The deregulated genetic factors are critically associated with idiopathic pulmonary arterial hypertension (IPAH) development and progression. However, the identification of hub-transcription factors (TFs) and miRNA-hub-TFs co-regulatory network-mediated pathogenesis in IPAH remains lacking.
We used GSE48149, GSE113439, GSE117261, GSE33463, and GSE67597 for identifying key genes and miRNAs in IPAH. We used a series of bioinformatics approaches, including R packages, protein-protein interaction (PPI) network, and gene set enrichment analysis (GSEA) to identify the hub-TFs and miRNA-hub-TFs co-regulatory networks in IPAH. Also, we employed a molecular docking approach to evaluate the potential protein-drug interactions.
We found that 14 TFs encoding genes, including ZNF83, STAT1, NFE2L3, and SMARCA2 are upregulated, and 47 TFs encoding genes, including NCOR2, FOXA2, NFE2, and IRF5 are downregulated in IPAH relative to the control. Then, we identified the differentially expressed 22 hub-TFs encoding genes, including four upregulated (STAT1, OPTN, STAT4, and SMARCA2) and 18 downregulated (such as NCOR2, IRF5, IRF2, MAFB, MAFG, and MAF) TFs encoding genes in IPAH. The deregulated hub-TFs regulate the immune system, cellular transcriptional signaling, and cell cycle regulatory pathways. Moreover, the identified differentially expressed miRNAs (DEmiRs) are involved in the co-regulatory network with hub-TFs. The six hub-TFs encoding genes, including STAT1, MAF, CEBPB, MAFB, NCOR2, and MAFG are consistently differentially expressed in the peripheral blood mononuclear cells of IPAH patients, and these hub-TFs showed significant diagnostic efficacy in distinguishing IPAH cases from the healthy individuals. Moreover, we revealed that the co-regulatory hub-TFs encoding genes are correlated with the infiltrations of various immune signatures, including CD4 regulatory T cells, immature B cells, macrophages, MDSCs, monocytes, Tfh cells, and Th1 cells. Finally, we discovered that the protein product of STAT1 and NCOR2 interacts with several drugs with appropriate binding affinity.
The identification of hub-TFs and miRNA-hub-TFs co-regulatory networks may provide a new avenue into the mechanism of IPAH development and pathogenesis.
Citation: Qian Li, Minawaer Hujiaaihemaiti, Jie Wang, Md. Nazim Uddin, Ming-Yuan Li, Alidan Aierken, Yun Wu. Identifying key transcription factors and miRNAs coregulatory networks associated with immune infiltrations and drug interactions in idiopathic pulmonary arterial hypertension[J]. Mathematical Biosciences and Engineering, 2023, 20(2): 4153-4177. doi: 10.3934/mbe.2023194
[1] | Chenchen Lu, Lin Chen, Shaoyong Lai . Local well-posedness and blow-up criterion to a nonlinear shallow water wave equation. AIMS Mathematics, 2024, 9(1): 1199-1210. doi: 10.3934/math.2024059 |
[2] | Xiaochun Sun, Yulian Wu, Gaoting Xu . Global well-posedness for the 3D rotating Boussinesq equations in variable exponent Fourier-Besov spaces. AIMS Mathematics, 2023, 8(11): 27065-27079. doi: 10.3934/math.20231385 |
[3] | Rou Lin, Min Zhao, Jinlu Zhang . Random uniform exponential attractors for non-autonomous stochastic Schrödinger lattice systems in weighted space. AIMS Mathematics, 2023, 8(2): 2871-2890. doi: 10.3934/math.2023150 |
[4] | Ailing Ban . Asymptotic behavior of non-autonomous stochastic Boussinesq lattice system. AIMS Mathematics, 2025, 10(1): 839-857. doi: 10.3934/math.2025040 |
[5] | Tariq Mahmood, Zhaoyang Shang . Blow-up criterion for incompressible nematic type liquid crystal equations in three-dimensional space. AIMS Mathematics, 2020, 5(2): 746-765. doi: 10.3934/math.2020051 |
[6] | Safyan Mukhtar, Wedad Albalawi, Faisal Haroon, Samir A. El-Tantawy . Analytical insight into fractional Fornberg-Whitham equations using novel transform methods. AIMS Mathematics, 2025, 10(4): 8165-8190. doi: 10.3934/math.2025375 |
[7] | Waqar Afzal, Mujahid Abbas, Najla M. Aloraini, Jongsuk Ro . Resolution of open problems via Orlicz-Zygmund spaces and new geometric properties of Morrey spaces in the Besov sense with non-standard growth. AIMS Mathematics, 2025, 10(6): 13908-13940. doi: 10.3934/math.2025626 |
[8] | Aslı Alkan, Halil Anaç . The novel numerical solutions for time-fractional Fornberg-Whitham equation by using fractional natural transform decomposition method. AIMS Mathematics, 2024, 9(9): 25333-25359. doi: 10.3934/math.20241237 |
[9] | Wenzhen Xiong, Yaqing Liu . Physical significance and periodic solutions of the high-order good Jaulent-Miodek model in fluid dynamics. AIMS Mathematics, 2024, 9(11): 31848-31867. doi: 10.3934/math.20241530 |
[10] | Ebner Pineda, Luz Rodriguez, Wilfredo Urbina . Variable exponent Besov-Lipschitz and Triebel-Lizorkin spaces for the Gaussian measure. AIMS Mathematics, 2023, 8(11): 27128-27150. doi: 10.3934/math.20231388 |
The deregulated genetic factors are critically associated with idiopathic pulmonary arterial hypertension (IPAH) development and progression. However, the identification of hub-transcription factors (TFs) and miRNA-hub-TFs co-regulatory network-mediated pathogenesis in IPAH remains lacking.
We used GSE48149, GSE113439, GSE117261, GSE33463, and GSE67597 for identifying key genes and miRNAs in IPAH. We used a series of bioinformatics approaches, including R packages, protein-protein interaction (PPI) network, and gene set enrichment analysis (GSEA) to identify the hub-TFs and miRNA-hub-TFs co-regulatory networks in IPAH. Also, we employed a molecular docking approach to evaluate the potential protein-drug interactions.
We found that 14 TFs encoding genes, including ZNF83, STAT1, NFE2L3, and SMARCA2 are upregulated, and 47 TFs encoding genes, including NCOR2, FOXA2, NFE2, and IRF5 are downregulated in IPAH relative to the control. Then, we identified the differentially expressed 22 hub-TFs encoding genes, including four upregulated (STAT1, OPTN, STAT4, and SMARCA2) and 18 downregulated (such as NCOR2, IRF5, IRF2, MAFB, MAFG, and MAF) TFs encoding genes in IPAH. The deregulated hub-TFs regulate the immune system, cellular transcriptional signaling, and cell cycle regulatory pathways. Moreover, the identified differentially expressed miRNAs (DEmiRs) are involved in the co-regulatory network with hub-TFs. The six hub-TFs encoding genes, including STAT1, MAF, CEBPB, MAFB, NCOR2, and MAFG are consistently differentially expressed in the peripheral blood mononuclear cells of IPAH patients, and these hub-TFs showed significant diagnostic efficacy in distinguishing IPAH cases from the healthy individuals. Moreover, we revealed that the co-regulatory hub-TFs encoding genes are correlated with the infiltrations of various immune signatures, including CD4 regulatory T cells, immature B cells, macrophages, MDSCs, monocytes, Tfh cells, and Th1 cells. Finally, we discovered that the protein product of STAT1 and NCOR2 interacts with several drugs with appropriate binding affinity.
The identification of hub-TFs and miRNA-hub-TFs co-regulatory networks may provide a new avenue into the mechanism of IPAH development and pathogenesis.
In this paper, we consider the following two-component Fornberg-Whitham (FW) system for a fluid
{ut+uux=(1−∂2x)−1∂x(ρ−u)ρt+(ρu)x=0(u,ρ)(0,x)=(u0,ρ0)(x) | (1.1) |
where x∈T=R/2πZ, t∈R+. Here, u=u(x,t) is the horizontal velocity of the fluid, and ρ=ρ(x,t) is the height of the fluid surface above a horizontal bottom. This system was first proposed in [5], and local well-posedness and non-uniform dependence on the initial data were established in Sobolev spaces Hs(R)×Hs−1(R) for s>32 in [11,12].
Local well-posedness in Besov spaces Bsp,r(R)×Bs−1p,r(R) of (1.1) was established in [4] for s>max{2+1p,52}. Besov spaces Bsp,r are a class of functions of interest in the study of nonlinear partial differential equations, as they are based on Sobolev spaces and introduce a measure of generalized Hölder regularity through the index r, along with the Sobolev index of differentiability s and the Lebesgue integrability index p. If s and p are fixed, the spaces Bsp,r grow larger with increasing r. In [4], the FW system was shown to be well-posed in the sense of Hadamard by establishing the existence and uniqueness of the solution to the system (1.1) and then proving continuity of the data-to-solution map when the initial data belong to Bsp,r(R)×Bs−1p,r(R) for s>max{2+1p,52}.
In this paper, our objective is to prove non-uniform dependence on periodic initial data for the two-component FW system (1.1) in Bsp,r(T)×Bs−1p,r(T) for s>max{2+1p,52}. We work with periodic initial data, as that simplifies our choice of approximate solutions and the resulting estimates. Setting Λ=1−∂2x, we rewrite (1.1) as
{ut+uux=Λ−1∂x(ρ−u)ρt+uρx+ρux=0(u,ρ)(0,x)=(u0,ρ0)(x) | (1.2) |
where x∈T=R/2πZ and t∈R+.
The paper is organized as follows: In Section 2, we recall the standard definitions and properties of Besov spaces, linear transport equations, the operator Λ, and the two-component FW system. In Section 3, we prove non-uniform dependence on initial data for the FW system (1.2) when the initial data belong to Bsp,r(T)×Bs−1p,r(T) for s>max{2+1p,52}. For this proof, we use a technique previously seen in the study of non-uniform continuity of data-to-solution maps for other nonlinear PDEs, for instance in [6,7,8,10,12]. We construct two sequences of approximate solutions such that the initial data for these sequences converge to each other in Bsp,r(T)×Bs−1p,r(T). Non-uniform dependence is then established by proving that the approximate and hence the exact solutions remain bounded away from each other for any positive time t>0. This idea was first explored by Kato in [9] to show that the data-to-solution map for Burgers' equation is not Hölder continuous in the Hs norm with s>3/2 for any Hölder exponent.
This section is a review of relevant definitions and results on Besov spaces, linear transport equations, the operator Λ, and the two-component FW system (1.2). We begin by listing some useful notation to be used throughout Section 3.
For any x,y∈R,
● x≲y denotes x≤αy for some constant α.
● x≈y denotes x=βy for some constant β.
● x≳y denotes x≥γy for some constant γ.
We recall the construction of a dyadic partition of unity from [8]. Consider a smooth bump function χ such that suppχ=[−43,43] and χ=1 on [−34,34]. For ξ>0, set φ−1(ξ)=χ, φ0(ξ)=χ(ξ2)−χ(ξ) and φq(ξ)=φ0(2−qξ). Then, suppφq=[34⋅2q,83⋅2q] and ∑q≥−1φq(ξ)=1. Using this partition, a Littlewood-Paley decomposition of any periodic distribution u is defined in [3] as follows:
Definition 2.1 (Littlewood-Paley decomposition). For any u∈D′(T) with the Fourier series u(x)=∑j∈Zˆujeijx where ˆuj=12π2π∫0e−ijyu(y)dy, its Littlewood-Paley decomposition is given by u=∑q≥−1Δqu, where Δqu are periodic dyadic blocks defined for all q∈Z as
Δqu=∑j∈Zφq(j)ˆujeijx. |
Using this Littlewood-Paley decomposition, Besov spaces on T are defined in [3] as follows:
Definition 2.2 (Besov spaces). Let s∈R and p, r∈[1,∞]. Then the Besov spaces of functions are defined as
Bsp,r≡Bsp,r(T)={u∈D′(T)|‖u‖Bsp,r<∞}, |
where
‖u‖Bsp,r={(∑q≥−1(2sq‖Δqu‖Lp)r)1/rif1≤r<∞supq≥−12sq‖Δqu‖Lpifr=∞. |
Following are some properties proved in [1, Section 2.8] and [3, Section 1.3] that facilitate the study of nonlinear partial differential equations in Besov spaces.
Lemma 2.3. Let s,sj∈R for j=1,2 and 1≤p,r≤∞. Then the following properties hold:
(1) Topological property: Bsp,r is a Banach space continuously embedded in D′(T).
(2) Algebraic property: For all s>0, Bsp,r∩L∞ is a Banach algebra.
(3) Interpolation: If f∈Bs1p,r∩Bs2p,r and θ∈[0,1], then f∈Bθs1+(1−θ)s2p,r and
‖f‖Bθs1+(1−θ)s2p,r≤‖f‖θBs1p,r‖f‖1−θBs2p,r. |
(4) Embedding: Bs1p,r↪Bs2p,r whenever s1≥s2. In particular, Bsp,r↪Bs−1p,r for all s∈R.
Remark on (2) in Lemma 2.3: When s>1p (or s≥1p and r=1), Bsp,r↪L∞. We will use the fact that for 0<s<1p, the result is still true as long as the functions are bounded.
Given a linear transport equation, Proposition A.1 in [2] proves the following estimate for its solution size in Besov spaces:
Proposition 2.4. Consider the linear transport equation
{∂tf+v∂xf=Ff(x,0)=f0(x) | (2.1) |
where f0∈Bsp,r(T), F∈L1((0,T);Bsp,r(T)) and v is such that ∂xv∈L1((0,T);Bs−1p,r(T)). Suppose f∈L∞((0,T);Bsp,r(T))∩C([0,T];D′(T)) is a solution to (2.1). Let 1≤p,r≤∞. If either s≠1+1p, or s=1+1p and r=1, then for a positive constant C that depends on s, p, and r, we have
‖f(t)‖Bsp,r≤eCV(t)(‖f0‖Bsp,r+C∫t0e−CV(τ)‖F(τ)‖Bsp,rdτ) |
where
V(t)=∫t0‖∂xv(τ)‖B1/pp,r∩L∞dτifs<1+1p |
and
V(t)=∫t0‖∂xv(τ)‖Bs−1p,rdτotherwise. |
For r<∞, f∈C([0,T],Bsp,r(T)), and if r=∞, then f∈C([0,T],Bs′p,1(T)) for all s′<s.
Let Λ=1−∂2x; then, for any test function g, the Fourier transform of Λ−1g is given by F(Λ−1g)=11+ξ2ˆg(ξ). Moreover, for any s∈R, Λ−1∂x is continuous from Bs−1p,r to Bsp,r; that is, for all h∈Bs−1p,r, there exists a constant κ>0 depending on s, p, and r such that
‖Λ−1∂xh‖Bsp,r≤κ‖h‖Bs−1p,r. | (2.2) |
The well-posedness of the two-component FW system (1.2) in Besov spaces was established on the real line in [4] with the following result:
Theorem 2.5. Let s>max{2+1p,52}, p∈[1,∞], r∈[1,∞] and (u0,ρ0)∈Bsp,r(R)×Bs−1p,r(R). Then the system (1.2) has a unique solution (u,ρ)∈C([0,T];Bsp,r(R)×Bs−1p,r(R)), where the doubling time T is given by
T=C(‖u0‖Bsp,r+‖ρ0‖Bs−1p,r)2, |
with C being a constant that depends on s, p, and r, and the solution size is estimated as
‖(u,ρ)‖Bsp,r×Bs−1p,r≤2(‖u0‖Bsp,r+‖ρ0‖Bs−1p,r). |
Moreover, the data-to-solution map is continuous.
Since we work with Bsp,r(T)×Bs−1p,r(T) in this paper, we state the following:
Corollary 2.6. Theorem 2.5 holds when R is replaced by T.
Proof. The existence of a solution to (1.2) is proved by altering the mollifier used to prove Theorem 2.5. This adaptation of the mollifier was done for the single Fornberg-Whitham equation in [7, Section 3.1]. Uniqueness and continuous dependence on periodic initial data for the system (1.2) are established by approximation arguments similar to those in [4, Sections 3.2 and 3.3].
In this section, we establish non-uniform dependence on initial data in the periodic case for the two-component FW system (1.2) in Besov spaces.
Theorem 3.1. Let s>max{2+1p,52} and r∈[1,∞]. The data-to-solution map (u0,ρ0)↦(u(t),ρ(t)) of the Cauchy problem (1.2) is not uniformly continuous from any bounded subset of Bsp,r(T)×Bs−1p,r(T) into C([0,T];Bsp,r(T))×C([0,T];Bs−1p,r(T)) where T is given by Theorem 2.5. In particular, there exist two sequences of solutions {(uω,n,ρω,n)} with ω=±1 such that the following hold:
(i) limn→∞(‖u1,n(0)−u−1,n(0)‖Bsp,r+‖ρ1,n(0)−ρ−1,n(0)‖Bs−1p,r)=0.
(ii) lim infn→∞(‖u1,n−u−1,n‖Bsp,r+‖ρ1,n−ρ−1,n‖Bs−1p,r)≳|sint|.
Proof. For n∈N, we consider two sequences of functions {(uω,n,ρω,n)} with ω=±1, defined by
{uω,n=−ωn+1nssin(nx+ωt)ρω,n=1n+1nssin(nx+ωt). |
We take initial data
{u0ω,n=uω,n(0)=−ωn+1nssinnxρ0ω,n=ρω,n(0)=1n+1nssinnx. |
Let the solutions to the FW system (1.2) with these initial data be denoted by (uω,n,ρω,n). At t=0, we have
limn→∞(‖u01,n−u0−1,n‖Bsp,r+‖ρ01,n−ρ0−1,n‖Bs−1p,r)=limn→∞2‖n−1‖Bsp,r=0, |
which proves part (i) of Theorem 3.1.
To prove part (ii), first we estimate ‖(u0ω,n,ρ0ω,n)‖Bγp,r×Bγ−1p,r and ‖(uω,n,ρω,n)‖Bγp,r×Bγ−1p,r for any γ>0 and r<∞. Using the triangle inequality, we have
‖(u0ω,n,ρ0ω,n)‖Bγp,r×Bγ−1p,r≤2‖n−1‖Bγp,r+n−s‖sinnx‖Bγp,r+n1−s‖sinnx‖Bγ−1p,r. | (3.1) |
By Definition 2.2,
‖sinnx‖Bγp,r=(∑q≥−12γqr‖Δqsinnx‖rLp)1r. | (3.2) |
From Definition 2.1, as shown in the Appendix, we have ‖Δqsin(nx)‖Lp=φq(n), where 0<φq(n)≤1 for all q such that 1ln(2)ln(38n)≤q≤1ln(2)ln(43n) and φq(n)=0 otherwise. Hence, (3.2) implies that for any γ>0,
‖sin(nx)‖Bγp,r≤(1ln(2)ln(43n)∑q=1ln(2)ln(38n)(2q)γr)1r. |
As 2q≤43n for every term in the summation, from the above, we obtain
‖sin(nx)‖Bγp,r≤(1ln(2)ln(43n)∑q=1ln(2)ln(38n)(43n)γr)1r=(1ln(2)[ln(43n)−ln(38n)])1r(43n)γ=(1ln(2)ln(329))1r(43)γnγ=Cγnγ. | (3.3) |
Here and in what follows, Cγ is a generic constant that depends only on γ for fixed p and r. Similarly, it follows that for any γ>0,
‖cos(nx)‖Bγp,r≤Cγnγ. | (3.4) |
By Definition 2.1,
Δqn−1=φq(0)n−1={n−1ifq=−10otherwise. |
So, ‖n−1‖Bγp,r=(∑q≥−12γqr‖Δqn−1‖rLp)1r=2−γn−1. Using this and (3.3), it follows from (3.1) that
‖(u0ω,n,ρ0ω,n)‖Bγp,r×Bγ−1p,r≤21−γn−1+Cγnγn−s+Cγnγ−1n1−s≤Cγmax{n−1,nγ−s}. | (3.5) |
Since (uω,n,ρω,n) is a phase shift of (u0ω,n,ρ0ω,n), we have
‖(uω,n,ρω,n)‖Bγp,r×Bγ−1p,r≤Cγmax{n−1,nγ−s}. | (3.6) |
If r=∞, (3.5) and (3.6) follow immediately from Definition 2.2.
We complete the proof of Theorem 3.1 by establishing (ii) for {(uω,n,ρω,n)}, taking advantage of the following lemma, whose proof follows the proof of Theorem 3.1. Lemma 3.2 establishes that for each n and ω, (uω,n,ρω,n) approximates (uω,n,ρω,n) in Bsp,r(T)×Bs−1p,r(T) uniformly on [0,T] for some T>0.
Lemma 3.2. Let Eω,n=(Eω,n1,Eω,n2) where Eω,n1=uω,n−uω,n and Eω,n2=ρω,n−ρω,n, with ω=±1. Then for all t∈(0,T), where T is given by Theorem 2.5, ‖Eω,n(t)‖Bsp,r×Bs−1p,r=‖Eω,n1(t)‖Bsp,r+‖Eω,n2(t)‖Bs−1p,r→0 as n→∞.
We show that (u−1,n,ρ−1,n) and (u1,n,ρ1,n) stay bounded away from each other for any t>0. Since
‖u1,n−u−1,n‖Bsp,r≥‖u1,n−u−1,n‖Bsp,r−‖u1,n−u1,n‖Bsp,r−‖u−1,n−u−1,n‖Bsp,r | (3.7) |
and
‖ρ1,n−ρ−1,n‖Bs−1p,r≥‖ρ1,n−ρ−1,n‖Bs−1p,r−‖ρ1,n−ρ1,n‖Bs−1p,r−‖ρ−1,n−ρ−1,n‖Bs−1p,r, | (3.8) |
adding (3.7) and (3.8) we obtain
‖u1,n−u−1,n‖Bsp,r+‖ρ1,n−ρ−1,n‖Bs−1p,r≥‖u1,n−u−1,n‖Bsp,r+‖ρ1,n−ρ−1,n‖Bs−1p,r−‖E1,n(t)‖Bsp,r×Bs−1p,r−‖E−1,n(t)‖Bsp,r×Bs−1p,r≥n−s(‖sin(nx+t)−sin(nx−t)‖Bsp,r+‖sin(nx+t)−sin(nx−t)‖Bs−1p,r)−2‖n−1‖Bsp,r−‖E1,n(t)‖Bsp,r×Bs−1p,r−‖E−1,n(t)‖Bsp,r×Bs−1p,r=2n−s(‖cos(nx)‖Bsp,r|sin(t)|+‖cos(nx)‖Bs−1p,r|sin(t)|)−21−γn−1−‖E1,n(t)‖Bsp,r×Bs−1p,r−‖E−1,n(t)‖Bsp,r×Bs−1p,r. | (3.9) |
By Definition 2.2, if r=∞, we immediately have
‖cos(nx)‖Bsp,r≥Csns, | (3.10) |
where Cs is a constant that depends only on s for a given p. For 1≤r<∞, there is a similar estimate, whose proof is given in the Appendix. Also, by Lemma 3.2, we have ‖Eω,n(t)‖Bsp,r×Bs−1p,r→0 for ω=±1, as n→∞. Using this and (3.10), it follows from (3.9) that
lim infn→∞(‖u1,n−u−1,n‖Bsp,r+‖ρ1,n−ρ−1,n‖Bs−1p,r)≥2Cs(lim infn→∞|sin(t)|+lim infn→∞n−1|sin(t)|)≈|sin(t)|>0. |
This proves part (ii) of Theorem 3.1 and completes the proof of non-uniform dependence on initial data for the two-component FW system (1.2) in Bsp,r(T)×Bs−1p,r(T) for s>max{2+1p,52}.
Now we prove Lemma 3.2.
Proof. (Lemma 3.2) We show that ‖Eω,n(t)‖Bγp,r×Bγ−1p,r→0 as n→∞ for any γ with max{s−32,1+1p}<γ<s−1, and then interpolate between such a γ and a value δ>s. Recall that Eω,n1=uω,n−uω,n and Eω,n2=ρω,n−ρω,n. It can be seen that Eω,n1 and Eω,n2 vanish at t=0 and that they satisfy the equations
{∂tEω,n1+uω,n∂xEω,n1=−Eω,n1∂xuω,n+Λ−1∂x(Eω,n2−Eω,n1)−R1∂tEω,n2+uω,n∂xEω,n2=−Eω,n2∂xuω,n−ρω,n∂xEω,n1−Eω,n1∂xρω,n−R2. | (3.11) |
Here, R1 and R2 are the approximate solutions for the FW system, that is,
{R1=∂tuω,n+uω,n∂xuω,n−Λ−1∂x(ρω,n−uω,n)R2=∂tρω,n+∂x(ρω,nuω,n). |
● Estimate for ‖R1‖Bγp,r: Using the definitions of uω,n and ρω,n, we have
R1=∂tuω,n+uω,n∂xuω,n−Λ−1∂x(ρω,n−uω,n)=12n2s−1sin(2(nx+ωt)). |
Then by (3.3),
‖R1‖Bγp,r≤Cγnγ−2s+1. |
● Estimate for ‖R2‖Bγ−1p,r: Using the definitions of uω,n and ρω,n,
R2=∂tρω,n+∂x(ρω,nuω,n)=1nscos(nx+ωt)+1n2s−1sin(2(nx+ωt)). |
So from (3.3) and (3.4), it follows that
‖R2‖Bγ−1p,r≤Cγ(n−snγ−1+n1−2snγ−1)≤Cγnγ−s−1. |
Therefore,
‖R1‖Bγp,r+‖R2‖Bγ−1p,r≲nγ−s−1. | (3.12) |
Since Eω,n1(t) and Eω,n2(t) satisfy the linear transport equations (3.11), to estimate the error ‖Eω,n(t)‖Bγp,r×Bγ−1p,r, we apply Proposition 2.4 to obtain
‖Eω,n1(t)‖Bγp,r≤K1eK1V1(t)∫t0e−K1V1(τ)‖F1(τ)‖Bγp,rdτ | (3.13) |
and
‖Eω,n2(t)‖Bγ−1p,r≤K2eK2V2(t)∫t0e−K2V2(τ)‖F2(τ)‖Bγ−1p,rdτ | (3.14) |
where K1, K2 are positive constants depending on γ and
F1(t)=−Eω,n1∂xuω,n+Λ−1∂x(Eω,n2−Eω,n1)−R1, | (3.15) |
F2(t)=−Eω,n2∂xuω,n−ρω,n∂xEω,n1−Eω,n1∂xρω,n−R2. | (3.16) |
V1(t)=∫t0‖∂xuω,n(τ)‖Bγ−1p,rdτ, |
V2(t)={∫t0‖∂xuω,n(τ)‖B1/pp,r∩L∞dτifγ<2+1p∫t0‖∂xuω,n(τ)‖Bγ−2p,rdτotherwise. |
Since max{s−32,1+1p}<γ<s−1, we have
V1(t)≲nγ−st≤n−1t andV2(t)≤C∫t0‖uω,n(τ)‖Bγp,rdτ | (3.17) |
for some constant C that depends on γ, p, and r. By Theorem 2.5 and Eq (3.5), it follows that
V2(t)≤2C∫t0‖(u0ω,n,ρ0ω,n)‖Bγp,r×Bγ−1p,rdτ≲n−1t. | (3.18) |
Let K=max{K1,K2}. Using (3.17) and (3.18), we combine (3.13) and (3.14) to obtain
‖Eω,n1(t)‖Bγp,r+‖Eω,n2(t)‖Bγ−1p,r≲∫t0eK(t−τ)/n(‖F1(τ)‖Bγp,r+‖F2(τ)‖Bγ−1p,r)dτ. | (3.19) |
● Estimate for ‖F1(τ)‖Bγp,r: From (3.15), as Bγp,r is a Banach algebra, we have
‖F1‖Bγp,r≤‖Eω,n1‖Bγp,r‖∂xuω,n‖Bγp,r+‖Λ−1∂x(Eω,n2−Eω,n1)‖Bγp,r+‖R1‖Bγp,r≤‖Eω,n1‖Bγp,r‖uω,n‖Bγ+1p,r+‖Λ−1∂x(Eω,n2−Eω,n1)‖Bγp,r+‖R1‖Bγp,r. | (3.20) |
From (2.2),
‖Λ−1∂x(Eω,n2−Eω,n1)‖Bγp,r≤κ‖Eω,n2−Eω,n1‖Bγ−1p,r≤M(‖Eω,n1‖Bγp,r+‖Eω,n2‖Bγ−1p,r) | (3.21) |
where M is a constant depending on γ,p, and r. By Theorem 2.5, we have
‖uω,n‖Bγ+1p,r≤2‖(u0ω,n,ρ0ω,n)‖Bγ+1p,r×Bγp,r, |
so by (3.5), ‖uω,n‖Bγ+1p,r≤2Cγmax{n−1,nγ+1−s}. As γ>max{s−32,1+1p},
‖uω,n‖Bγ+1p,r≲nγ+1−s. | (3.22) |
Using (3.21) and (3.22), from (3.20), we obtain
‖F1(τ)‖Bγp,r≲(M+nγ+1−s)‖Eω,n1(τ)‖Bγp,r+M‖Eω,n2(τ)‖Bγ−1p,r+‖R1(τ)‖Bγp,r. | (3.23) |
● Estimate for ‖F2(τ)‖Bγ−1p,r: We may use the algebra property, item (2) of Lemma 2.3, for Bγ−1p,r since γ−1>max{s−52,1p}>0 and the functions we are dealing with are bounded. Then, from (3.16),
‖F2‖Bγ−1p,r≤‖Eω,n2‖Bγ−1p,r‖∂xuω,n‖Bγ−1p,r+‖ρω,n‖Bγ−1p,r‖∂xEω,n1‖Bγ−1p,r+‖∂xρω,n‖Bγ−1p,r‖Eω,n1‖Bγ−1p,r+‖R2‖Bγ−1p,r≲n−1‖Eω,n1‖Bγp,r+‖Eω,n2‖Bγ−1p,r‖uω,n‖Bγp,r+‖R2‖Bγ−1p,r. | (3.24) |
By Corollary 2.6, ‖uω,n‖Bγp,r≤2‖(u0ω,n,ρ0ω,n)‖Bγp,r×Bγ−1p,r, which implies
‖uω,n‖Bγp,r≤2Cγmax{n−1,nγ−s} |
by (3.5). As γ<s−1, ‖uω,n‖Bγp,r≲n−1. Using this in (3.24) yields
‖F2(τ)‖Bγ−1p,r≲n−1‖Eω,n1(τ)‖Bγp,r+n−1‖Eω,n2(τ)‖Bγ−1p,r+‖R2(τ)‖Bγ−1p,r. | (3.25) |
Adding (3.23) and (3.25) gives
‖F1(τ)‖Bγp,r+‖F2(τ)‖Bγ−1p,r≲(M+nγ+1−s)(‖Eω,n1(τ)‖Bγp,r+‖Eω,n2(τ)‖Bγ−1p,r)+‖R1(τ)‖Bγp,r+‖R2(τ)‖Bγ−1p,r. | (3.26) |
Substituting (3.26) into (3.19), we obtain
‖Eω,n(t)‖Bγp,r×Bγ−1p,r≲f(t)+∫t0g(τ)‖Eω,n(τ)‖Bγp,r×Bγ−1p,rdτ | (3.27) |
where
f(t)≈∫t0eK(t−τ)/n(‖R1(τ)‖Bγp,r+‖R2(τ)‖Bγ−1p,r)dτ | (3.28) |
and
g(τ)≈(M+nγ+1−s)eK(t−τ)/n≤(M+1)eK(t−τ)/n. | (3.29) |
Using Grönwall's inequality, from (3.27) we obtain
‖Eω,n(t)‖Bγp,r×Bγ−1p,r≲f(t)+∫t0g(τ)f(τ)e∫tτg(z)dzdτ. | (3.30) |
Using (3.12) along with (3.28) and (3.29), from (3.30), we obtain
‖Eω,n(t)‖Bγp,r×Bγ−1p,r≲nγ−s−1, | (3.31) |
which means that ‖Eω,n(t)‖Bγp,r×Bγ−1p,r→0 as n→∞ for any max{s−32,1+1p}<γ<s−1.
On the other hand, if δ∈(s,s+1), then noting that the solution with the given data is in Bδp,r×Bδ−1p,r for any δ we have, for 0<t<T (from Theorem 2.5)
‖Eω,n(t)‖Bδp,r×Bδ−1p,r≤‖(uω,n,ρω,n)‖Bδp,r×Bδ−1p,r+‖(uω,n,ρω,n)‖Bδp,r×Bδ−1p,r≤2‖(u0ω,n,ρ0ω,n)‖Bδp,r×Bδ−1p,r+‖(uω,n,ρω,n)‖Bδp,r×Bδ−1p,r, | (3.32) |
where we have used the solution size estimate in Theorem 2.5. Now, for δ<s+1, Eqs (3.5) and (3.6) imply that ‖(u0ω,n,ρ0ω,n)‖Bδp,r×Bδ−1p,r≤Cδnδ−s and ‖(uω,n,ρω,n)‖Bδp,r×Bδ−1p,r≤Cδnδ−s, where Cδ denotes a constant that depends only on δ, for a given p and r. So (3.32) yields
‖Eω,n(t)‖Bδp,r×Bδ−1p,r≲nδ−s. | (3.33) |
We use the interpolation property, item (3) from Lemma 2.3, with θ=δ−sδ−γ, to obtain
‖Eω,n(t)‖Bsp,r×Bs−1p,r≤‖Eω,n(t)‖θBγp,r×Bγ−1p,r‖Eω,n(t)‖1−θBδp,r×Bδ−1p,r. | (3.34) |
From (3.34), using (3.31) and (3.33), we obtain
‖Eω,n(t)‖Bsp,r×Bs−1p,r≲(nγ−s−1)δ−sδ−γ(nδ−s)s−γδ−γ=n−θ. | (3.35) |
As θ∈(0,1), (3.35) implies that ‖Eω,n(t)‖Bsp,r×Bs−1p,r→0 as n→∞ for any s>max{2+1p,52}. This completes the proof of Lemma 3.2.
When p=r=2, Bs2,2 and Hs are equivalent by [2, Proposition 1.2], and so we obtain the following corollary:
Corollary 3.3. The data-to-solution map for the two-component FW system (1.2) is not uniformly continuous from any bounded subset of Hs(T)×Hs−1(T) into C([0,T];Hs(T))×C([0,T];Hs−1(T)) for s>52.
In this paper, we considered the two-component Fornberg-Whitham (FW) system (1.2) and used a sequential approach to prove that its data-to-solution map is not uniformly continuous for periodic initial data belonging to Besov spaces Bsp,r(T)×Bs−1p,r(T) where s>max{2+1p,52}. As a corollary, this establishes non-uniform dependence on periodic initial data for the FW system (1.2) in Sobolev spaces Hs(T)×Hs−1(T) for s>52.
In this appendix, we provide a lower bound on ‖cos(nx)‖Bsp,r for any s>0 and 1≤r<∞. By Definition 2.2,
‖cos(nx)‖Bsp,r=(∑q≥−12sqr‖Δqcosnx‖rLp)1r. | (4.1) |
By Definition 2.1, Δqcos(nx)=φq(n)einx. Therefore, ‖Δqcos(nx)‖Lp=φq(n), where 0<φq(n)≤1 for all q such that 1ln(2)ln(38n)≤q≤1ln(2)ln(43n) and \varphi_q\left(n\right) = 0 otherwise, (4.1) implies that
\begin{equation*} \|\cos (nx)\|_{B^{s}_{p,r}}\; = \; \left(\sum\limits_{q = \frac{1}{\ln (2)}\ln \left(\frac{3}{8}n\right)}^{ \frac{1}{\ln (2)}\ln \left(\frac{4}{3}n\right)} \left(2^q\right)^{sr}\varphi_q^r(n)\right)^{\frac{1}{r}}\; . \end{equation*} |
Since 2^q \geq \frac{3}{8}n for all terms in the summation, from the above we have
\begin{equation} \|\cos (nx)\|_{B^{s}_{p,r}}\; \geq\; \left(\frac{3}{8}\right)^{s}n^s\left(\sum\limits_{q = \frac{1}{\ln (2)}\ln \left(\frac{3}{8}n\right)}^{ \frac{1}{\ln (2)}\ln \left(\frac{4}{3}n\right)} \varphi_q^r(n)\right)^{\frac{1}{r}} \; . \end{equation} | (4.2) |
Recall that \varphi_0(\xi) = \chi\left(\frac{\xi}{2}\right) - \chi(\xi) and \varphi_q(\xi) = \varphi_0(2^{-q}\xi) for any q > -1 , where \mathrm{supp}\; \chi = [-\frac{4}{3}, \frac{4}{3}] and \chi = 1 on [-\frac{3}{4}, \frac{3}{4}] . This means that \mathrm{supp}\; \varphi_q = [\frac{3}{4}\cdot 2^q, \frac{8}{3}\cdot 2^q] for any q\geq 1 and furthermore, \varphi_q = 1 on the interval [\frac{4}{3}\cdot 2^q, \frac{3}{2}\cdot 2^q] . In other words, \varphi_q(n) = 1 for \frac{1}{\ln (2)}\ln \left(\frac{2}{3}n\right)\leq q \leq \frac{1}{\ln (2)}\ln \left(\frac{3}{4}n\right) . Therefore, from (4.2) we have
\begin{align*} \label{app2} \|\cos (nx)\|_{B^{s}_{p,r}}\; & \geq\; \left(\frac{3}{8}\right)^{s}n^s\left(\sum\limits_{q = \frac{1}{\ln (2)}\ln \left(\frac{2}{3}n\right)}^{ \frac{1}{\ln (2)}\ln \left(\frac{3}{4}n\right)} 1\right)^{\frac{1}{r}} \nonumber \\ & = \left(\frac{3}{8}\right)^{s}n^s\left(\frac{1}{\ln (2)}\left[\ln \left(\frac{3}{4}n\right) - \ln \left(\frac{2}{3}n\right)\right]\right)^{\frac{1}{r}} \nonumber \\ & = \left(\frac{1}{\ln (2)} \ln \left(\frac{9}{8}\right) \right)^{\frac{1}{r}}\left(\frac{3}{8}\right)^{s}n^s\; = \; C_s n^s, \end{align*} |
where C_s is a constant that depends only on s , for a given p and r . The same estimate holds for \|\sin (nx)\|_{B^{s}_{p, r}} as well.
All authors contributed equally towards conceptualization, formal analysis, investigation and methodology in this project; Writing of the original draft was done by Prerona Dutta; thereafter all authors together completed the review and editing process. All authors have read and approved the final version of the manuscript for publication.
The authors declare they have not used Artificial Intelligence (AI) tools in the creation of this article.
We thank John Holmes, at The Ohio State University, for his valuable suggestions on this project. We would also like to thank the anonymous referees for their comments which greatly helped in improving the paper overall.
All authors declare no conflicts of interest in this paper.
[1] |
M. M. Hoeper, M. Humbert, R. Souza, M. Idrees, S. M. Kawut, K. Sliwa-Hahnle, et al., A global view of pulmonary hypertension, Lancet Respir. Med., 4 (2016), 306–322. https://doi.org/10.1016/s2213-2600(15)00543-3 doi: 10.1016/s2213-2600(15)00543-3
![]() |
[2] |
H. Zeng, X. Liu, Y. Zhang, Identification of potential biomarkers and immune infiltration characteristics in idiopathic pulmonary arterial hypertension using bioinformatics analysis, Front. Cardiovasc. Med., 8 (2021). https://doi.org/10.3389/fcvm.2021.624714 doi: 10.3389/fcvm.2021.624714
![]() |
[3] |
N. Galiè, M. Humbert, J. Vachiery, S. Gibbs, I. Lang, A. Torbicki, et al., 2015 ESC/ERS Guidelines for the Diagnosis and Treatment of Pulmonary Hypertension, Rev. Esp. Cardiol. (Engl. Ed.), 69 (2016), 177. https://doi.org/10.1016/j.rec.2016.01.002 doi: 10.1016/j.rec.2016.01.002
![]() |
[4] |
V. V. McLaughlin, M. D. McGoon, Pulmonary Arterial Hypertension, Circulation, 114 (2006), 1417–1431. https://doi.org/10.1161/CIRCULATIONAHA.104.503540 doi: 10.1161/CIRCULATIONAHA.104.503540
![]() |
[5] | P. Pahal, S. Sharma, Idiopathic Pulmonary Artery Hypertension, StatPearls Publishing, Florida, 2022. |
[6] |
E. Spiekerkoetter, S. M. Kawut, V. A. de Jesus Perez, New and emerging therapies for pulmonary arterial hypertension, Annu. Rev. Med., 70 (2019), 45–59. https://doi.org/10.1146/annurev-med-041717-085955 doi: 10.1146/annurev-med-041717-085955
![]() |
[7] |
J. Y. Cao, K. M. Wales, R. Cordina, E. M. T. Lau, D. S. Celermajer, Pulmonary vasodilator therapies are of no benefit in pulmonary hypertension due to left heart disease: A meta-analysis, Int. J. Cardiol., 273 (2018), 213–220. https://doi.org/10.1016/j.ijcard.2018.09.043 doi: 10.1016/j.ijcard.2018.09.043
![]() |
[8] |
L. Yan, Q. Luo, Z. Zhao, Q. Zhao, Q. Jin, Y. Zhang, et al., Nocturnal hypoxia in patients with idiopathic pulmonary arterial hypertension, Pulm. Circ., 10 (2020), 1–7. https://doi.org/10.1177/2045894019885364 doi: 10.1177/2045894019885364
![]() |
[9] |
N. W. Morrell, M. A. Aldred, W. K. Chung, C. G. Elliott, W. C. Nichols, F. Soubrier, et al., Genetics and genomics of pulmonary arterial hypertension, Eur. Respir. J., 53 (2019), 1801899. https://doi.org/10.1183/13993003.01899-2018 doi: 10.1183/13993003.01899-2018
![]() |
[10] |
M. A. Aldred, J. Vijayakrishnan, V. James, F. Soubrier, M. A. Gomez-Sanchez, G. Martensson, et al., BMPR2 gene rearrangements account for a significant proportion of mutations in familial and idiopathic pulmonary arterial hypertension, Hum. Mutat., 27 (2006), 212–213. https://doi.org/10.1002/humu.9398 doi: 10.1002/humu.9398
![]() |
[11] |
S. H. Choi, Y. Jung, J. Jang, S. Han, Idiopathic pulmonary arterial hypertension associated with a novel frameshift mutation in the bone morphogenetic protein receptor Ⅱ gene and enhanced bone morphogenetic protein signaling, Medicine, 98 (2019), e17594. https://doi.org/10.1097/MD.0000000000017594 doi: 10.1097/MD.0000000000017594
![]() |
[12] |
A. Chida, M. Shintani, T. Nakayama, Y. Furutani, E. Hayama, K. Inai, et al., Missense mutations of the BMPR1B (ALK6) gene in childhood idiopathic pulmonary arterial hypertension, Circ. J., 76 (2012), 1501–1508. https://doi.org/10.1253/circj.cj-11-1281 doi: 10.1253/circj.cj-11-1281
![]() |
[13] |
D. Saygin, T. Tabib, H. E. T. Bittar, E. Valenzi, J. Sembrat, S. Y. Chan, et al., Transcriptional profiling of lung cell populations in idiopathic pulmonary arterial hypertension, Pulm. Circ., 10 (2020), 1–15. https://doi.org/10.1177/2045894020908782 doi: 10.1177/2045894020908782
![]() |
[14] |
Y. Wu, J. Wharton, R. Walters, E. Vasilaki, J. Aman, L. Zhao, et al., The pathophysiological role of novel pulmonary arterial hypertension gene SOX17, Eur. Respir. J., (2021). https://doi.org/10.1183/13993003.04172-2020 doi: 10.1183/13993003.04172-2020
![]() |
[15] |
C. S. Park, S. H. Kim, H. Y. Yang, J. Kim, R. T. Schermuly, Y. S. Cho, et al., Sox17 deficiency promotes pulmonary arterial hypertension via HGF/c-Met signaling, Circ. Res., 131 (2022), 792–806. https://doi.org/10.1161/CIRCRESAHA.122.320845 doi: 10.1161/CIRCRESAHA.122.320845
![]() |
[16] |
T. Wang, S. Wang, Y. Xu, C. Zhao, X. Qiao, C. Yang, et al., SOX17 loss-of-function mutation underlying familial pulmonary arterial hypertension, Int. Heart. J., 62 (2021), 566–574. https://doi.org/10.1536/ihj.20-711 doi: 10.1536/ihj.20-711
![]() |
[17] |
N. Zhu, C. L. Welch, J. Wang, P. M. Allen, C. Gonzaga-Jauregui, L. Ma, et al., Rare variants in SOX17 are associated with pulmonary arterial hypertension with congenital heart disease, Genome Med., 56 (2018). https://doi.org/10.1186/s13073-018-0566-x doi: 10.1186/s13073-018-0566-x
![]() |
[18] |
X. Yuan, Z. Wang, L. Wang, Q. Zhao, S. Gong, Y. Sun, et al., Increased levels of runt-related transcription factor 2 are associated with poor survival of patients with idiopathic pulmonary arterial hypertension, Am. J. Men's Health., 14 (2020). https://doi.org/10.1177/1557988320945458 doi: 10.1177/1557988320945458
![]() |
[19] |
L. C. Price, S. J. Wort, F. Perros, P. Dorfmüller, A. Huertas, D. Montani, et al., Inflammation in pulmonary arterial hypertension, Chest, 141 (2012), 210–221. https://doi.org/10.1378/chest.11-0793 doi: 10.1378/chest.11-0793
![]() |
[20] |
H. Zeng, X. Liu, Y. Zhang, Identification of potential biomarkers and immune infiltration characteristics in idiopathic pulmonary arterial hypertension using bioinformatics analysis, Front. Cardiovasc. Med., 8 (2021). https://doi.org/10.3389/fcvm.2021.624714 doi: 10.3389/fcvm.2021.624714
![]() |
[21] |
I. Sarrion, L. Milian, G. Juan, M. Ramon, I. Furest, C. Carda, et al., Role of circulating miRNAs as biomarkers in idiopathic pulmonary arterial hypertension: Possible relevance of miR-23a, Oxid. Med. Cell. Longevity, 2015 (2015), 792846. https://doi.org/10.1155/2015/792846 doi: 10.1155/2015/792846
![]() |
[22] |
W. He, X. Su, L. Chen, C. Liu, W. Lu, T. Wang, et al., Potential biomarkers and therapeutic targets of idiopathic pulmonary arterial hypertension, Physiol. Rep., 10 (2022), e15101. https://doi.org/10.14814/phy2.15101 doi: 10.14814/phy2.15101
![]() |
[23] |
C. Li, Z. Zhang, Q. Xu, R. Shi, Comprehensive analyses of miRNA-mRNA network and potential drugs in idiopathic pulmonary arterial hypertension, BioMed Res. Int., 2020 (2020), 5156304. https://doi.org/10.1155/2020/5156304 doi: 10.1155/2020/5156304
![]() |
[24] |
S. Hao, P. Jiang, L. Xie, G. Xiang, Z. Liu, W. Hu, et al., Essential genes and MiRNA-mRNA network contributing to the pathogenesis of idiopathic pulmonary arterial hypertension, Front. Cardiovasc. Med., 8 (2021), 627873. https://doi.org/10.3389/fcvm.2021.627873 doi: 10.3389/fcvm.2021.627873
![]() |
[25] |
D. Li, A. Tulahong, M. N. Uddin, H. Zhao, H. Zhang, Meta-analysis identifying epithelial-derived transcriptomes predicts poor clinical outcome and immune infiltrations in ovarian cancer, Math. Biosci. Eng., 18 (2021), 6527–6551. https://doi.org/10.3934/mbe.2021324 doi: 10.3934/mbe.2021324
![]() |
[26] |
E. Hsu, H. Shi, R. M. Jordan, J. Lyons-Weiler, J. M. Pilewski, C. A. Feghali-Bostwick, Lung tissues in patients with systemic sclerosis have gene expression patterns unique to pulmonary fibrosis and pulmonary hypertension, Arthritis Rheum., 63 (2011), 783–794. https://doi.org/10.1002/art.30159 doi: 10.1002/art.30159
![]() |
[27] |
L. Renaud, W. A. da Silveira, N. Takamura, G. Hardiman, C. Feghali-Bostwick, Prominence of IL6, IGF, TLR, and bioenergetics pathway perturbation in lung tissues of scleroderma patients with pulmonary fibrosis, front. immunol., 11 (2020), 383. https://doi.org/10.3389/fimmu.2020.00383 doi: 10.3389/fimmu.2020.00383
![]() |
[28] |
M. Mura, M. J. Cecchini, M. Joseph, J. T. Granton, Osteopontin lung gene expression is a marker of disease severity in pulmonary arterial hypertension, Respirology, 24 (2019), 1104–1110. https://doi.org/10.1111/resp.13557 doi: 10.1111/resp.13557
![]() |
[29] |
R. S. Stearman, Q. M. Bui, G. Speyer, A. Handen, A. R. Cornelius, B. B. Graham, et al., Systems analysis of the human pulmonary arterial hypertension lung transcriptome, Am. J. Respir. Cell Mol. Biol., 60 (2019), 637–649. https://doi.org/10.1165/rcmb.2018-0368OC doi: 10.1165/rcmb.2018-0368OC
![]() |
[30] |
C. E. Romanoski, X. Qi, S. Sangam, R. R. Vanderpool, R.S. Stearman, A. Conklin, et al., Transcriptomic profiles in pulmonary arterial hypertension associate with disease severity and identify novel candidate genes, Pulm. Circ., 10 (2020). https://doi.org/10.1177/2045894020968531 doi: 10.1177/2045894020968531
![]() |
[31] |
C. Cheadle, A. E. Berger, S. C. Mathai, D. N. Grigoryev, T. N. Watkins, Y. Sugawara, et al., Erythroid-specific transcriptional changes in PBMCs from pulmonary hypertension patients, PloS One., 7 (2012), e34951. https://doi.org/10.1371/journal.pone.0034951 doi: 10.1371/journal.pone.0034951
![]() |
[32] |
D. Wu, C. C. Talbot, Q. Liu, Z. Jing, R. L. Damico, R. Tuder et al., Identifying microRNAs targeting Wnt/β-catenin pathway in end-stage idiopathic pulmonary arterial hypertension, J. Mol. Med., 94 (2016), 875–885. https://doi.org/10.1007/s00109-016-1426-z doi: 10.1007/s00109-016-1426-z
![]() |
[33] |
J. Xia, E. E. Gill, R. E. W. Hancock, NetworkAnalyst for statistical, visual and network-based meta-analysis of gene expression data, Nat. Protoc., 10 (2015), 823–844. https://doi.org/10.1038/nprot.2015.052 doi: 10.1038/nprot.2015.052
![]() |
[34] |
W. E. Johnson, C. Li, A. Rabinovic, Adjusting batch effects in microarray expression data using empirical bayes methods, Biostatistics, 8 (2007), 118–127. https://doi.org/10.1093/biostatistics/kxj037 doi: 10.1093/biostatistics/kxj037
![]() |
[35] |
M. E. Ritchie, B. Phipson, D. Wu, Y. Hu, C. W. Law, W. Shi, et al., limma powers differential expression analyses for RNA-sequencing and microarray studies, Nucleic Acids Res., 43 (2015), e47. https://doi.org/10.1093/nar/gkv007 doi: 10.1093/nar/gkv007
![]() |
[36] |
A. Subramanian, P. Tamayo, V. K. Mootha, S. Mukherjee, B. L. Ebert, M. A. Gillette, et al., Gene set enrichment analysis: a knowledge-based approach for interpreting genome-wide expression profiles, PNAS, 102 (2005), 15545–15550. https://doi.org/10.1073/pnas.0506580102 doi: 10.1073/pnas.0506580102
![]() |
[37] |
D. Szklarczyk, A. L. Gable, D. Lyon, A. Junge, S. Wyder, J. Huerta-Cepas, et al., STRING v11: protein-protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets, Nucleic Acids Res., 47 (2019), 607–613. https://doi.org/10.1093/nar/gky1131 doi: 10.1093/nar/gky1131
![]() |
[38] |
C. Chin, S. Chen, H. Wu, C. Ho, M. Ko, C. Lin, CytoHubba: identifying hub objects and sub-networks from complex interactome, BMC Syst. Biol., 8 (2014), S11. https://doi.org/10.1186/1752-0509-8-S4-S11 doi: 10.1186/1752-0509-8-S4-S11
![]() |
[39] |
J. Wang, R. Akter, M. F. Shahriar, M. N. Uddin, Cancer-Associated Stromal Fibroblast-Derived Transcriptomes Predict Poor Clinical Outcomes and Immunosuppression in Colon Cancer, Pathol. Oncol. Res., (2022). https://doi.org/10.3389/pore.2022.1610350 doi: 10.3389/pore.2022.1610350
![]() |
[40] |
P. Shannon, A. Markiel, O. Ozier, N. S. Baliga, J. T. Wang, D. Ramage, et al., Cytoscape: a software environment for integrated models of biomolecular interaction networks, Genome Res., 13 (2003), 2498–2504. https://doi.org/10.1101/gr.1239303 doi: 10.1101/gr.1239303
![]() |
[41] |
Y. Fan, K. Siklenka, S. K. Arora, P. Ribeiro, S. Kimmins, J. Xia, MiRNet-dissecting miRNA-target interactions and functional associations through network-based visual analysis, Nucleic Acids Res., 44 (2016), 135–141. https://doi.org/10.1093/nar/gkw288 doi: 10.1093/nar/gkw288
![]() |
[42] |
X. Robin, N. Turck, A. Hainard, N. Tiberti, F. Lisacek, J. Sanchez, M. Müller, PROC: an open-source package for R and S+ to analyze and compare ROC curves, BMC Bioinformatics., 77 (2011). https://doi.org/10.1186/1471-2105-12-77 doi: 10.1186/1471-2105-12-77
![]() |
[43] |
J. Wang, M. N. Uddin, J. Hao, R. Chen, Y. Xiang, D. Xiong, et al., Identification of potential novel prognosis-related genes through transcriptome sequencing, bioinformatics analysis, and clinical validation in acute myeloid leukemia, Front. Genet., 12 (2021). https://doi.org/10.3389/fgene.2021.723001 doi: 10.3389/fgene.2021.723001
![]() |
[44] |
M. N. Uddin, R. Akter, M. Li, Z. Abdelrahman, Expression of SARS-COV-2 cell receptor gene ACE2 is associated with immunosuppression and metabolic reprogramming in lung adenocarcinoma based on bioinformatics analyses of gene expression profiles, Chem. Biol. Interact., 335 (2021), 109370. https://doi.org/10.1016/j.cbi.2021.109370 doi: 10.1016/j.cbi.2021.109370
![]() |
[45] |
K. C. Cotto, A. H. Wagner, Y. Feng, S. Kiwala, A. C. Coffman, G. Spies, et al., DGIdb 3.0: A redesign and expansion of the drug-gene interaction database, Nucleic Acids Res., 46 (2018), 1068–1073. https://doi.org/10.1093/nar/gkx1143 doi: 10.1093/nar/gkx1143
![]() |
[46] |
X. Mao, Z. Ren, G. N. Parker, H. Sondermann, M. A. Pastorello, W. Wang, et al., Structural bases of unphosphorylated STAT1 association and receptor binding, Mol. Cell, 17 (2005), 761–771. https://doi.org/10.1016/j.molcel.2005.02.021 doi: 10.1016/j.molcel.2005.02.021
![]() |
[47] |
A. Yamamura, M. J. Nayeem, A. A. Mamun, R. Takahashi, H. Hayashi, M. Sato, Platelet-derived growth factor up-regulates Ca2+-sensing receptors in idiopathic pulmonary arterial hypertension, FASEB J., 33 (2019), 7363–7374. https://doi.org/10.1096/fj.201802620R doi: 10.1096/fj.201802620R
![]() |
[48] |
S. Gairhe, K. S. Awad, E. J. Dougherty, G. A. Ferreyra, S. Wang, Z. Yu, et al., Type Ⅰ interferon activation and endothelial dysfunction in caveolin-1 insufficiency-associated pulmonary arterial hypertension, PNAS, 118 (2021). https://doi.org/10.1073/pnas.2010206118 doi: 10.1073/pnas.2010206118
![]() |
[49] |
A. D. Stefano, G. Caramori, A. Capelli, I. Gnemmi, F. L. Ricciardolo, T. Oates, et al., STAT4 activation in smokers and patients with chronic obstructive pulmonary disease, Eur. Respir. J., 24 (2004), 78–85. https://doi.org/10.1183/09031936.04.00080303 doi: 10.1183/09031936.04.00080303
![]() |
[50] |
H. Alam, N. Li, S. S. Dhar, S. J. Wu, J. Lv, K. Chen, et al., HP1γ promotes lung adenocarcinoma by downregulating the transcription-repressive regulators NCOR2 and ZBTB7A, Cancer Res., 78 (2018), 3834–3848. https://doi.org/10.1158/0008-5472.CAN-17-3571 doi: 10.1158/0008-5472.CAN-17-3571
![]() |
[51] |
Y. Yang, H. Yuan, J. G. Edwards, Y. Skayian, K. Ochani, E. J. Miller, et al., Deletion of STAT5a/b in vascular smooth muscle abrogates the male bias in hypoxic pulmonary hypertension in mice: Implications in the human disease, Mol. Med., 20 (2014), 625–638. https://doi.org/10.2119/molmed.2014.00180 doi: 10.2119/molmed.2014.00180
![]() |
[52] |
T. Hashimoto-Kataoka, N. Hosen, T. Sonobe, Y. Arita, T. Yasui, T. Masaki, et al., Interleukin-6/interleukin-21 signaling axis is critical in the pathogenesis of pulmonary arterial hypertension, PNAS, 112 (2015), 2677–2686. https://doi.org/10.1073/pnas.1424774112 doi: 10.1073/pnas.1424774112
![]() |
[53] |
E. Zhao, H. Xie, Y. Zhang, Identification of differentially expressed genes associated with idiopathic pulmonary arterial hypertension by integrated bioinformatics approaches, J. Comput. Biol., 28 (2021), 79–88. https://doi.org/10.1089/cmb.2019.0433 doi: 10.1089/cmb.2019.0433
![]() |
[54] |
W. Wang, Z. Jiang, D. Zhang, L. Fu, R. Wan, K. Hong, Comparative transcriptional analysis of pulmonary arterial hypertension associated with three different diseases, Front. Cell Dev. Biol., 9 (2021). https://doi.org/10.3389/fcell.2021.672159 doi: 10.3389/fcell.2021.672159
![]() |
[55] |
H. Göös, M. Kinnunen, K. Salokas, Z. Tan, X. Liu, L. Yadav, et al., Human transcription factor protein interaction networks, Nat. Commun., 13 (2022), 766. https://doi.org/10.1038/s41467-022-28341-5 doi: 10.1038/s41467-022-28341-5
![]() |
[56] |
Q. Yang, C. Jia, P. Wang, M. Xiong, J. Cui, L. Li, et al., MicroRNA-505 identified from patients with essential hypertension impairs endothelial cell migration and tube formation, Int. J. Cardiol., 177 (2014), 925–934. https://doi.org/10.1016/j.ijcard.2014.09.204 doi: 10.1016/j.ijcard.2014.09.204
![]() |
[57] |
H. Wang, Z. Ma, X. Liu, C. Zhang, Y. Hu, L. Ding, et al., MiR-183-5p is required for non-small cell lung cancer progression by repressing PTEN, Biomed. Pharmacother., 111 (2019), 1103–1111. https://doi.org/10.1016/j.biopha.2018.12.115 doi: 10.1016/j.biopha.2018.12.115
![]() |
[58] |
J. Li, S. Sun, N. Li, P. Lv, S. Xie, P. Wang, MiR-205 as a promising biomarker in the diagnosis and prognosis of lung cancer, Oncotarget, 8 (2017), 91938–91949. https://doi.org/10.18632/oncotarget.20262 doi: 10.18632/oncotarget.20262
![]() |
[59] |
Y. Zhao, J. Zhang, J. Yang, Y. Wei, J. Peng, C. Fu, et al., MiR-205-5p promotes lung cancer progression and is valuable for the diagnosis of lung cancer, Thorac Cancer, 13 (2022), 832–843. https://doi.org/10.1111/1759-7714.14331 doi: 10.1111/1759-7714.14331
![]() |
[60] |
W. Liu, X. Wan, Z. Mu, F. Li, L. Wang, J. Zhao, et al., MiR-1256 suppresses proliferation and migration of non-small cell lung cancer via regulating TCTN1, Oncol. Lett., 16 (2018), 1708–1714. https://doi.org/10.3892/ol.2018.8794 doi: 10.3892/ol.2018.8794
![]() |
[61] |
H. El Chami, P. M. Hassoun, Immune and inflammatory mechanisms in pulmonary arterial hypertension, Prog. Cardiovasc. Dis., 55 (2012), 218–228. https://doi.org/10.1016/j.pcad.2012.07.006 doi: 10.1016/j.pcad.2012.07.006
![]() |
[62] |
N. M. Patel, S. M. Kawut, S. Jelic, S. M. Arcasoy, D. J. Lederer, A. C. Borczuk, Pulmonary arteriole gene expression signature in idiopathic pulmonary fibrosis, Eur. Respir. J., 41 (2013), 1324–1330. https://doi.org/10.1183/09031936.00084112 doi: 10.1183/09031936.00084112
![]() |
[63] |
H. Yang, Y. Lu, H. Yang, Y. Zhu, Y. Tang, L. Li, et al., Integrated weighted gene co-expression network analysis uncovers STAT1(signal transducer and activator of transcription 1) and IFI44L (interferon-induced protein 44-like) as key genes in pulmonary arterial hypertension, Bioengineered, 12 (2021), 6021–6034. https://doi.org/10.1080/21655979.2021.1972200 doi: 10.1080/21655979.2021.1972200
![]() |
[64] |
L. Gabryšová, M. Alvarez-Martinez, R. Luisier, L. S. Cox, J. Sodenkamp, C. Hosking, et al., C-Maf controls immune responses by regulating disease-specific gene networks and repressing IL-2 in CD4+ T cells, Nat. Immunol., 19 (2018), 497–507. https://doi.org/10.1038/s41590-018-0083-5 doi: 10.1038/s41590-018-0083-5
![]() |
[65] |
X. Yang, C. Wang, Y. Lin, P. Zhang, Identification of crucial hub genes and differential T cell infiltration in idiopathic pulmonary arterial hypertension using bioinformatics strategies, Front. Mol. Biosci., 9 (2022). https://doi.org/10.3389/fmolb.2022.800888 doi: 10.3389/fmolb.2022.800888
![]() |
[66] |
S. Ni, T. Ji, J. Dong, F. Chen, H. Feng, H. Zhao, et al., Immune cells in pulmonary arterial hypertension, Heart Lung Circ., 31 (2022), 934–943. https://doi.org/10.1016/j.hlc.2022.02.007 doi: 10.1016/j.hlc.2022.02.007
![]() |
[67] |
M. Rabinovitch, C. Guignabert, M. Humbert, M. R. Nicolls, Inflammation and immunity in the pathogenesis of pulmonary arterial hypertension, Circ. Res., 115 (2014), 165–175. https://doi.org/10.1161/CIRCRESAHA.113.301141 doi: 10.1161/CIRCRESAHA.113.301141
![]() |
[68] |
M. Masullo, M. Menegazzi, S. Di Micco, P. Beffy, G. Bifulco, M. Dal Bosco, et al., Direct interaction of garcinol and related polyisoprenylated benzophenones of Garcinia cambogia fruits with the transcription factor STAT-1 as a likely mechanism of their inhibitory effect on cytokine signaling pathways, J. Nat. Prod., 77 (2014), 543–549. https://doi.org/10.1021/np400804y doi: 10.1021/np400804y
![]() |
[69] |
M. Toshner, E. Spiekerkoetter, H. Bogaard, G. Hansmann, S. Nikkho, K. W. Prins, Repurposing of medications for pulmonary arterial hypertension, Pulm. Circ., 10 (2020). https://doi.org/10.1177/2045894020941494 doi: 10.1177/2045894020941494
![]() |
[70] |
R. Papp, C. Nagaraj, D. Zabini, B. M. Nagy, M. Lengyel, D. S. Maurer, et al., Targeting TMEM16A to reverse vasoconstriction and remodelling in idiopathic pulmonary arterial hypertension, Eur. Respir. J., 53 (2019). https://doi.org/10.1183/13993003.00965-2018 doi: 10.1183/13993003.00965-2018
![]() |
![]() |
![]() |