Gliomas are common malignant tumors of the central nervous system. Despite the surgical resection and postoperative radiotherapy and chemotherapy, the prognosis of glioma remains poor. Therefore, it is important to reveal the molecular mechanisms that promotes glioma progression. Microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to identify 428 differentially expressed genes (DEGs) and a core module from three microarray datasets. Heat maps were drawn based on DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the DAVID database. The core module was significantly involved in several KEGG pathways, such as "cell cycle", "viral carcinogenesis", "progesterone-mediated oocyte maturation", "p53 signaling pathway". The protein-protein interaction (PPI) networks and modules were built using the STRING database and the MCODE plugin, respectively, which were visualized using Cytoscape software. Identification of hub genes in the core module using the CytoHubba plugin. The top modular genes AURKA, CDC20, CDK1, CENPF, and TOP2A were associated with glioma development and prognosis. In the Human Protein Atlas (HPA) database, CDC20, CENPF and TOP2A have significant protein expression. Univariate and multivariate cox regression analysis showed that only CENPF had independent influencing factors in the CGGA database. GSEA analysis found that CENPF was significantly enriched in the cell cycle, P53 signaling pathway, MAPK signaling pathway, DNA replication, spliceosome, ubiquitin-mediated proteolysis, focal adhesion, pathway in cancer, glioma, which was highly consistent with previous studies. Our study revealed a core module that was highly correlated with glioma development. The key gene CENPF and signaling pathways were identified through a series of bioinformatics analysis. CENPF was identified as a candidate biomarker molecule.
Citation: Moxuan Zhang, Quan Zhang, Jilin Bai, Zhiming Zhao, Jian Zhang. Transcriptome analysis revealed CENPF associated with glioma prognosis[J]. Mathematical Biosciences and Engineering, 2021, 18(3): 2077-2096. doi: 10.3934/mbe.2021107
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Gliomas are common malignant tumors of the central nervous system. Despite the surgical resection and postoperative radiotherapy and chemotherapy, the prognosis of glioma remains poor. Therefore, it is important to reveal the molecular mechanisms that promotes glioma progression. Microarray datasets were obtained from the Gene Expression Omnibus (GEO) database. The GEO2R tool was used to identify 428 differentially expressed genes (DEGs) and a core module from three microarray datasets. Heat maps were drawn based on DEGs. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment analysis were performed using the DAVID database. The core module was significantly involved in several KEGG pathways, such as "cell cycle", "viral carcinogenesis", "progesterone-mediated oocyte maturation", "p53 signaling pathway". The protein-protein interaction (PPI) networks and modules were built using the STRING database and the MCODE plugin, respectively, which were visualized using Cytoscape software. Identification of hub genes in the core module using the CytoHubba plugin. The top modular genes AURKA, CDC20, CDK1, CENPF, and TOP2A were associated with glioma development and prognosis. In the Human Protein Atlas (HPA) database, CDC20, CENPF and TOP2A have significant protein expression. Univariate and multivariate cox regression analysis showed that only CENPF had independent influencing factors in the CGGA database. GSEA analysis found that CENPF was significantly enriched in the cell cycle, P53 signaling pathway, MAPK signaling pathway, DNA replication, spliceosome, ubiquitin-mediated proteolysis, focal adhesion, pathway in cancer, glioma, which was highly consistent with previous studies. Our study revealed a core module that was highly correlated with glioma development. The key gene CENPF and signaling pathways were identified through a series of bioinformatics analysis. CENPF was identified as a candidate biomarker molecule.
Fractional differential equations (FDEs) appeared as an excellent mathematical tool for, modeling of many physical phenomena appearing in various branches of science and engineering, such as viscoelasticity, statistical mechanics, dynamics of particles, etc. Fractional calculus is a recently developing work in mathematics which studies derivatives and integrals of functions of fractional order [26].
The most used fractional derivatives are the Riemann-Liouville (RL) and Caputo derivatives. These derivatives contain a non-singular derivatives but still conserves the most important peculiarity of the fractional operators [1,2,10,11,23,24]. Atangana and Baleanu described a derivative with a generalized Mittag-leffler (ML) function. This derivative is often called the Atangana-Baleanu (AB) fractional derivative. The AB-derivative in the senses of Riemman-Liouville and Caputo are denoted by ABR-derivative and ABC-derivative, respectively.
The AB fractional derivative is a nonlocal fractional derivative with nonsingular kernel which is connected with various applications [3,5,6,8,9,13,14,15,16]. Using the advantage of the non-singular ML kernal present in the AB fractional derivatives, operators, many authors from various branches of applied mathematics have developed and studied mathematical models involving AB fractional derivatives [18,22,29,30,31,32,35,36,37].
Mohamed et al. [25] considered a system of multi-derivatives for Caputo FDEs with an initial value problem, examined the existence and uniqueness results and obtained numerical results. Sutar et al. [32,33] considered multi-derivative FDEs involving the ABR derivative and examined existence, uniqueness and dependence results. Kucche et al. [12,19,20,21,34] enlarged the work of multi-derivative fractional differential equations involving the Caputo fractional derivative and studied the existence, uniqueness and continuous dependence of the solution.
Inspired by the preceding work, we perceive the multi-derivative nonlinear neutral fractional integro-differential equation with AB fractional derivative of the Riemann-Liouville sense of the problem:
dVdȷ+⋆0Dδȷ[V(ȷ)−x(ȷ,y(ȷ))]=φ(ȷ,V(ȷ),∫ȷ0K(ȷ,θ,V(θ))dθ,∫T0χ(ȷ,θ,V(θ))dθ),ȷ∈I | (1.1) |
V(0)=V0∈R, | (1.2) |
where ⋆0Dδȷ denotes the ABR fractional derivative of order δ∈(0,1), and φ∈C(I×R×R×R,R) is a non-linear function. Let P1V(ȷ)=∫ȷ0K(ȷ,θ,V(θ))dθ and P2V(ȷ)=∫T0χ(ȷ,θ,V(θ))dθ. Now, (1.1) becomes,
dVdȷ+⋆0Dδȷ[V(ȷ)−x(ȷ,y(ȷ))]=φ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ)),ȷ∈I, | (1.3) |
V(0)=V0∈R. | (1.4) |
In this work, we derive a few supplemental results using the characteristics of the fractional integral operator εαδ,η,V;c+. The existence results are obtained by Krasnoselskii's fixed point theorem and the uniqueness and data dependence results are obtained by the Gronwall-Bellman inequality.
Definition 2.1. [14] The Sobolev space Hq(X) is defined as Hq(X)={φ∈L2(X):Dβφ∈L2(X),∀|β|≤q}. Let q∈[1,∞) and X be open, X⊂R.
Definition 2.2. [11,17] The generalized ML function Eαδ,β(u) for complex δ,β,α with Re(δ)>0 is defined by
Eαδ,β(u)=∞∑t=0(α)tα(δt+β)utt!, |
and the Pochhammer symbol is (α)t, where (α)0=1,(α)t=α(α+1)...(α+t−1), t=1,2...., and E1δ,β(u)=Eδ,β(u),E1δ,1(u)=Eδ(u).
Definition 2.3. [4] The ABR fractional derivative of V of order δ is
⋆0Dδȷ[V(ȷ)−x(ȷ,y(ȷ))]=B(δ)1−δddȷ∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ]V(θ)dθ, |
where V∈H1(0,1), δ∈(0,1), B(δ)>0. Here, Eδ is a one parameter ML function, which shows B(0)=B(1)=1.
Definition 2.4. [4] The ABC fractional derivative of V of order δ is
⋆0Dδȷ[V(ȷ)−x(ȷ,y(ȷ))]=B(δ)1−δ∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ]V′(θ)dθ, |
where V∈H1(0,1), δ∈(0,1), and B(δ)>0. Here, Eδ is a one parameter ML function, which shows B(0)=B(1)=1.
Lemma 2.5. [4] If L{g(ȷ);b}=ˉG(b), then L{⋆0Dδȷg(ȷ);b}=B(δ)1−δbδˉG(b)bδ+δ1−δ.
Lemma 2.6. [26] L[ȷmδ+β−1E(m)δ,β(±aȷδ);b]=m!bδ−β(bδ±a)m+1,Em(ȷ)=dmdȷmE(ȷ).
Definition 2.7. [17,27] The operator εαδ,η,V;c+ on class L(m,n) is
(εαδ,η,V;c+)[V(ȷ)−x(ȷ,y(ȷ))]=∫t0(ȷ−θ)α−1Eαδ,η[V(ȷ−θ)δ]Θ(θ)dθ,ȷ∈[c,d], |
where δ,η,V,α∈C(Re(δ),Re(η)>0), and n>m.
Lemma 2.8. [17,27] The operator εαδ,η,V;c+ is bounded on C[m,n], such that ‖(εαδ,η,V;c+)[V(ȷ)−x(ȷ,y(ȷ))]‖≤P‖Θ‖, where
P=(n−m)Re(η)∞∑t=0|(α)t||α(δt+η)|[Re(δ)t+Re(η)]|V(n−m)Re(δ)|tt!. |
Here, δ,η,V,α∈C(Re(δ),Re(η)>0), and n>m.
Lemma 2.9. [17,27] The operator εαδ,η,V;c+ is invertible in the space L(m,n) and φ∈L(m,n) its left inversion is given by
([εαδ,η,V;c+]−1)[V(ȷ)−x(ȷ,y(ȷ))]=(Dη+ςc+ε−αδ,η,V;c+)[V(ȷ)−x(ȷ,y(ȷ))],ȷ∈(m,n], |
where δ,η,V,α∈C(Re(δ),Re(η)>0), and n>m.
Lemma 2.10. [17,27] Let δ,η,V,α∈C(Re(δ),Re(η)>0),n>m and suppose that the integral equation is
∫ȷ0(ȷ−θ)α−1Eαδ,η[V(ȷ−θ)δ]Θ(θ)dθ=φ(ȷ),ȷ∈(m,n], |
is solvable in the space L(m,n).Then, its unique solution Θ(ȷ) is given by
Θ(ȷ)=(Dη+ςc+ε−αδ,η,V;c+)[V(ȷ)−x(ȷ,y(ȷ))],ȷ∈(m,n]. |
Lemma 2.11. [7] (Krasnoselskii's fixed point theorem) Let A be a Banach space and X be bounded, closed, convex subset of A. Let F1,F2 be maps of S into A such that F1V+F2φ∈X ∀ V,φ∈U. The equation F1V+F2V=V has a solution on S, and F1, F2 is a contraction and completely continuous.
Lemma 2.12. [28] (Gronwall-Bellman inequality) Let V and φ be continuous and non-negative functions defined on I. Let V(ȷ)≤A+∫ȷaφ(θ)V(θ)dθ,ȷ∈I; here, A is a non-negative constant.
V(ȷ)≤Aexp(∫ȷaφ(θ)dθ),ȷ∈I. |
In this part, we need some fixed-point-techniques-based hypotheses for the results:
(H1) Let V∈C[0,T], function φ∈(C[0,T]×R×R×R,R) is a continuous function, and there exist +ve constants ζ1,ζ2 and ζ. ‖φ(ȷ,V1,V2,V3)−φ(ȷ,φ1,φ2,φ3)‖≤ζ1(‖V1−φ1‖+‖V2−φ2‖+‖V3−φ3‖) for all V1,V2,V3,φ1,φ2,φ3 in Y, ζ2=maxV∈R‖f(ȷ,0,0,0)‖, and ζ=max{ζ1,ζ2}.
(H2) P1 is a continuous function, and there exist +ve constants C1,C2 and C. ‖P1(ȷ,θ,V1)−P1(ȷ,θ,φ1)‖≤C1(‖V1−φ1‖)∀V1,φ1 in Y, C2=max(ȷ,θ)∈D‖P1(ȷ,θ,0)‖, and C=max{C1,C2}.
(H3) P2 is a continuous function and there are +ve constants D1,D2 and D. ‖P2(ȷ,θ,V1)−P2(ȷ,θ,φ1)‖≤D1(‖V1−φ1‖) for all V1,φ1 in Y, D2=max(ȷ,θ)∈D‖P2(ȷ,θ,0)‖ and D=max{D1,D2}.
(H4) Let x∈c[0,I], function u∈(c[0,I]×R,R) is a continuous function, and there is a +ve constant k>0, such that ‖u(ȷ,x)−u(ȷ,y)‖≤k‖x−y‖. Let Y=C[R,X] be the set of continuous functions on R with values in the Banach space X.
Lemma 2.13. If (H2) and (H3) are satisfied the following estimates, ‖P1V(ȷ)‖≤ȷ(C1‖V‖+C2),‖P1V(ȷ)−P1φ(ȷ)‖≤Cȷ‖V−φ‖, and ‖P2V(ȷ)‖≤ȷ(D1‖V‖+D2),‖P2V(ȷ)−P2φ(ȷ)‖≤Dȷ‖V−φ‖.
Theorem 3.1. The function φ∈C(I×R×R×R,R) and V∈C(I) is a solution for the problem of Eqs (1.3) and (1.4), iff V is a solution of the fractional equation
V(ȷ)=V0−B(δ)1−δ∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ][V(ȷ)−x(ȷ,y(ȷ))]dθ+∫ȷ0φ(θ,V(θ),P1V(θ),P2V(θ))dθ,ȷ∈I. | (3.1) |
Proof. (1) By using Definition 2.3 and Eq (1.3), we get
ddȷ(V(ȷ)+B(δ)1−δ∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ][V(ȷ)−x(ȷ,y(ȷ))]dθ)=φ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ)). |
Integrating both sides of the above equation with limits 0 to ȷ, we get
V(ȷ)+B(δ)1−δ∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ][V(ȷ)−x(ȷ,y(ȷ))]dθ−V(0)=∫ȷ0φ(θ,V(θ),P1V(θ),P2V(θ))dθ,ȷ∈I. |
Conversely, with differentiation on both sides of Eq (3.1) with respect to ȷ, we get
dVdȷ+B(δ)1−δddȷ∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ][V(ȷ)−x(ȷ,y(ȷ))]dθ=φ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ)),ȷ∈I. |
Using Definition 2.3, we get Eq (1.3) and substitute ȷ=0 in Eq (3.1), we get Eq (1.4).
Proof. (2) In Equation (1.3), taking the Laplace Transform on both sides, we get
L[V′(ȷ);b]+L[⋆0Dδȷ;b][V(ȷ)−x(ȷ,y(ȷ))]=L[φ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ));b]. |
Now, using the Laplace Transform formula for the AB fractional derivative of the RL sense, as given in Lemma 2.5, we get
bˉX(b)−[V(ȷ)−x(ȷ,y(ȷ))]−V(0)+B(δ)1−δbδˉX(b)bδ+δ1−δ=ˉG(b), |
ˉX(b)=[V(ȷ);b] and ˉG(b)=L[φ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ));b]. Using Eq (1.4), we get
ˉX(b)=V01b−B(δ)1−δbδ−1ˉX(b)bδ+δ1−δ[V(ȷ)−x(ȷ,y(ȷ))]+1bˉG(b). | (3.2) |
In Eq (3.2) applying the inverse Laplace Transform on both sides using Lemma 2.6 and the convolution theorem, we get
L−1[ˉX(b);ȷ]=V0L−1[1b;ȷ]−B(δ)1−δ(L−1[bδ−1bδ+δ1−δ][V(ȷ)−x(ȷ,y(ȷ))]∗L−1[ˉX(b);ȷ])+L−1[ˉG(b);ȷ]∗L−1[1b;ȷ]=V0−B(δ)1−δ(Eδ[−δ1−δȷδ][V(ȷ)−x(ȷ,y(ȷ))])+φ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ))=V0−B(δ)1−δ∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ][V(ȷ)−x(ȷ,y(ȷ))]dθ+∫ȷ0φ(θ,V(θ),P1V(θ),P2V(θ))dθ.V(ȷ)=V0−B(δ)1−δ∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ][V(ȷ)−x(ȷ,y(ȷ))]dθ+∫ȷ0φ(θ,V(θ),P1V(θ),P2V(θ))dθ. | (3.3) |
Theorem 3.2. Let δ∈(0,1). Define the operator F on C(I):
(FV)(ȷ)=V0−B(δ)1−δ(ε1δ,1,−δ1−δ;0+)[V(ȷ)−x(ȷ,y(ȷ))],V∈C(I). | (3.4) |
(A) F is a bounded linear operator on C(I).
(B) F satisfying the hypotheses.
(C) F(X) is equicontinuous, and X is a bounded subset of C(I).
(D) F is invertible, function φ∈C(I), and the operator equation FV=φ has a unique solution in C(I).
Proof. (A) From Definition 2.7 and Lemma 2.8, the fractional integral operator ε1δ,1,−δ1−δ;0+ is a bounded linear operator on C(I), such that
‖ε1δ,1,−δ1−δ;0+‖‖[V(ȷ)−x(ȷ,y(ȷ))]‖≤P‖V‖,ȷ∈I,where |
P=T∞∑n=0(1)nα(δn+1)(δn+1)|−δ1−δTδ|nn!=T∞∑n=0(δ1−δ)nTδnα(δn+2)=TEδ,2(δ1−δTδ), |
and we have
‖FV‖=|B(δ)1−δ|‖ε1δ,1,−δ1−δ;0+‖‖[V(ȷ)−x(ȷ,y(ȷ))]‖≤PB(δ)1−δ‖V‖,∀V∈C(I). | (3.5) |
Thus, FV=φ is a bounded linear operator on C(I).
(B) We consider V,φ∈C(I). By using linear operator F and bounded operator ε1δ,1,−δ1−δ;0+, for any ȷ∈I,
|(FV)(ȷ)−(Fφ)(ȷ)|=|F(V−φ)[V(ȷ)−x(ȷ,y(ȷ))]|≤B(δ)1−δ‖(ε1δ,1,−δ1−δ;0+V−φ)[V(ȷ)−x(ȷ,y(ȷ))]‖≤PB(δ)1−δ‖V−φ‖. |
Where, P=TEδ,2(δ1−δTδ), then the operator F is satisfied the hypotheses with constant PB(δ)1−δ.
(C) Let U={V∈C(I):‖V‖≤R} be a bounded and closed subset of C(I), V∈U, and ȷ1,ȷ2∈I with ȷ1≤ȷ2.
|(FV)(ȷ1)−(FV)(ȷ2)|=|B(δ)1−δ(ε1δ,1,−δ1−δ;0+)[V(ȷ1)−u(l1,x(l))]−B(δ)1−δ(ε1δ,1,−δ1−δ;0+)[V(ȷ2)−u(l2,x(l))]|≤B(δ)1−δ|∫ȷ10{Eδ[−δ1−δ(ȷ1−θ)δ]−Eδ[−δ1−δ(ȷ2−θ)δ]}[V(ȷ)−x(ȷ,y(ȷ))]dθ|+B(δ)1−δ|∫ȷ2ȷ1Eδ[−δ1−δ(ȷ2−θ)δ][V(ȷ)−x(ȷ,y(ȷ))]dθ|≤B(δ)1−δ∞∑n=0|(−δ1−δ)n|1α(nδ+1)∫ȷ10|(ȷ1−θ)nδ−(ȷ2−θ)nδ||[V(ȷ)−x(ȷ,y(ȷ))]|dθ+B(δ)1−δ∞∑n=0|(−δ1−δ)n|1α(nδ+1)∫ȷ2ȷ1|(ȷ2−θ)nδ||[V(ȷ)−x(ȷ,y(ȷ))]|dθ≤LB(δ)1−δ∞∑n=0(δ1−δ)n1α(nδ+1)∫ȷ10(ȷ2−θ)nδ−(ȷ1−θ)nδdθ+LB(δ)1−δ∞∑n=0(δ1−δ)n1α(nδ+1)∫ȷ2ȷ1(ȷ2−θ)nδdθ≤RB(δ)1−δ∞∑n=0(δ1−δ)n1α(nδ+1){−(ȷ2−ȷ1)nδ+1+ȷnδ+12−ȷnδ+11+(ȷ2−ȷ1)nδ+1}≤RB(δ)1−δ∞∑n=0(δ1−δ)n1α(nδ+2){ȷnδ+12−ȷnδ+11}|(FV)(ȷ1)−(FV)(ȷ2)|≤RB(δ)1−δ∞∑n=0(δ1−δ)n1α(nδ+2){ȷnδ+12−ȷnδ+11}. | (3.6) |
Hence, if |ȷ1−ȷ2|→0 then |(FV)(ȷ1)−(FV)(ȷ2)|→0.
∴ (FV) is equicontinuous on I.
(D) By Lemmas 2.9 and 2.10, φ∈C(I), and we get
(ε1δ,1,−δ1−δ;0+)−1[V(ȷ)−x(ȷ,y(ȷ))]=(D1+β0+ε−1δ,β,−δ1−δ;0+)[V(ȷ)−x(ȷ,y(ȷ))],ȷ∈(m,n). | (3.7) |
By Eqs (3.4) and (3.5), we have
(F−1)[V(ȷ)−x(ȷ,y(ȷ))]=(B(δ)1−δε1δ,1,−δ1−δ;0+)−1[V(ȷ)−x(ȷ,y(ȷ))]=1−δB(δ)(D1+β0+ε−1δ,β,−δ1−δ;0+)[V(ȷ)−x(ȷ,y(ȷ))],ȷ∈(m,n), |
where β∈C with Re(β)>0. This shows F is invertible on C(I) and
(FV)[V(ȷ)−x(ȷ,y(ȷ))]=[V(ȷ)−x(ȷ,y(ȷ))],ȷ∈I, |
has the unique solution,
V(ȷ)=(F−1[V(ȷ)−x(ȷ,y(ȷ))])=1−δB(δ)(D1+β0+ε−1δ,β,−δ1−δ;0+)[V(ȷ)−x(ȷ)),y(ȷ))],ȷ∈(m,n). | (3.8) |
Theorem 4.1. Let φ∈C(I×R×R×R,R). Then, the ABR derivative ⋆0Dδȷ[V(ȷ)−x(ȷ,y(ȷ))]=φ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ)),ȷ∈I, is solvable in C(I), and the solution in C(I) is
V(ȷ)=1−δB(δ)(D1+β0+ε−1δ,β,−δ1−δ;0+)[V(ȷ)−x(ȷ,y(ȷ))],ȷ∈I, | (4.1) |
where β∈C,Re(β)>0, and ˆφ(ȷ)=∫ȷ0φ(θ,V(θ),P1V(θ),P2V(θ))dθ,ȷ∈I.
Proof. The corresponding fractional equation of the ABR derivative
⋆0Dδȷ[V(ȷ)−x(ȷ,y(ȷ))]=φ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ)),ȷ∈I, |
is given by
B(δ)1−δ∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ][V(ȷ)−x(ȷ,y(ȷ))]dθ=∫ȷ0φ(θ,V(θ),P1V(θ),P2V(θ))dθ,ȷ∈I. |
Using operator F of Eq (3.4), we get
(FV)(s)=∫ȷ0φ(θ,V(θ),P1V(θ),P2V(θ))dθ=ˆφ(ȷ),ȷ∈I. | (4.2) |
Equations (3.7) and (4.2) are solvable, and we get
V(ȷ)=1−δB(δ)(D1+β0+ε−1δ,β,−δ1−δ;0+)[V(ȷ)−x(ȷ,y(ȷ))],ȷ∈I;β∈C,Re(β)>0. | (4.3) |
Theorem 4.2. Let φ∈C(I×R×R×R,R) satisfy (H1)–(H3) with L=supȷ∈Iω(ȷ), where ω(ȷ)=ζ(1+Cȷ+DT), if L=min{1,12T}. Then problem of (1.3) and (1.4) has a solution in C(I) provided
2B(δ)TEδ,2(δ1−δ)Tδ1−δ≤1. | (4.4) |
Proof. Define
R=‖V0‖+NφT1−LT−B(δ)TEδ,2(δ1−δ)Tδ1−δ, |
where Nφ=supȷ∈I‖φ(ȷ,0,0,0)‖. Let U={V∈C(I):‖V‖≤R}. Consider F1:X→A and F2:X→A given as
(F1V)(ȷ)=V0+∫ȷ0φ(θ,V(θ),P1V(θ),P2V(θ))dθ,ȷ∈I,(F2V)(ȷ)=−(F)[V(ȷ)−x(ȷ,y(ȷ))],ȷ∈I. |
Let V=F1V+F2V,V∈C(I) is the fractional Eq (3.1) to the problems (1.3) and (1.4).
Hence, the operators F1 and F2 satisfy the Krasnoselskii's fixed point theorem.
Step (ⅰ) F1 is a contraction.
By (H1)–(H3) on φ, ∀ V,φ∈C(I) and ȷ∈I,
|F1V(ȷ)−F2φ(ȷ)|≤ω(ȷ)|V(ȷ)−φ(ȷ)|≤R‖V−φ‖. | (4.5) |
This gives, ‖F1V−F2φ‖≤RT‖V−φ‖,V,φ∈C(I).
Step (ⅱ) F2 is completely continuous. By using Theorem 3.3 and Ascoli-Arzela theorem, F2=−F is completely continuous.
Step (ⅲ) F1V+F2φ∈U, for any V,φ∈U, using Theorem 3.3, we obtain
‖(F1V+F2φ)(ȷ)‖≤‖(F1V)(ȷ)‖+‖(F2φ)(ȷ)‖≤‖V0‖+∫ȷ0‖φ(θ,V(θ),P1V(θ),P2V(θ))‖dθ+‖ε1δ,1,−δ1−δ;0+φ‖≤‖V0‖+∫ȷ0‖φ(θ,V(θ),P1V(θ),P2V(θ))‖dθ+B(δ)1−δTEδ,2(δ1−δTδ)‖φ‖≤‖V0‖+∫ȷ0‖φ(θ,V(θ),P1V(θ),P2V(θ))−φ(θ,0,0,0)‖dθ+∫ȷ0‖φ(θ,0,0,0)‖dθ+B(δ)1−δTEδ,2(δ1−δTδ)L≤‖V0‖+∫ȷ0ζ(‖V‖+Cȷ‖V‖+DT‖V‖)dθ+Nφ∫ȷ0dθ+B(δ)1−δTEδ,2(δ1−δTδ)L≤‖V0‖+ζ(1+Cȷ+DT)∫ȷ0‖V‖dθ+Nφ∫ȷ0dθ+B(δ)1−δTEδ,2(δ1−δTδ)L≤‖V0‖+ω(ȷ)R∫ȷ0dθ+Nφ∫ȷ0dθ+B(δ)1−δTEδ,2(δ1−δTδ)L≤‖V0‖+LRT+NφT+B(δ)1−δTEδ,2(δ1−δTδ)L. | (4.6) |
By definition of R, we get
‖V0‖+NφT=L(1−RT+B(δ)TEδ,2(δ1−δTδ)1−δ). | (4.7) |
Using the Eq (4.5) in (4.7), we get condition of Eq (4.4).
‖(F1V+F2φ)(ȷ)‖≤L(2B(δ)TEδ,2(δ1−δ)Tδ1−δ),ȷ∈I. | (4.8) |
∴‖(F1V+F2φ)(ȷ)‖≤L,ȷ∈I. This gives, F1V+F2φ∈U, ∀V,φ∈X.
From Steps (ⅰ)–(ⅲ), all the conditions of Lemma 2.11 follow.
Theorem 4.3. By Theorem 4.2, the Eqs (1.3) and (1.4) have a unique solution in C(I).
Proof. (1) The problems (1.3) and (1.4) have an operator equation form as:
(ε1δ,1,−δ1−δ;0+)[V(ȷ)−x(ȷ,y(ȷ))]=ˆφ(ȷ),ȷ∈I, | (4.9) |
where,
ˆφ(ȷ)=1−δB(δ)(V0−V(ȷ)+∫ȷ0φ(θ,V(θ),P1V(θ),P2V(θ))dθ),ȷ∈I. |
By Theorem 4.2, Eq (4.7) is solvable in C(I), by Lemma 2.10 we get a unique solution of Eqs (1.3) and (1.4),
V(ȷ)=(D1+β0+ε−1δ,β,−δ1−δ;0+)[V(ȷ)−x(ȷ,y(ȷ))],V∈C(I). |
Proof. (2) Let V,φ be solutions of Eqs (1.3) and (1.4). By fractional integral operators and (H1)–(H3), we find, for any ȷ∈I,
|V(ȷ)−φ(ȷ)|≤|B(δ)1−δ(ε1δ,1,−δ1−δ;0+(V−φ))[V(ȷ)−x(ȷ,y(ȷ))]|+∫ȷ0|φ(θ,V(θ),P1V(θ),P2V(θ))−φ(θ,φ(θ),P1φ(θ),P2φ(θ))|dθ≤|B(δ)1−δ∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ](V(θ)−φ(θ))dθ|+∫ȷ0ζ(|V(θ)−φ(θ)|+C|V(θ)−φ(θ)|+D|V(θ)−φ(θ)|)dθ≤B(δ)1−δ∫ȷ0Eδ(|−δ1−δTδ|)|V(θ)−φ(θ)|dθ+∫ȷ0ζ(1+C+D)|V(θ)−φ(θ)|dθ≤B(δ)1−δ∫ȷ0Eδ(δ1−δTδ)|V(θ)−φ(θ)|dθ+∫ȷ0[V(ȷ)−x(ȷ,y(ȷ))]|V(θ)−φ(θ)|dθ≤∫ȷ0[B(δ)1−δEδ(δ1−δTδ)+[V(ȷ)−x(ȷ,y(ȷ))]]|V(θ)−φ(θ)|dθ|V(ȷ)−φ(ȷ)|≤∫ȷ0[B(δ)1−δEδ(δ1−δTδ)+[V(ȷ)−x(ȷ,y(ȷ))]]|V(θ)−φ(θ)|dθ. | (4.10) |
Theorem 5.1. By Theorem 4.2, if V(ȷ) is a solution of Eqs (1.3) and (1.4), then
|V(ȷ)|≤{|V0|+NφT}exp(∫ȷ0[B(δ)1−δEδ(δ1−δTδ)+[V(ȷ)−x(ȷ,y(ȷ))]]dθ),ȷ∈I, | (5.1) |
where, Nφ=supȷ∈I|φ(ȷ,0,0,0)|.
Proof. If V(ȷ) is a solution of Eqs (1.3) and (1.4), for all ȷ∈I,
|V(ȷ)|≤|V0|−B(δ)1−δ∫ȷ0Eδ(|−δ1−δ(ȷ−θ)δ|)|V(θ)|dθ+∫ȷ0|φ(θ,V(θ),P1V(θ),P2V(θ))|dθ |
≤|V0|−B(δ)1−δ∫ȷ0Eδ(δ1−δ(ȷ−θ)δ)|V(θ)|dθ+∫ȷ0|φ(θ,V(θ),P1V(θ),P2V(θ))−φ(θ,0,0,0)|dθ+∫ȷ0|φ(θ,0,0,0)|dθ≤|V0|−B(δ)1−δ∫ȷ0Eδ(δ1−δTδ)|V(θ)|dθ+∫ȷ0ζ(|V(θ)|+C|V(θ)|+D|V(θ)|)dθ+Nφȷ≤|V0|−B(δ)1−δ∫ȷ0Eδ(δ1−δTδ)|V(θ)|dθ+∫ȷ0ζ(1+C+D)|V(ȷ)|dθ+NφT≤|V0|−B(δ)1−δ∫ȷ0Eδ(δ1−δTδ)|V(θ)|dθ+∫ȷ0[V(ȷ)−x(ȷ,y(ȷ))]|V(θ)|dθ+NφT≤{|V0|+NφT}+∫ȷ0[B(δ)1−δEδ(δ1−δTδ)+[V(ȷ)−x(ȷ,y(ȷ))]]|V(θ)|dθ. |
By Lemma 2.12, we get
|V(ȷ)|≤{|V0|+NφT}exp(∫ȷ0[B(δ)1−δEδ(δ1−δTδ)+[V(ȷ)−x(ȷ,y(ȷ))]]dθ),ȷ∈I. | (5.2) |
We discuss data dependence results for the problem
dφdȷ+⋆0Dδȷ[V(ȷ)−x(ȷ,y(ȷ))]=˜φ(ȷ,φ(ȷ),P1φ(ȷ),P2φ(ȷ)),ȷ∈I, | (6.1) |
φ(0)=φ0∈R. | (6.2) |
Theorem 6.1. Equation (4.2) holds, and ξk>0, where k=1,2 are real numbers such that,
|V0−φ0|≤ξ1,|φ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ))−˜φ(ȷ,φ(ȷ),P1φ(ȷ),P2φ(ȷ))|≤ξ2,ȷ∈I. | (6.3) |
φ(ȷ) is a solution of ABR fractional derivative Eqs (6.1) and (6.2), and V(ȷ) is a solution of Eqs (1.3) and (1.4).
Proof. Let V,φ are the solution of Eqs (1.3) and (1.4), (6.1) and (6.2) respectively. We find for any
|V(ȷ)−φ(ȷ)|≤|V0−φ0|+B(δ)1−δ∫ȷ0Eδ(|−δ1−δ(ȷ−θ)δ|)|V(θ)−φ(θ)|dθ+∫ȷ0|φ(θ,V(θ),P1V(θ),P2V(θ))−˜φ(s,φ(θ),P1φ(θ),P2φ(θ))|dθ≤|V0−φ0|+B(δ)1−δ∫ȷ0Eδ(|−δ1−δ(ȷ−θ)δ|)|V(θ)−φ(s)|dθ+∫ȷ0|φ(θ,V(θ),P1V(θ),P2V(θ))−φ(θ,φ(θ),P1φ(θ),P2φ(θ))|dθ+∫ȷ0|φ(θ,V(θ),P1V(θ),P2V(θ))−˜φ(θ,φ(θ),P1φ(θ),P2φ(θ))|dθ≤ξ1+B(δ)1−δ∫ȷ0Eδ(δ1−δTδ)|V(θ)−φ(θ)|dθ+∫ȷ0ζ(|V(θ)−φ(θ)|+C|V(θ)−φ(θ)|+D|V(θ)−φ(θ)|)dθ+ξ2∫ȷ0dθ≤ξ1+B(δ)1−δ∫ȷ0Eδ(δ1−δTδ)|V(θ)−φ(θ)|dθ+∫ȷ0ζ(1+C+D)|V(θ)−φ(θ)|dθ+ξ2ȷ≤ξ1+B(δ)1−δ∫ȷ0Eδ(δ1−δTδ)|V(θ)−φ(θ)|dθ+∫ȷ0[V(ȷ)−x(ȷ,y(ȷ))]|V(θ)−φ(θ)|dθ+ξ2T|V(ȷ)−φ(ȷ)|≤ξ1+ξ2T+∫ȷ0[B(δ)1−δEδ(δ1−δTδ)+[V(ȷ)−x(ȷ,y(ȷ))]]|V(ȷ)−φ(θ)|dθ. |
By Lemma 2.12, we get
|V(ȷ)−φ(ȷ)|≤(ξ1+ξ2T)exp(∫ȷ0[B(δ)1−δEδ(δ1−δTδ)+[V(ȷ)−x(ȷ,y(ȷ))]]dθ),ȷ∈I. | (6.4) |
Let any λ,λ0∈R and
dVdȷ+⋆0Dδȷ[V(ȷ)−x(ȷ,y(ȷ))]=Θ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ),λ),ȷ∈I, | (7.1) |
V(0)=V0∈R. | (7.2) |
dVdȷ+⋆0Dδȷ[V(ȷ)−x(ȷ,y(ȷ)]=Θ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ),λ0),ȷ∈I, | (7.3) |
V(0)=V0∈R. | (7.4) |
Theorem 7.1. Let the function Θ satisfy Theorem 4.2. Suppose there exists ω,u∈C(I,R+) such that,
|Θ(ȷ,V,P1V,P2V,λ)−Θ(ȷ,φ,P1φ,P2φ,λ)|≤ω(ȷ)|V−φ|,|Θ(ȷ,V,P1V,P2V,λ)−Θ(ȷ,V,P1V,P2V,λ0)|≤u(ȷ)|λ−λ0|. |
If V1,V2 are the solutions of Eqs (7.1) and (7.3), then
|V1(ȷ)−V2(ȷ)|≤PT|λ−λ0|exp(∫ȷ0[B(δ)1−δEδ(−δ1−δTδ)+[V(ȷ)−x(ȷ,y(ȷ))]]dθ),ȷ∈I, | (7.5) |
where P=supȷ∈Iu(ȷ).
Proof. Let, for any ȷ∈I,
|V1(ȷ)−V2(ȷ)|≤B(δ)1−δ|∫ȷ0Eδ[−δ1−δ(ȷ−θ)δ](V2(θ)−V1(θ)dθ)|+∫ȷ0|Θ(θ,V1(θ),P1V1(θ),P2V1(θ),λ)−Θ(θ,V2(θ),P1V2(θ),P2V2(θ),λ0)|dθ≤B(δ)1−δ∫ȷ0Eδ(|−δ1−δ(ȷ−θ)δ|)|V1(θ)−V2(θ)|dθ+∫ȷ0|Θ(θ,V1(θ),P1V1(θ),P2V1(θ),λ)−Θ(θ,V2(θ),P1V2(θ),P2V2(θ),λ)|dθ+∫ȷ0|Θ(θ,V2(θ),P1V2(θ),P2V2(θ),λ)−Θ(θ,V2(θ),P1V2(θ),P2V2(θ),λ0)|dθ≤B(δ)1−δ∫ȷ0Eδ(δ1−δ(ȷ−θ)δ)|V1(θ)−V2(θ)|dθ+∫ȷ0ζ(|V1(θ)−V2(θ)|+C|V1(θ)−V2(θ)|+D|V1(θ)−V2(θ)|)dθ+∫ȷ0u(θ)|λ−λ0|dθ≤B(δ)1−δ∫ȷ0Eδ(δ1−δTδ)|V1(θ)−V2(θ)|dθ+∫ȷ0ζ(1+C+D)|V1(θ)−V2(θ)|dθ+Pȷ|λ−λ0|≤B(δ)1−δ∫ȷ0Eδ(δ1−δTδ)|V1(θ)−V2(θ)|dθ+∫ȷ0[V(ȷ)−x(ȷ,y(ȷ))]|V1(θ)−V2(θ)|dθ+PT|λ−λ0|≤∫ȷ0[B(δ)1−δEδ(δ1−δTδ)+[V(ȷ)−x(ȷ,y(ȷ))]]|V1(θ)−V2(θ)|dθ+PT|λ−λ0|. |
By Lemma 2.12,
|V1(ȷ)−V2(ȷ)|≤PT|λ−λ0|exp(∫ȷ0[B(δ)1−δEδ(δ1−δTδ)+[V(ȷ)−x(ȷ,y(ȷ))]]dθ),ȷ∈I. | (7.6) |
Consider a nonlinear ABR fractional derivative with neutral integro-differential equations of the form:
dVdȷ+⋆0D12ȷ[V(ȷ)−x(ȷ,y(ȷ))]=φ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ)),ȷ∈I=[0,2], | (8.1) |
V(0)=1∈R. | (8.2) |
φ:(I×R×R×R)→R is a continuous nonlinear function such that,
φ(ȷ,V(ȷ),P1V(ȷ),P2V(ȷ))=|V(ȷ)|+13+M(ȷ)+N(ȷ),ȷ∈I, |
and
M(ȷ)=B(12){ȷE12,2(−ȷ12)+E12(−ȷ12)−ȷ−1},N(ȷ)=B(12){E12,2(−ȷ12)+ȷE12(−ȷ12)−1}. |
We observe that for all V,φ∈R and ȷ∈I,
|φ(ȷ,V,P1V,P2V)−φ(ȷ,φ,P1φ,P2φ)|=|(|V(ȷ)|+13+M(ȷ)+N(ȷ))−(|φ(ȷ)|+13+M(ȷ)+N(ȷ))|≤13|V−φ|. | (8.3) |
The function φ satisfies (H1)–(H4) with constant 13. From Theorem 4.2, we have δ=12 and T = 2 which is substitute in Eq (4.2), and we get
B(12)<18E12,2(212). | (8.4) |
If the function B(δ) satisfies Eq (8.4), then Eqs (8.1) and (8.2) have a unique solution.
V(ȷ)=ȷ3+1,ȷ∈[0,2]. | (8.5) |
In this research article, we explored multi-derivative nonlinear neutral fractional integro-differential equations involving the ABR fractional derivative. The elementary results of the existence, uniqueness and dependence solution on various data are based on the Prabhakar fractional integral operator εαδ,η,V;c+ involving a generalized ML function. The existence results are obtained by Krasnoselskii's fixed point theorem, and the uniqueness and data dependence results are obtained by the Gronwall-Bellman inequality with continuous functions.
The research on Existence and data dependence results for neutral fractional order integro-differential equations by Khon Kaen University has received funding support from the National Science, Research and Innovation Fund.
The authors declare no conflict of interest.
[1] |
M. Qian, S. Wang, X. Guo, J. Wang, Z. Zhang, W. Qiu, et al., Hypoxic glioma-derived exosomes deliver microRNA-1246 to induce M2 macrophage polarization by targeting TERF2IP via the STAT3 and NF-κB pathways, Oncogene, 39 (2020), 428-442. doi: 10.1038/s41388-019-0996-y
![]() |
[2] |
Z. P. Wen, W. J. Zeng, Y. H. Chen, H. Li, J. Y. Wang, Q. Cheng, et al., Knockdown ATG4C inhibits gliomas progression and promotes temozolomide chemosensitivity by suppressing autophagic flux, J. Exp. Clin. Cancer Res., 38 (2019), 1-15. doi: 10.1186/s13046-018-1018-6
![]() |
[3] |
X. Chen, M. Zhang, H. Gan, H. Wang, J. H. Lee, D. Fang, et al., A novel enhancer regulates MGMT expression and promotes temozolomide resistance in glioblastoma, Nat. Commun., 9 (2018), 1-14. doi: 10.1038/s41467-017-02088-w
![]() |
[4] |
Y. Chen, P. Liu, P. Sun, J. Jiang, Y. Zhu, T. Dong, et al., Oncogenic MSH6-CXCR4-TGFB1 feedback loop: a novel therapeutic target of photothermal therapy in glioblastoma multiforme, Theranostics, 9 (2019), 1453-1473. doi: 10.7150/thno.29987
![]() |
[5] | T. Liu, A. Li, Y. Xu, Y. Xin, Momelotinib sensitizes glioblastoma cells to temozolomide by enhancement of autophagy via JAK2/STAT3 inhibition, Oncol. Rep., 41 (2019), 1883-1892. |
[6] |
J. Ruiz, D. Case, G. Enevold, R. Rosdhal, S. B. Tatter, T.L. Ellis, et al., A phase Ⅱ trial of thalidomide and procarbazine in adult patients with recurrent or progressive malignant gliomas, J. Neurooncol, 106 (2012), 611-617. doi: 10.1007/s11060-011-0698-y
![]() |
[7] |
M. Nagane, R. Nishikawa, Y. Narita, H. Kobayashi, S. Takano, N. Shinoura, et al., Phase Ⅱ study of single-agent bevacizumab in Japanese patients with recurrent malignant glioma, Jpn. J. Clin. Oncol., 42 (2012) 887-895. doi: 10.1093/jjco/hys121
![]() |
[8] |
T. Jiang, Y. Mao, W. Ma, Q. Mao, Y. You, X. Yang, et al., CGCG clinical practice guidelines for the management of adult diffuse gliomas, Cancer Lett, 375 (2016) 263-273. doi: 10.1016/j.canlet.2016.01.024
![]() |
[9] | Cancer Genome Atlas Research Network, Comprehensive genomic characterization defines human glioblastoma genes and core pathways, Nature, 455 (2008), 1061. |
[10] |
H. Wang, A. K. Diaz, T. I. Shaw, Y. Li, M. Niu, J. H. Cho, et al., Deep multiomics profiling of brain tumors identifies signaling networks downstream of cancer driver genes, Nat. Commun., 10 (2019) 1-15. doi: 10.1038/s41467-018-07882-8
![]() |
[11] |
B. Zhang, Q. Wu, R. Xu, X. Hu, Y. Sun, Q. Wang, et al., The promising novel biomarkers and candidate small molecule drugs in lower-grade glioma: Evidence from bioinformatics analysis of high-throughput data, J. Cell. Biochem., 120 (2019), 15106-15118. doi: 10.1002/jcb.28773
![]() |
[12] |
B. Kamaraj, R. Purohit, Mutational analysis on membrane associated transporter protein (MATP) and their structural consequences in oculocutaeous albinism type 4 (OCA4)-a molecular dynamics approach, J. Cell. Biochem., 117 (2016), 2608-2619. doi: 10.1002/jcb.25555
![]() |
[13] |
X. Fan, L. Shi, H. Fang, Y. Cheng, R. Perkins, W. Tong, DNA microarrays are predictive of cancer prognosis: a re-evaluation, Clin. Cancer Res., 16 (2010), 629-636. doi: 10.1158/1078-0432.CCR-09-1815
![]() |
[14] |
L. Ein-Dor, O. Zuk, E. Domany, Thousands of samples are needed to generate a robust gene list for predicting outcome in cancer, Proc. Natl. Acad. Sci., 103 (2006), 5923-5928. doi: 10.1073/pnas.0601231103
![]() |
[15] |
S. Michiels, S. Koscielny, C. Hill, Prediction of cancer outcome with microarrays: a multiple random validation strategy, Lancet, 365 (2005), 488-492. doi: 10.1016/S0140-6736(05)17866-0
![]() |
[16] |
S. E. Wilhite, T. Barrett, Strategies to explore functional genomics data sets in NCBI's GEO database, Methods Mol. Biol., 802 (2012), 41-53. doi: 10.1007/978-1-61779-400-1_3
![]() |
[17] |
S. Venneti, J. T. Huse, The evolving molecular genetics of low-grade glioma, Adv. Anat. Pathol., 22 (2015), 94-101. doi: 10.1097/PAP.0000000000000049
![]() |
[18] |
F. Wu, R. C. Chai, Z. Wang, Y. Q. Liu, Z. Zhao, G. Z. Li, et al., Molecular classification of IDH-mutant glioblastomas based on gene expression profiles, Carcinogenesis, 40 (2019), 853-860. doi: 10.1093/carcin/bgz032
![]() |
[19] |
J. Lomax, Get ready to GO! A biologist's guide to the Gene Ontology, Briefings Bioinf., 6 (2005), 298-304. doi: 10.1093/bib/6.3.298
![]() |
[20] |
M. Kanehisa, S. Goto, KEGG: kyoto encyclopedia of genes and genomes, Nucleic Acids Res., 28 (2000), 27-30. doi: 10.1093/nar/28.1.27
![]() |
[21] |
L. Slemc, T. Kunej, Transcription factor HIF1A: downstream targets, associated pathways, polymorphic hypoxia response element (HRE) sites, and initiative for standardization of reporting in scientific literature, Tumour Biol., 37 (2016), 14851-14861. doi: 10.1007/s13277-016-5331-4
![]() |
[22] |
M. Kohl, S. Wiese, B. Warscheid, Cytoscape: software for visualization and analysis of biological networks, Methods Mol. Biol., 696 (2011), 291-303. doi: 10.1007/978-1-60761-987-1_18
![]() |
[23] |
C. H. Chin, S. H. Chen, H. H. Wu, C. W. Ho, M. T. Ko, C.Y. Lin, CytoHubba: identifying hub objects and sub-networks from complex interactome, BMC Syst. Biol., 8 (2014), 1-7. doi: 10.1186/1752-0509-8-1
![]() |
[24] |
Z. Tang, C. Li, B. Kang, G. Gao, C. Li, Z. Zhang, GEPIA: a web server for cancer and normal gene expression profiling and interactive analyses, Nucleic Acids Res., 45 (2017), W98-W102. doi: 10.1093/nar/gkx247
![]() |
[25] | M. Uhlen, L. Fagerberg, B. M. Hallstrom, C. Lindskog, P. Oksvold, A. Mardinoglu, Tissue-based map of the human proteome, Science, 347 (2015). |
[26] |
Y. Gusev, K. Bhuvaneshwar, L. Song, J. C. Zenklusen, H. Fine, S. Madhavan, The REMBRANDT study, a large collection of genomic data from brain cancer patients, Sci. Data, 5 (2018), 180158. doi: 10.1038/sdata.2018.158
![]() |
[27] |
A. M. Griesinger, D. K. Birks, A. M. Donson, V. Amani, L. M. Hoffman, A. Waziri, et al., Characterization of distinct immunophenotypes across pediatric brain tumor types, J. Immunol., 191 (2013), 4880–4888. doi: 10.4049/jimmunol.1301966
![]() |
[28] |
B. S. Kruthika, R. Jain, A. Arivazhagan, R. D. Bharath, T. C. Yasha, P. Kondaiah, et al., Transcriptome profiling reveals PDZ binding kinase as a novel biomarker in peritumoral brain zone of glioblastoma, J. Neurooncol., 141 (2019), 315–325. doi: 10.1007/s11060-018-03051-5
![]() |
[29] |
Q. Ma, W. Long, C. Xing, J. Chu, M. Luo, H. Y. Wang, et al., Cancer stem cells and immunosuppressive microenvironment in glioma, Front. Immunol., 9 (2018), 2924. doi: 10.3389/fimmu.2018.02924
![]() |
[30] |
M. K. Kalita, U. K. Nandal, A. Pattnaik, A. Sivalingam, G. Ramasamy, M. Kumar, et al., CyclinPred: a SVM-based method for predicting cyclin protein sequences, PLoS One, 3 (2008), e2605. doi: 10.1371/journal.pone.0002605
![]() |
[31] |
Y. Liao, Y. Feng, J. Shen, F. J. Hornicek, Z. Duan, The roles and therapeutic potential of cyclin-dependent kinases (CDKs) in sarcoma, Cancer Metastasis Rev., 35 (2016), 151-163. doi: 10.1007/s10555-015-9601-1
![]() |
[32] |
F. Brand, A. Forster, A. Christians, M. Bucher, C. M. Thome, M. S. Raab, et al., FOCAD loss impacts microtubule assembly, G2/M progression and patient survival in astrocytic gliomas, Acta Neuropathol., 139 (2020), 175-192. doi: 10.1007/s00401-019-02067-z
![]() |
[33] |
A. C. Cheng, Y. C. Hsu, C. C. Tsai, The effects of cucurbitacin E on GADD45beta-trigger G2/M arrest and JNK-independent pathway in brain cancer cells, J. Cell. Mol. Med., 23 (2019), 3512-3519. doi: 10.1111/jcmm.14250
![]() |
[34] |
N. Liu, G. Hu, H. Wang, Z. Li, Z. Guo, PLK1 inhibitor facilitates the suppressing effect of
temozolomide on human brain glioma stem cells, J. Cell. Mol. Med., 22 (2018), 5300–5310. doi: 10.1111/jcmm.13793
![]() |
[35] | Q. K. Ji, J. W. Ma, R. H. Liu, X. S. Li, F. Z. Shen, L. Y. Huang, et al., CDCA7L promotes glioma proliferation by targeting CCND1 and predicts an unfavorable prognosis, Mol. Med. Rep., 20 (2019), 1149-1156. |
[36] |
T. Yawata, Y. Higashi, Y. Kawanishi, T. Nakajo, N. Fukui, H. Fukuda, et al., CD146 is highly expressed in glioma stem cells and acts as a cell cycle regulator, J. Neurooncol., 144 (2019), 21-32. doi: 10.1007/s11060-019-03200-4
![]() |
[37] |
H. Fan, L. Geng, F. Yang, X. Dong, D. He, Y. Zhang, Ursolic acid derivative induces apoptosis in glioma cells through down-regulation of cAMP, Eur. J. Med. Chem., 176 (2019), 61-67. doi: 10.1016/j.ejmech.2019.04.059
![]() |
[38] |
J. B. Vannier, G. Sarek, S. J. Boulton, RTEL1: functions of a disease-associated helicase, Trends Cell Biol., 24 (2014), 416-425. doi: 10.1016/j.tcb.2014.01.004
![]() |
[39] |
T. Wang, X. Li, S. L. Sun, EX527, a Sirt-1 inhibitor, induces apoptosis in glioma via activating the p53 signaling pathway, Anti-cancer Drugs, 31 (2020), 19-26. doi: 10.1097/CAD.0000000000000824
![]() |
[40] |
L. X. Xu, Z. H. Li, Y. F. Tao, R. H. Li, F. Fang, H. Zhao, et al., Histone acetyltransferase inhibitor Ⅱ induces apoptosis in glioma cell lines via the p53 signaling pathway, J. Exp. Clin. Cancer Res., 33 (2014), 1-15. doi: 10.1186/1756-9966-33-1
![]() |
[41] |
X. L. Zhang, X. T. Ji, B. Sun, L. L. Qian, X. L. Hu, H. X. Lou, et al., Anti-cancer effect of marchantin C via inducing lung cancer cellular senescence associated with less secretory phenotype, Biochim. Biophys. Acta Gen. Subj., 1863 (2019), 1443-1457. doi: 10.1016/j.bbagen.2019.05.006
![]() |
[42] |
J. Song, Q. Ma, M. Hu, D. Qian, B. Wang, N. He, The inhibition of miR-144-3p on cell proliferation and metastasis by targeting TOP2A in HCMV-positive glioblastoma cells, Molecules, 23 (2018), 3259. doi: 10.3390/molecules23123259
![]() |
[43] |
X. Li, E. Martinez-Ledesma, C. Zhang, F. Gao, S. Zheng, J. Ding, et al., TIE2-FGFR1 interaction induces adaptive PI3K inhibitor resistance by upregulating Aurora A/PlK1/CDK1 signaling in glioblastoma, Cancer Res., 79 (2019), 5088-5101. doi: 10.1158/0008-5472.CAN-19-0325
![]() |
[44] |
L. Xu, H. Liu, Z. Yan, Z. Sun, S. Luo, Q. Lu, Inhibition of the Hedgehog signaling pathway suppresses cell proliferation by regulating the Gli2/miR-124/AURKA axis in human glioma cells, Int. J. Oncol., 50 (2017), 1868-1878. doi: 10.3892/ijo.2017.3946
![]() |
[45] |
Y. Ding, S. Yu, Z. Bao, Y. Liu, T. Liang, CDC20 with malignant progression and poor prognosis of astrocytoma revealed by analysis on gene expression, J. Neurooncol., 133 (2017), 87-95. doi: 10.1007/s11060-017-2434-8
![]() |
[46] |
Y. Zhang, J. Li, K. Yi, J. Feng, Z. Cong, Z. Wang, et al., Elevated signature of a gene module coexpressed with CDC20 marks genomic instability in glioma, Proc. Natl. Acad. Sci., 116 (2019), 6975-6984. doi: 10.1073/pnas.1814060116
![]() |
[47] | Z. Song, Y. Pan, G. Ling, S. Wang, M. Huang, X. Jiang, et al., Escape of U251 glioma cells from temozolomide-induced senescence was modulated by CDK1/survivin signaling, Am. J. Transl. Res., 9 (2017), 2163-2180. |
[48] |
S. Deguchi, K. Katsushima, A. Hatanaka, K. Shinjo, F. Ohka, T. Wakabayashi, et al., Oncogenic effects of evolutionarily conserved noncoding RNA ECONEXIN on gliomagenesis, Oncogene, 36 (2017), 4629-4640. doi: 10.1038/onc.2017.88
![]() |
[49] |
M. Shahid, M. Y. Lee, H. Piplani, A. M. Andres, B. Zhou, A. Yeon, et al., Centromere protein F (CENPF), a microtubule binding protein, modulates cancer metabolism by regulating pyruvate kinase M2 phosphorylation signaling, Cell Cycle, 17 (2018), 2802-2818. doi: 10.1080/15384101.2018.1557496
![]() |
[50] |
M. Shahid, M. Kim, M. Y. Lee, A. Yeon, S. You, H. L. Kim, et al., Downregulation of CENPF remodels prostate cancer cells and alters cellular metabolism, Proteomics, 19 (2019), 1900038. doi: 10.1002/pmic.201900038
![]() |
[51] |
X. Yang, B. S. Miao, C. Y. Wei, R. Z. Dong, P. T. Gao, X. Y. Zhang, et al., Lymphoid-specific helicase promotes the growth and invasion of hepatocellular carcinoma by transcriptional regulation of centromere protein F expression, Cancer Sci., 110 (2019), 2133-2144. doi: 10.1111/cas.14037
![]() |
![]() |
![]() |
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