial set of 105 samples were evaluated in the entire cohort (n = 338). A five-protein classifier which combined FOLH1, KLK3, TGFB1, SPARC, and CAMKK2 with existing clinical and pathological standard of care variables demonstrated significant improvement in predicting distant metastasis, achieving an area under the receiver-operating characteristic curve of 0.92 (0.86, 0.99, p = 0.001) and a negative predictive value of 92% in the training/testing analysis. This classifier has the potential to stratify patients based on risk of aggressive, metastatic PCa that will require early intervention compared to low risk patients who could be managed through active surveillance.

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In silico Prediction of Inhibitory Constant of Thrombin Inhibitors Using Machine Learning

Combinatorial Chemistry & High Throughput Screening ◽

10.2174/1386207322666181220130232 ◽

2019 ◽

Vol 21 (9) ◽

pp. 662-669 ◽

Cited By ~ 1

Author(s):

Junnan Zhao ◽

Lu Zhu ◽

Weineng Zhou ◽

Lingfeng Yin ◽

Yuchen Wang ◽

...

Keyword(s):

Machine Learning ◽

Prediction Models ◽

Regression Tree ◽

Large Data ◽

Thrombin Inhibitors ◽

Coagulation Cascade ◽

Gradient Boosting ◽

Support Vector ◽

Data Set ◽

Descriptor Selection

Background: Thrombin is the central protease of the vertebrate blood coagulation cascade, which is closely related to cardiovascular diseases. The inhibitory constant Ki is the most significant property of thrombin inhibitors. Method: This study was carried out to predict Ki values of thrombin inhibitors based on a large data set by using machine learning methods. Taking advantage of finding non-intuitive regularities on high-dimensional datasets, machine learning can be used to build effective predictive models. A total of 6554 descriptors for each compound were collected and an efficient descriptor selection method was chosen to find the appropriate descriptors. Four different methods including multiple linear regression (MLR), K Nearest Neighbors (KNN), Gradient Boosting Regression Tree (GBRT) and Support Vector Machine (SVM) were implemented to build prediction models with these selected descriptors. Results: The SVM model was the best one among these methods with R2=0.84, MSE=0.55 for the training set and R2=0.83, MSE=0.56 for the test set. Several validation methods such as yrandomization test and applicability domain evaluation, were adopted to assess the robustness and generalization ability of the model. The final model shows excellent stability and predictive ability and can be employed for rapid estimation of the inhibitory constant, which is full of help for designing novel thrombin inhibitors.

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Machine Learning Methods Applied to the Prediction of Pseudo-nitzschia spp. Blooms in the Galician Rias Baixas (NW Spain)

ISPRS International Journal of Geo-Information ◽

10.3390/ijgi10040199 ◽

2021 ◽

Vol 10 (4) ◽

pp. 199

Author(s):

Francisco M. Bellas Aláez ◽

Jesus M. Torres Palenzuela ◽

Evangelos Spyrakos ◽

Luis González Vilas

Keyword(s):

Machine Learning ◽

Performance Metrics ◽

Prediction Models ◽

Support Vector ◽

False Alarms ◽

Learning Approaches ◽

Learning Methods ◽

Machine Learning Methods ◽

Rías Baixas ◽

New Algorithms

This work presents new prediction models based on recent developments in machine learning methods, such as Random Forest (RF) and AdaBoost, and compares them with more classical approaches, i.e., support vector machines (SVMs) and neural networks (NNs). The models predict Pseudo-nitzschia spp. blooms in the Galician Rias Baixas. This work builds on a previous study by the authors (doi.org/10.1016/j.pocean.2014.03.003) but uses an extended database (from 2002 to 2012) and new algorithms. Our results show that RF and AdaBoost provide better prediction results compared to SVMs and NNs, as they show improved performance metrics and a better balance between sensitivity and specificity. Classical machine learning approaches show higher sensitivities, but at a cost of lower specificity and higher percentages of false alarms (lower precision). These results seem to indicate a greater adaptation of new algorithms (RF and AdaBoost) to unbalanced datasets. Our models could be operationally implemented to establish a short-term prediction system.

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Development of Machine Learning Models for Prediction of Smoking Cessation Outcome

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18052584 ◽

2021 ◽

Vol 18 (5) ◽

pp. 2584

Author(s):

Cheng-Chien Lai ◽

Wei-Hsin Huang ◽

Betty Chia-Chen Chang ◽

Lee-Ching Hwang

Keyword(s):

Machine Learning ◽

Smoking Cessation ◽

Success Rate ◽

Prediction Models ◽

Smoking Status ◽

Medical Center ◽

Machine Learning Algorithms ◽

Classification And Regression Tree ◽

Support Vector ◽

Smoking Cessation Outcome

Predictors for success in smoking cessation have been studied, but a prediction model capable of providing a success rate for each patient attempting to quit smoking is still lacking. The aim of this study is to develop prediction models using machine learning algorithms to predict the outcome of smoking cessation. Data was acquired from patients underwent smoking cessation program at one medical center in Northern Taiwan. A total of 4875 enrollments fulfilled our inclusion criteria. Models with artificial neural network (ANN), support vector machine (SVM), random forest (RF), logistic regression (LoR), k-nearest neighbor (KNN), classification and regression tree (CART), and naïve Bayes (NB) were trained to predict the final smoking status of the patients in a six-month period. Sensitivity, specificity, accuracy, and area under receiver operating characteristic (ROC) curve (AUC or ROC value) were used to determine the performance of the models. We adopted the ANN model which reached a slightly better performance, with a sensitivity of 0.704, a specificity of 0.567, an accuracy of 0.640, and an ROC value of 0.660 (95% confidence interval (CI): 0.617–0.702) for prediction in smoking cessation outcome. A predictive model for smoking cessation was constructed. The model could aid in providing the predicted success rate for all smokers. It also had the potential to achieve personalized and precision medicine for treatment of smoking cessation.

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Mitochondria-Targeted Antioxidants MitoQ and MitoTEMPO Do Not Influence BRAF-Driven Malignant Melanoma and KRAS-Driven Lung Cancer Progression in Mice

Antioxidants ◽

10.3390/antiox10020163 ◽

2021 ◽

Vol 10 (2) ◽

pp. 163

Author(s):

Kristell Le Gal ◽

Clotilde Wiel ◽

Mohamed X. Ibrahim ◽

Marcus Henricsson ◽

Volkan I. Sayin ◽

...

Keyword(s):

Lung Cancer ◽

Malignant Melanoma ◽

Cancer Cells ◽

Cancer Progression ◽

Nuclear Dna ◽

Human Melanoma ◽

Oxygen Species ◽

Lung Cancer Cell ◽

Primary Tumors ◽

Anti Cancer

Cancer cells produce high levels of mitochondria-associated reactive oxygen species (ROS) that can damage macromolecules, but also promote cell signaling and proliferation. Therefore, mitochondria-targeted antioxidants have been suggested to be useful in anti-cancer therapy, but no studies have convincingly addressed this question. Here, we administered the mitochondria-targeted antioxidants MitoQ and MitoTEMPO to mice with BRAF-induced malignant melanoma and KRAS-induced lung cancer, and found that these compounds had no impact on the number of primary tumors and metastases; and did not influence mitochondrial and nuclear DNA damage levels. Moreover, MitoQ and MitoTEMPO did not influence proliferation of human melanoma and lung cancer cell lines. MitoQ and its control substance dTPP, but not MitoTEMPO, increased glycolytic rates and reduced respiration in melanoma cells; whereas only dTPP produced this effect in lung cancer cells. Our results do not support the use of mitochondria-targeted antioxidants for anti-cancer monotherapy, at least not in malignant melanoma and lung cancer.

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Machine Learning-Based Prediction of Air Quality

Applied Sciences ◽

10.3390/app10249151 ◽

2020 ◽

Vol 10 (24) ◽

pp. 9151

Author(s):

Yun-Chia Liang ◽

Yona Maimury ◽

Angela Hsiang-Ling Chen ◽

Josue Rodolfo Cuevas Juarez

Keyword(s):

Machine Learning ◽

Air Quality ◽

Random Forest ◽

Prediction Models ◽

Superior Performance ◽

Support Vector ◽

Economic Activities ◽

Adaptive Boosting ◽

Series Of Experiments ◽

Artificial Neural Network Ann

Air, an essential natural resource, has been compromised in terms of quality by economic activities. Considerable research has been devoted to predicting instances of poor air quality, but most studies are limited by insufficient longitudinal data, making it difficult to account for seasonal and other factors. Several prediction models have been developed using an 11-year dataset collected by Taiwan’s Environmental Protection Administration (EPA). Machine learning methods, including adaptive boosting (AdaBoost), artificial neural network (ANN), random forest, stacking ensemble, and support vector machine (SVM), produce promising results for air quality index (AQI) level predictions. A series of experiments, using datasets for three different regions to obtain the best prediction performance from the stacking ensemble, AdaBoost, and random forest, found the stacking ensemble delivers consistently superior performance for R2 and RMSE, while AdaBoost provides best results for MAE.

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Screening of gene markers related to the prognosis of metastatic skin cutaneous melanoma based on Logit regression and survival analysis

BMC Medical Genomics ◽

10.1186/s12920-021-00923-0 ◽

2021 ◽

Vol 14 (1) ◽

Author(s):

Guoliang Jia ◽

Zheyu Song ◽

Zhonghang Xu ◽

Youmao Tao ◽

Yuanyu Wu ◽

...

Keyword(s):

Gene Expression ◽

Cutaneous Melanoma ◽

Expression Profiles ◽

The Cancer Genome Atlas ◽

Training Dataset ◽

Validation Dataset ◽

Survival Prognosis ◽

Gene Markers ◽

Clinical Prognosis ◽

Logit Regression

Abstract Background Bioinformatics was used to analyze the skin cutaneous melanoma (SKCM) gene expression profile to provide a theoretical basis for further studying the mechanism underlying metastatic SKCM and the clinical prognosis. Methods We downloaded the gene expression profiles of 358 metastatic and 102 primary (nonmetastatic) CM samples from The Cancer Genome Atlas (TCGA) database as a training dataset and the GSE65904 dataset from the National Center for Biotechnology Information database as a validation dataset. Differentially expressed genes (DEGs) were screened using the limma package of R3.4.1, and prognosis-related feature DEGs were screened using Logit regression (LR) and survival analyses. We also used the STRING online database, Cytoscape software, and Database for Annotation, Visualization and Integrated Discovery software for protein–protein interaction network, Gene Ontology, and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway analyses based on the screened DEGs. Results Of the 876 DEGs selected, 11 (ZNF750, NLRP6, TGM3, KRTDAP, CAMSAP3, KRT6C, CALML5, SPRR2E, CD3G, RTP5, and FAM83C) were screened using LR analysis. The survival prognosis of nonmetastatic group was better compared to the metastatic group between the TCGA training and validation datasets. The 11 DEGs were involved in 9 KEGG signaling pathways, and of these 11 DEGs, CALML5 was a feature DEG involved in the melanogenesis pathway, 12 targets of which were collected. Conclusion The feature DEGs screened, such as CALML5, are related to the prognosis of metastatic CM according to LR. Our results provide new ideas for exploring the molecular mechanism underlying CM metastasis and finding new diagnostic prognostic markers.

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