RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma
Abstract
:Simple Summary
Abstract
1. Introduction
- To the best of our knowledge, this is the first study that utilizes a proteomic dataset acquired pre- and post-completion of CRT to categorize its alteration based on thousands of proteomic features available before and following intervention.
- To our knowledge, this is also the first study that employs filter and embedding-based FS algorithms for GBM proteomic data acquired with SOC treatment.
- We present a novel rank-based feature weighting and selection mechanism (RadWise) to identify relevant feature subsets with a cross-validation technique for machine learning problems.
- We combine the advantages of the two efficient and popular FS methods, namely, LASSO and mRMR, with rank-based feature weighting for pattern classification.
- We have investigated the comprehensive effects of FS and feature weighting methods separately for five different learning models on the proteomic dataset.
- The effects and the results of conventional statistical approaches without feature engineering have also been presented, compared, and discussed in detail and were compared to the FS method.
- We compared our proposed methodology with our statistical threshold-based heuristic method as well.
- We achieve high-performance results with approximately a thousand times smaller size features than the original proteomic dataset predictors.
- We examined the identified features in ingenuity pathway analysis (IPA) to link to disease (brain cancer, malignancy), pathways, and upstream proteins.
- Our results present promising results for GBM proteomic biomarker research in our field.
2. Methods
2.1. The Proposed Architecture for Feature Selection of Radiation Therapy Categorization of Glioblastoma Patients
2.2. Feature Selection Methods
2.2.1. mRMR
2.2.2. LASSO
2.3. Feature Weighting
2.4. Classification Methods
2.4.1. Support Vector Machine
2.4.2. Logistic Regression
2.4.3. K Nearest Neighbors
2.4.4. Random Forest
2.4.5. AdaBoost
3. Experimental Work
3.1. Experimental Process
3.2. Dataset
3.3. Performance Metrics
3.4. Computational Results
3.4.1. The Effects of Using Feature Selection Methods
3.4.2. The Effects of Using Only LASSO Feature Selection and Weighting Methods
3.4.3. The Effects of Using Only mRMR Feature Selection and Feature Weighting Methods
3.4.4. The Effects of Using LASSO and mRMR Feature Selection and Feature Weighting Methods
3.4.5. Performance Results Based on Feature Selection and Weighting Process
3.5. Comparison with the Related Methods for Proteomic Biomarker Identification
3.5.1. Comparison with Statistical Threshold-Based Heuristic Method (OSTH)
3.5.2. Comparison with the Related Statistical Test-Based Method
- Our feature selection approach employs two strategies: Multivariate filter FS (i.e., mRMR) and embedded FS (i.e., LASSO).
- Our hybrid FS method utilizes cross-validation to obtain a more robust result and a final feature set for our dataset.
- Our rank-based weighting approach assigns more importance to mRMR than to LASSO to arrive at the described performance results (i.e., accuracy rate). Due to this weighting criterion, the most prominent features identified in the statistical analyses are also identified by ML. However, signals such as cystatin M that in the purely statistical test-based approach rank lower can be elevated given their interaction with other targets. This is of great importance since novel large proteomic panels may identify molecules with known, unknown, or unassigned biological annotation and eventually novel functions or roles that are context specific.
- We experimented with all minimum weight-based values to obtain the minimum selected features with the highest accuracy rate. This process reduced 7289 protein signals to 8 with a higher accuracy rate.
- If the current feature selection process were not applied, the number and names of the features could not be determined precisely.
Entrez Gene Symbol | Target Full Name | Biological Relevance to Glioma |
---|---|---|
K2C5 | Keratin, type II cytoskeletal 5 | Yes, evolving biomarker/target [61] |
Keratin-1 | Keratin, type II cytoskeletal 1 | Yes, evolving biomarker/target [61] |
STRATIFIN (SFN) | 14-3-3 protein sigma | Yes, tumor suppressor gene expression pattern correlates with glioma grade and prognosis [62] |
MIC-1 (GDF15) | Growth/differentiation factor 15 | Yes, biomarker, novel immune checkpoint [63] |
GFAP | Glial fibrillary acidic protein | Yes, evolving biomarker/target [64] |
CSPG3 (NCAN) | Neurocan core protein | Yes, glycoproteomic profiles of GBM subtypes, differential expression versus control tissue [65] |
Cystatin M (CST6) | Cystatin M | Yes, cell type-specific expression in normal brain and epigenetic silencing in glioma [66] |
Proteinase-3 (PRTN3) | Proteinase-3 | Yes, evolving role, may relate to pyroptosis, oxidative stress and immune response [59] |
3.5.3. Connecting the Identified Markers to Disease, Pathways, and Upstream Proteins
4. Discussion
5. Conclusions and Future Work
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Protocol | Most recent amendment approval date | Current protocol version date |
02C0064 | 14 March 2022 | 14 March 2022 |
04C0200 | 19 April 2022 | 25 February 2022 |
06C0112 | Study closure approval 30 June 2020 | NA |
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
ACC | Accuracy |
AdaBoost | Adaptive Boosting |
AUC | Area Under the ROC Curve |
CRT | Chemoirradiation |
CNS | Central Nervous System |
F1 | F-Measure |
FS | Feature Selection |
FW | Feature Weighting |
GBM | Glioblastoma Multiforme |
HGG | High-Grade Glioma |
IPA | Ingenuity Pathway Analysis |
KNN | K Nearest Neighbors |
LGG | Low-Grade Glioma |
LASSO | Least Absolute Shrinkage and Selection Operator |
LR | Logistic Regression |
MRI | Magnetic Resonance Imaging |
MRMR | Minimum Redundancy Maximum Relevance |
NCI | National Cancer Institute |
NIH | National Institutes of Health |
NP-Hard | Non-Deterministic Polynomial Time Hard |
OS | Overall Survival |
PRE | Precision |
REC | Recall |
RF | Random Forest |
RT | Radiation Therapy |
ROC | Receiver Operating Characteristic |
SOC | Standard of Care |
SPEC | Specificity |
SVM | Support Vector Machine |
TCGA | The Cancer Genome Atlas |
TMZ | Temozolomide |
WHO | World Health Organization |
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ML-ACC | Without FS | LASSO FS | mRMR FS |
---|---|---|---|
SVM | 57.860 | 78.674 | 91.515 |
LR | 67.633 | 85.341 | 92.708 |
KNN | 62.197 | 53.068 | 88.466 |
RF | 73.826 | 88.466 | 89.659 |
AdaBoost | 88.409 | 89.072 | 88.447 |
k | # of Features | SVM | LR | KNN | RF | AdaBoost |
---|---|---|---|---|---|---|
5 | 11 | 93.314 | 92.083 | 82.386 | 91.496 | 91.477 |
4 | 26 | 89.640 | 93.314 | 62.197 | 93.939 | 90.890 |
3 | 44 | 89.053 | 96.363 | 46.345 | 92.121 | 93.939 |
2 | 90 | 85.985 | 92.064 | 60.379 | 91.496 | 93.901 |
1 | 197 | 76.269 | 85.966 | 54.868 | 87.841 | 87.822 |
k | # of Features | SVM | LR | KNN | RF | AdaBoost |
---|---|---|---|---|---|---|
5 | 5 | 86.004 | 88.428 | 87.235 | 87.841 | 87.197 |
4 | 7 | 90.890 | 92.708 | 90.265 | 91.496 | 91.496 |
3 | 8 | 95.152 | 96.364 | 92.708 | 90.871 | 93.920 |
2 | 11 | 92.708 | 96.364 | 92.689 | 90.284 | 94.508 |
1 | 34 | 92.102 | 92.102 | 87.254 | 92.121 | 90.871 |
k | # of Features | SVM | LR | KNN | RF | AdaBoost |
---|---|---|---|---|---|---|
15 | 2 | 87.216 | 87.216 | 89.659 | 87.841 | 85.379 |
14 | 2 | 87.216 | 87.216 | 89.659 | 87.841 | 85.379 |
13 | 4 | 90.265 | 90.265 | 90.284 | 92.727 | 89.034 |
12 | 6 | 93.920 | 92.708 | 91.477 | 92.102 | 93.314 |
11 | 6 | 93.920 | 92.708 | 91.477 | 92.102 | 93.314 |
10 | 8 | 95.152 | 96.364 | 92.708 | 90.265 | 93.920 |
9 | 8 | 95.152 | 96.364 | 92.708 | 90.265 | 93.920 |
8 | 8 | 95.152 | 96.364 | 92.708 | 90.265 | 93.920 |
7 | 10 | 93.333 | 94.546 | 90.284 | 92.102 | 90.284 |
6 | 12 | 93.939 | 95.152 | 90.909 | 91.496 | 90.246 |
5 | 17 | 96.345 | 95.114 | 88.447 | 91.496 | 90.871 |
4 | 32 | 93.295 | 95.739 | 68.921 | 90.890 | 89.640 |
3 | 52 | 92.121 | 95.151 | 49.962 | 93.333 | 92.670 |
2 | 113 | 87.216 | 92.670 | 60.379 | 91.496 | 95.152 |
1 | 218 | 78.087 | 85.966 | 55.492 | 89.053 | 93.314 |
k | # of Features | SVM | LR | KNN | RF | AdaBoost |
---|---|---|---|---|---|---|
15 | 2 | 5.175 | 4.408 | 4.072 | 4.232 | 2.191 |
14 | 2 | 5.175 | 4.408 | 4.072 | 4.232 | 2.191 |
13 | 4 | 4.423 | 4.821 | 4.425 | 4.924 | 2.384 |
12 | 6 | 5.060 | 5.269 | 3.509 | 4.088 | 3.515 |
11 | 6 | 5.060 | 5.269 | 3.509 | 4.088 | 3.515 |
10 | 8 | 3.636 | 4.454 | 5.269 | 3.496 | 4.272 |
9 | 8 | 3.636 | 4.454 | 5.269 | 3.496 | 4.272 |
8 | 8 | 3.636 | 4.454 | 5.269 | 3.496 | 4.272 |
7 | 10 | 4.848 | 5.555 | 4.425 | 4.088 | 4.425 |
6 | 12 | 4.285 | 3.636 | 6.357 | 3.995 | 3.526 |
5 | 17 | 2.966 | 2.444 | 3.476 | 4.827 | 5.400 |
4 | 32 | 6.177 | 4.105 | 6.384 | 4.259 | 1.442 |
3 | 52 | 4.535 | 2.424 | 7.329 | 4.020 | 2.469 |
2 | 113 | 4.807 | 1.557 | 4.954 | 4.430 | 4.924 |
1 | 218 | 4.720 | 3.133 | 5.559 | 3.031 | 3.515 |
ML | ACC% | AUC | F1 | PRE | REC | SPEC |
---|---|---|---|---|---|---|
SVM | 57.860 | 0.415 | 0.518 | 0.698 | 0.515 | 0.690 |
LR | 67.633 | 0.755 | 0.676 | 0.681 | 0.681 | 0.673 |
KNN | 62.197 | 0.647 | 0.581 | 0.662 | 0.527 | 0.722 |
RF | 73.826 | 0.808 | 0.744 | 0.768 | 0.746 | 0.737 |
AdaBoost | 88.409 | 0.951 | 0.886 | 0.882 | 0.893 | 0.873 |
ML | ACC% | AUC | F1 | PRE | REC | SPEC |
---|---|---|---|---|---|---|
SVM | 95.152 | 0.989 | 0.949 | 0.975 | 0.928 | 0.976 |
LR | 96.364 | 0.987 | 0.964 | 0.963 | 0.965 | 0.965 |
KNN | 92.708 | 0.965 | 0.930 | 0.929 | 0.932 | 0.923 |
RF | 90.265 | 0.978 | 0.902 | 0.885 | 0.928 | 0.876 |
AdaBoost | 93.920 | 0.979 | 0.941 | 0.941 | 0.942 | 0.935 |
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Tasci, E.; Jagasia, S.; Zhuge, Y.; Sproull, M.; Cooley Zgela, T.; Mackey, M.; Camphausen, K.; Krauze, A.V. RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma. Cancers 2023, 15, 2672. https://doi.org/10.3390/cancers15102672
Tasci E, Jagasia S, Zhuge Y, Sproull M, Cooley Zgela T, Mackey M, Camphausen K, Krauze AV. RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma. Cancers. 2023; 15(10):2672. https://doi.org/10.3390/cancers15102672
Chicago/Turabian StyleTasci, Erdal, Sarisha Jagasia, Ying Zhuge, Mary Sproull, Theresa Cooley Zgela, Megan Mackey, Kevin Camphausen, and Andra Valentina Krauze. 2023. "RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma" Cancers 15, no. 10: 2672. https://doi.org/10.3390/cancers15102672
APA StyleTasci, E., Jagasia, S., Zhuge, Y., Sproull, M., Cooley Zgela, T., Mackey, M., Camphausen, K., & Krauze, A. V. (2023). RadWise: A Rank-Based Hybrid Feature Weighting and Selection Method for Proteomic Categorization of Chemoirradiation in Patients with Glioblastoma. Cancers, 15(10), 2672. https://doi.org/10.3390/cancers15102672