As a type of programmed cell death, anoikis resistance plays an essential role in tumor metastasis, allowing cancer cells to survive in the systemic circulation and as a key pathway for regulating critical biological processes. We conducted an exploratory analysis to improve risk stratification and optimize adjuvant treatment choices for patients with breast cancer, and identify multigene features in mRNA and lncRNA transcriptome profiles associated with anoikis. First, the variance selection method filters low information content genes in RNA sequence and then extracts the mRNA and lncRNA expression data base on annotation files. Then, the top ten key mRNAs are screened out through the PPI network. Pearson analysis has been employed to identify lncRNAs related to anoikis, and the prognosis-related lncRNAs are selected using Univariate Cox regression and machine learning. Finally, we identified a group of RNAs (including ten mRNAs and six lncRNAs) and integrated the expression data of 16 genes to construct a risk-scoring system for BRCA prognosis and drug sensitivity analysis. The risk score's validity has been evaluated with the ROC curve, Kaplan-Meier survival curve analysis and decision curve analysis (DCA). For the methylation data, we have obtained 169 anoikis-related prognostic methylation sites, integrated these sites with 16 RNA features and further used the deep learning model to evaluate and predict the survival risk of patients. The developed anoikis feature is demonstrated a consistency index (C-index) of 0.778, indicating its potential to predict the survival probability of breast cancer patients using deep learning methods.
Citation: Huili Yang, Wangren Qiu, Zi Liu. Anoikis-related mRNA-lncRNA and DNA methylation profiles for overall survival prediction in breast cancer patients[J]. Mathematical Biosciences and Engineering, 2024, 21(1): 1590-1609. doi: 10.3934/mbe.2024069
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As a type of programmed cell death, anoikis resistance plays an essential role in tumor metastasis, allowing cancer cells to survive in the systemic circulation and as a key pathway for regulating critical biological processes. We conducted an exploratory analysis to improve risk stratification and optimize adjuvant treatment choices for patients with breast cancer, and identify multigene features in mRNA and lncRNA transcriptome profiles associated with anoikis. First, the variance selection method filters low information content genes in RNA sequence and then extracts the mRNA and lncRNA expression data base on annotation files. Then, the top ten key mRNAs are screened out through the PPI network. Pearson analysis has been employed to identify lncRNAs related to anoikis, and the prognosis-related lncRNAs are selected using Univariate Cox regression and machine learning. Finally, we identified a group of RNAs (including ten mRNAs and six lncRNAs) and integrated the expression data of 16 genes to construct a risk-scoring system for BRCA prognosis and drug sensitivity analysis. The risk score's validity has been evaluated with the ROC curve, Kaplan-Meier survival curve analysis and decision curve analysis (DCA). For the methylation data, we have obtained 169 anoikis-related prognostic methylation sites, integrated these sites with 16 RNA features and further used the deep learning model to evaluate and predict the survival risk of patients. The developed anoikis feature is demonstrated a consistency index (C-index) of 0.778, indicating its potential to predict the survival probability of breast cancer patients using deep learning methods.
Metal--organic materials (MOMs) are a class of synthesized, often porous, and crystalline materials that have comprised the focus of a large amount of experimental and theoretical studies for the past few decades [1,2,3]. The application of MOMs is very diverse and has become essentially ubiquitous in scientific research, ranging from gas sorption [3,4,5,6,7,8,9,10,11,12,13,14,15,16,17], separation [4,18,19], catalysis [20,21,22,23], sensing [24,25], photoinduced electron transfer [26,27,28,29] and biological uses [30,31]. The major reason for this is that there are conceivably infinite variations of MOM structures that can be synthesized by altering their component metal ions, organic linkers, secondary building units (SBUs) [32,33], solvents, and thermodynamic or physical conditions.
This study focuses on explaining the sorption properties of CO
Recent studies involved examining C
The syntheses of MPM-1-Cl and MPM-1-Br are reported in references [38,39], respectively. Both MPMs feature an interesting hydrogen-bonding network in which (1) four hydrogen atoms from four different adenine linkers are hydrogen-bonded to a single halide ion and (2) an adenine linker from one [Cu
The purpose of this study is to elucidate (with atomistic resolution) the sorption properties of CO
The potential energy function for MPM-1-Cl was developed by our group in previous work [41] and utilized herein. The crystal structure for MPM-1-Br was obtained from reference [39]. For all simulations in both MPMs, the sorbent atoms were treated as rigid to accomodate a constant volume ensemble system. This approximation is especially valid when phononic effects are minor [43]. As with previous work on MPM-1-Cl [41], all atoms of MPM-1-Br were given Lennard-Jones 12--6 parameters, point partial charges, and scalar point polarizabilities to model repulsion/dispersion, stationary electrostatic, and explicit polarization, respectively. The Lennard-Jones parameters for all MPM atoms were taken from the Universal Force Field (UFF)[44], while the exponentially-damped polarizabilities for all atoms other than Cu were obtained from van Duijnen et al. [45]. The polarizability parameter for Cu
The potentials used for CO
Simulated annealing calculations were performed using the polarizable CO
C
Interestingly, the relative uptake trend is reversed at 298 K: MPM-1-Br shows slightly greater affinity for C
Xie et al. also measured CO
Xie et al. [37] derived the experimental
CO | C | ||||||
MPM-1-Cl | Exp. | Simulation | Exp. | Simulation | |||
Model | CO | CO | TraPPE | C | C | ||
23.76 | 22.77 | 24.33 | 26.22 | 28.57 | 25.49 | 25.65 | |
0.05 atm loading, 273 K | 0.26 | 0.23 | 0.36 | 0.80 | 0.80 | 1.08 | 0.81 |
1.0 atm loading, 273 K | 3.50 | 3.63 | 4.75 | 5.06 | 3.86 | 5.05 | 5.25 |
0.05 atm loading, 298 K | 0.12 | 0.13 | 0.17 | 0.35 | 0.34 | 0.34 | 0.25 |
1.0 atm loading, 298 K | 1.97 | 2.07 | 2.74 | 3.60 | 2.78 | 4.21 | 4.57 |
MPM-1-Br | Exp. | Simulation | Exp. | Simulation | |||
Model | CO | CO | TraPPE | C | C | ||
21.61 | 25.02 | 25.57 | 25.40 | 25.05 | 30.61 | 27.55 | |
0.05 atm loading, 273 K | 0.19 | 0.23 | 0.33 | 0.51 | 0.81 | 1.41 | 1.23 |
1.0 atm loading, 273 K | 2.54 | 2.52 | 3.20 | 3.77 | 3.59 | 3.69 | 3.66 |
0.05 atm loading, 298 K | 0.12 | 0.11 | 0.13 | 0.20 | 0.43 | 0.34 | 0.22 |
1.0 atm loading, 298 K | 1.56 | 1.52 | 1.80 | 2.47 | 2.82 | 3.31 | 3.30 |
With regards to C
The
The experimental and simulated C
Figure 5a shows the simulated C
As shown in Table 1, the experimental C
The simulated C
The theoretical
The simulated CO
The CO
A comparison of the experimental and simulated CO
As shown in Table 1, the experimental atmospheric CO
Note, we also performed simulations of CO
The simulated CO
In MPM-1-Br, the
The calculated
Pham et al. [41] reported a binding site for CO
The electrostatic and electrodynamic (polarizable) effects serve to attract the positively charged carbon center of the CO
MPM-1-Cl is able to sorb more CO
The primary binding site for C
Overall, there are more concurrent interactions between the C
The results for the classical binding energy calculations from simulated annealing are presented in Table 2. It is clear from these simulations that both materials favor C
MPM-1-Cl | Binding energy (kJ mol | Steps ( | Final Temp. (K) |
CO | 2.37 | 113 | |
C | 2.46 | 125 | |
MPM-1-Br | |||
CO | 3.24 | 129 | |
C | 4.53 | 150 |
This study aimed to elucidate the CO
It was discovered through our simulations that the primary binding site for C
Herein, we demonstrated how substitution of the halide ion in two isostructural MPMs with the empirical formula [Cu
The authors acknowledge the National Science Foundation (Award No. DMR-1607989), including support from the Major Research Instrumentation Program (Award No. CHE-1531590). Computational resources were made available by a XSEDE Grant (No. TG-DMR090028) and by Research Computing at the University of South Florida. B.S. also acknowledges support from an American Chemical Society Petroleum Research Fund grant (ACS PRF 56673-ND6).
The authors declare no conflict of interest related to the content of this publication.
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CO | C | ||||||
MPM-1-Cl | Exp. | Simulation | Exp. | Simulation | |||
Model | CO | CO | TraPPE | C | C | ||
23.76 | 22.77 | 24.33 | 26.22 | 28.57 | 25.49 | 25.65 | |
0.05 atm loading, 273 K | 0.26 | 0.23 | 0.36 | 0.80 | 0.80 | 1.08 | 0.81 |
1.0 atm loading, 273 K | 3.50 | 3.63 | 4.75 | 5.06 | 3.86 | 5.05 | 5.25 |
0.05 atm loading, 298 K | 0.12 | 0.13 | 0.17 | 0.35 | 0.34 | 0.34 | 0.25 |
1.0 atm loading, 298 K | 1.97 | 2.07 | 2.74 | 3.60 | 2.78 | 4.21 | 4.57 |
MPM-1-Br | Exp. | Simulation | Exp. | Simulation | |||
Model | CO | CO | TraPPE | C | C | ||
21.61 | 25.02 | 25.57 | 25.40 | 25.05 | 30.61 | 27.55 | |
0.05 atm loading, 273 K | 0.19 | 0.23 | 0.33 | 0.51 | 0.81 | 1.41 | 1.23 |
1.0 atm loading, 273 K | 2.54 | 2.52 | 3.20 | 3.77 | 3.59 | 3.69 | 3.66 |
0.05 atm loading, 298 K | 0.12 | 0.11 | 0.13 | 0.20 | 0.43 | 0.34 | 0.22 |
1.0 atm loading, 298 K | 1.56 | 1.52 | 1.80 | 2.47 | 2.82 | 3.31 | 3.30 |
MPM-1-Cl | Binding energy (kJ mol | Steps ( | Final Temp. (K) |
CO | 2.37 | 113 | |
C | 2.46 | 125 | |
MPM-1-Br | |||
CO | 3.24 | 129 | |
C | 4.53 | 150 |
CO | C | ||||||
MPM-1-Cl | Exp. | Simulation | Exp. | Simulation | |||
Model | CO | CO | TraPPE | C | C | ||
23.76 | 22.77 | 24.33 | 26.22 | 28.57 | 25.49 | 25.65 | |
0.05 atm loading, 273 K | 0.26 | 0.23 | 0.36 | 0.80 | 0.80 | 1.08 | 0.81 |
1.0 atm loading, 273 K | 3.50 | 3.63 | 4.75 | 5.06 | 3.86 | 5.05 | 5.25 |
0.05 atm loading, 298 K | 0.12 | 0.13 | 0.17 | 0.35 | 0.34 | 0.34 | 0.25 |
1.0 atm loading, 298 K | 1.97 | 2.07 | 2.74 | 3.60 | 2.78 | 4.21 | 4.57 |
MPM-1-Br | Exp. | Simulation | Exp. | Simulation | |||
Model | CO | CO | TraPPE | C | C | ||
21.61 | 25.02 | 25.57 | 25.40 | 25.05 | 30.61 | 27.55 | |
0.05 atm loading, 273 K | 0.19 | 0.23 | 0.33 | 0.51 | 0.81 | 1.41 | 1.23 |
1.0 atm loading, 273 K | 2.54 | 2.52 | 3.20 | 3.77 | 3.59 | 3.69 | 3.66 |
0.05 atm loading, 298 K | 0.12 | 0.11 | 0.13 | 0.20 | 0.43 | 0.34 | 0.22 |
1.0 atm loading, 298 K | 1.56 | 1.52 | 1.80 | 2.47 | 2.82 | 3.31 | 3.30 |
MPM-1-Cl | Binding energy (kJ mol | Steps ( | Final Temp. (K) |
CO | 2.37 | 113 | |
C | 2.46 | 125 | |
MPM-1-Br | |||
CO | 3.24 | 129 | |
C | 4.53 | 150 |