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Aggrephagy-related gene signature correlates with survival and tumor-associated macrophages in glioma: Insights from single-cell and bulk RNA sequencing

  • These two authors contributed equally
  • Background: Aggrephagy is a lysosome-dependent process that degrades misfolded protein condensates to maintain cancer cell homeostasis. Despite its importance in cellular protein quality control, the role of aggrephagy in glioma remains poorly understood. Objective: To investigate the expression of aggrephagy-related genes (ARGs) in glioma and in different cell types of gliomas and to develop an ARGs-based prognostic signature to predict the prognosis, tumor microenvironment, and immunotherapy response of gliomas. Methods: ARGs were identified by searching the Reactome database. We developed the ARGs-based prognostic signature (ARPS) using data from the Cancer Genome Atlas (TCGA, n = 669) by Lasso-Cox regression. We validated the robustness of the signature in clinical subgroups and CGGA cohorts (n = 970). Gene set enrichment analysis (GSEA) was used to identify the pathways enriched in ARPS subgroups. The correlations between ARGs and macrophages were also investigated at single cell level. Results: A total of 44 ARGs showed heterogeneous expression among different cell types of gliomas. Five ARGs (HSF1, DYNC1H1, DYNLL2, TUBB6, TUBA1C) were identified to develop ARPS, an independent prognostic factor. GSEA showed gene sets of patients with high-ARPS were mostly enriched in cell cycle, DNA replication, and immune-related pathways. High-ARPS subgroup had higher immune cell infiltration states, particularly macrophages, Treg cells, and neutrophils. APRS had positive association with tumor mutation burden (TMB) and immunotherapy response predictors. At the single cell level, we found ARGs correlated with macrophage development and identified ARGs-mediated macrophage subtypes with distinct communication characteristics with tumor cells. VIM+ macrophages were identified as pro-inflammatory and had higher interactions with malignant cells. Conclusion: We identified a novel signature based on ARGs for predicting glioma prognosis, tumor microenvironment, and immunotherapy response. We highlight the ARGs-mediated macrophages in glioma exhibit classical features.

    Citation: Xiaowei Zhang, Jiayu Tan, Xinyu Zhang, Kritika Pandey, Yuqing Zhong, Guitao Wu, Kejun He. Aggrephagy-related gene signature correlates with survival and tumor-associated macrophages in glioma: Insights from single-cell and bulk RNA sequencing[J]. Mathematical Biosciences and Engineering, 2024, 21(2): 2407-2431. doi: 10.3934/mbe.2024106

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  • Background: Aggrephagy is a lysosome-dependent process that degrades misfolded protein condensates to maintain cancer cell homeostasis. Despite its importance in cellular protein quality control, the role of aggrephagy in glioma remains poorly understood. Objective: To investigate the expression of aggrephagy-related genes (ARGs) in glioma and in different cell types of gliomas and to develop an ARGs-based prognostic signature to predict the prognosis, tumor microenvironment, and immunotherapy response of gliomas. Methods: ARGs were identified by searching the Reactome database. We developed the ARGs-based prognostic signature (ARPS) using data from the Cancer Genome Atlas (TCGA, n = 669) by Lasso-Cox regression. We validated the robustness of the signature in clinical subgroups and CGGA cohorts (n = 970). Gene set enrichment analysis (GSEA) was used to identify the pathways enriched in ARPS subgroups. The correlations between ARGs and macrophages were also investigated at single cell level. Results: A total of 44 ARGs showed heterogeneous expression among different cell types of gliomas. Five ARGs (HSF1, DYNC1H1, DYNLL2, TUBB6, TUBA1C) were identified to develop ARPS, an independent prognostic factor. GSEA showed gene sets of patients with high-ARPS were mostly enriched in cell cycle, DNA replication, and immune-related pathways. High-ARPS subgroup had higher immune cell infiltration states, particularly macrophages, Treg cells, and neutrophils. APRS had positive association with tumor mutation burden (TMB) and immunotherapy response predictors. At the single cell level, we found ARGs correlated with macrophage development and identified ARGs-mediated macrophage subtypes with distinct communication characteristics with tumor cells. VIM+ macrophages were identified as pro-inflammatory and had higher interactions with malignant cells. Conclusion: We identified a novel signature based on ARGs for predicting glioma prognosis, tumor microenvironment, and immunotherapy response. We highlight the ARGs-mediated macrophages in glioma exhibit classical features.



    Let q be an integer. For each integer a with

    1a<q,  (a,q)=1,

    we know that [1] there exists one and only one ˉa with

    1ˉa<q

    such that

    aˉa1(q).

    Define

    R(q):={a:1aq,(a,q)=1,2a+ˉa},
    r(q):=#R(q).

    The work [2] posed the problem of investigating a nontrivial estimation for r(q) when q is an odd prime. Zhang [3,4] gave several asymptotic formulas for r(q), one of which is:

    r(q)=12ϕ(q)+O(q12d2(q)log2q),

    where ϕ(q) is the Euler function and d(q) is the divisor function. Lu and Yi [5] studied a generalization of the Lehmer problem over short intervals. Let n2 be a fixed positive integer, q3 and c be integers with

    (nc,q)=1.

    They defined

    rn(θ1,θ2,c;q)=#{(a,b)[1,θ1q]×[1,θ2q]abc( mod q),na+b},

    where 0<θ1,θ21, and obtained

    rn(θ1,θ2,c;q)=(11n)θ1θ2φ(q)+O(q1/2τ6(q)log2q),

    where the O constant depends only on n. In addition, Xi and Yi [6] considered generalized Lehmer problem over short intervals. Han and Liu [7] gave an upper bound estimation for another generalization of the Lehmer problem over incomplete interval.

    Guo and Yi [8] also found the Lehmer problem has good distribution properties on Beatty sequences. For fixed real numbers α and β, defined by

    Bα,β:=(αn+β)n=1.

    Beatty sequences are linear sequences. Based on the results obtained, we conjecture the Lehmer problem also has good distribution properties in some non-linear sequences.

    The Piatetski-Shapiro sequence is a non-linear sequence, defined by

    Nc={nc:nN},

    where cR is non-integer with c>1 and zR. This sequence was first introduced by Piatetski-Shapiro [9] to study prime numbers in sequences of the form f(n), where f(n) is a polynomial. A positive integer is called square-free if it is a product of distinct primes. The distribution of square-free numbers in the Piatetski-Shapiro sequence has been studied extensively. Stux [10] found that, as x tends to infinity,

    nxnc is square-free 1=(6π2+o(1))x, for  1<c<43. (1.1)

    In 1978, Rieger [11] improved the range to 1<c<3/2 and obtained

    6xπ2+O(x(2c+1)/4+ε), for  1<c<32

    Considering the results obtained, we develop this problem by investigating

    R(c;q):=nNcR(q)n is square-free 1

    and range of c when q tends to infinity. By methods of exponential sum and Kloosterman sums and fairly detailed calculations, we get the following result, which is significant for understanding the distribution properties of the Lehmer problem.

    Theorem 1.1. Let q be an odd integer and large enough,

    γ:=1/candc(1,43),

    we obtain

    R(c;q)=3π2pq(1+p1)1qγ+O(pq(1p12)1qγ12)+O(q713γ+413pq(1p12)1logq)+O(q34d3(q)logq)+O(qγ16d2(q)log3q),

    where the O constant only depends on c.

    This paper consists of three main sections. Introduction covers the origins and developments of the Lehmer problem, along with several interesting results. It also presents relevant findings related to the Piatetski-Shapiro sequences. The second section includes some definitions and lemmas throughout the paper. The third section outlines the calculation process, where we use additive characteristics to convert the congruence equations into exponential sum problems. We then employ the Kloosterman sums and exponential sums methods to derive an interesting asymptotic formula.

    To complete the proof of the theorem, we need the following several definitions and lemmas.

    In this paper, we denote by t and {t} the integral part and the fractional part of t, respectively. As is customary, we put

    e(t):=e2πitand{t}:=tt.

    The notation t is used to denote the distance from the real number t to the nearest integer; that is,

    t:=minnZ|tn|.

    And indicates that the variable summed over takes values coprime to the number q. Throughout the paper, ε always denotes an arbitrarily small positive constant, which may not be the same at different occurrences; the implied constants in symbols O,, and may depend (where obvious) on the parameters c and ε, but are absolute otherwise. For given functions F and G, the notations

    FG,   GF   andF=O(G)

    are all equivalent to the statement that the inequality

    |F|C|G|

    holds with some constant C>0.

    Lemma 2.1. Let 1c(m) denote the characteristic function of numbers in a Piatetski-Shapiro sequence, then

    1c(m)=γmγ1+O(mγ2)+ψ((m+1)γ)ψ(mγ),

    where

    ψ(t)=tt12   andγ=1/c.

    Proof. Note that an integer m has the form

    m=nc

    for some integer n if and only if

    mnc<m+1,(m+1)γ<nmγ.

    So

    1c(m)=mγ(m+1)γ=mγψ(mγ)+(m+1)γ+ψ((m+1)γ)=γmγ1+O(mγ2)+ψ((m+1)γ)ψ(mγ).

    Thie completes the proof.

    Lemma 2.2. Let H1 be an integer, ah,bh be real numbers, we have

    |ψ(t)0<|h|Hahe(th)||h|Hbhe(th),ah1|h|,bh1H.

    Proof. In 1985, Vaaler showed how Beurling's function could be used to construct a trigonometric polynomial approximation to ψ(x). For each positive integer N, Vaaler's construction yields a trigonometric polynomial ψ of degree N which satisfies

    |ψ(x)ψ(x)|12N+2|n|N(1|n|N+1)e(nx),

    where

    ψ(x)=1|n|N(2πin)1ˆJN+1(n)e(nx),H(z)=sin2πzπ2{n=sgn(n)(zn)2+2z},J(z)=12H(z),HN(z)=sin2πzπ2{|n|Nsgn(n)(zn)2+2z},JN(z)=12HN(z),

    and sgn(n) is the sign of n. The Fourier transform ˆJ(t) satisfies

    ˆJ(t)={1,t=0;πt(1|t|)cotπt+|t|,0<|t|<1;0,t1.

    To be short, we denote

    ah=(2πih)1ˆJH+1(h)1|h|,bh=12H+2(1|h|H+1)1H.

    There are more details in Appendix Theorem A.6. of [12].

    Lemma 2.3. Denote

    Kl(m,n;q)=qa=1qb=1ab1(modq)e(ma+nbq),

    then

    Kl(m,n;q)(m,n,q)12q12d(q),

    where (m,n,q) is the greatest common divisor of m,n and q and d(q) is the number of positive divisors of q.

    Proof. The proof is given in [13].

    Lemma 2.4. (Korobov [14]) Let α be a real number, Q be an integer, and P be a positive integer, then

    |Q+Px=Q+1e(αx)|min(P,12α).

    Lemma 2.5. (Karatsuba [15]) For any number b, U<0, K1, let

    a=sr+θr2,(r,s)=1,   r1,   |θ|1,

    then

    kKmin(U,1ak+b)(Kr+1)(U+rlogr).

    Lemma 2.6. Suppose f is continuously differentiable, f(n) is monotonic, and

    f(n)λ1>0

    on I, then

    nIe(f(n))λ11.

    Proof. See [12, Theorem 2.1].

    Lemma 2.7. Let k be a positive integer, k2. Suppose that f(n) is a real-valued function with k continuous derivatives on [N,2N], Further suppose that

    0<Ff(k)(n)hF.

    Then

    |N<x2Ne(f(n))|FκNλ+F1,

    where the implied constant is absolute.

    Proof. See [12, Chapter 3].

    By the definition of Mobius function

    μ(n)={(1)ω(n),p|n,p2n,0,p2|n,

    it is clear that n is square-free if and only if

    μ2(n)=1,

    where ω(n) is the number of prime divisor of n. So

    R(c;q)=12qn=1(1(1)n+ˉn)μ2(n)1c(n)=12(R1R2), (3.1)

    where

    R1=qn=1μ2(n)1c(n)

    and

    R2=qn=1(1)n+ˉnμ2(n)1c(n).

    From Lemma 2.1, we have

    R1=qn=1μ2(n)1c(n)=qn=1μ2(n)(γnγ1+O(nγ2)+ψ((n+1)γ)ψ(nγ))=R11+R12, (3.2)

    where

    R11:=qn=1μ2(n)(γnγ1+O(nγ2))=qn=1μ2(n)γnγ1+O(qn=1μ2(n)nγ2).

    Let

    D={d:p|dp|q}

    and λ(n) is Liouville function. When nR(q),

    μ2(n)={dm=n,dDλ(d)μ2(m),(n,q)=1,0,(n,q)>1. (3.3)

    We just consider the first term of R11. Applying Euler summuation [1],

    qn=1μ2(n)γnγ1=qn=1dm=ndDλ(d)μ2(m)γ(dm)γ1=dDλ(d)dγ1mqdμ2(m)γmγ1=dDλ(d)dγ1mqd(l2mμ(l))γmγ1=dDλ(d)dγ1l(qd)12μ(l)l2γ2mqdl2γmγ1=dDλ(d)dγ1l(qd)12μ(l)l2γ2((qdl2)γ+O((qdl2)γ1))=qγdDλ(d)d1l(qd)12μ(l)l2+O(dDl(qd)12qγ1)=qγdDλ(d)d1(lμ(l)l2+O((qd)12))+O(pq(1p12)1qγ12)=6π2pq(1+p1)1qγ+O(pq(1p12)1qγ12),

    thus

    R11=6π2pq(1+p1)1qγ+O(pq(1p12)1qγ12). (3.4)

    For R12, by Lemma 2.2, we have

    R12:=qn=1μ2(n)(ψ((n+1)γ)ψ(nγ))=R121+O(R122), (3.5)

    where

    R121:=qn=1μ2(n)(0<|h|Hah(e((n+1)γh)e(nγh)))

    and

    R122:=qn=1μ2(n)(|h|Hbh(e((n+1)γh)+e(nγh))).

    Define

    f(t)=e(((dt)γ(dt+1)γ)h)1,

    then

    f(t) |h|(dt)γ1,f(t)t|h|dγ1tγ2.

    By Lemma 2.2 and Eq (3.3),

    R121=0<|h|HahdDλ(d)(1<mqdμ2(m)e((dm)γh)f(m))0<|h|H|h|1dD|qd0f(t)d(1<mtμ2(m)e((dm)γh))|0<|h|H|h|1dD|f(qd)(1<mqdμ2(m)e((dm)γh))|+0<|h|H|h|1dD|qd0f(t)t1<mtμ2(m)e((dm)γh)dt|.

    Let

    (κ,λ)=(16,23)

    be an exponential pair. Applying Lemma 2.7, it's easy to see

    1<mtμ2(m)e((dm)γh)=mt(l2mμ(l))e((dm)γh)lt12|mtl2e((dl2m)γh)|lt12logq(((dl2)γ|h|(tl2)γ1)16(tl2)23+((dl2)γ|h|(tl2)γ1)1)logqlt12((dγ|h|)16t16γ+12l1+(dγ|h|)1t1γl2)(dγ|h|)16t16γ+12log2q+(dγ|h|)1t1γlogq(dγ|h|)16t16γ+12log2q,

    thus

    R1210<|h|H|h|1dD|h|qγ1(dγ|h|)16(qd)16γ+12log2q+0<|h|H|h|1dDqd0|h|dγ1tγ2(dγ|h|)16t16γ+12log2qdt0<|h|H|h|16dDd12q76γ12log2q+0<|h|H|h|16dDd76γ1log2qqd0t76γ32dtH76pq(1p12)1q76γ12log2q. (3.6)

    For R122, the contribution from h0 can be bounded by similar methods of Eq (3.6). Taking

    H=q913713γ1,

    we obtain

    R122=b0qn=1μ2(n)+0<|h|Hbhqn=1μ2(n)(e((n+1)γh)+e(nγh))H1q+H76pq(1p12)1q76γ12logqq713γ+413pq(1p12)1log2q. (3.7)

    It follows from Eqs (3.5)–(3.7),

    R12q713γ+413pq(1p12)1log2q.

    Hence

    R1=6π2pq(1+p1)1qγ+O(pq(1p12)1qγ12)+O(q713γ+413pq(1p12)1log2q). (3.8)

    Similarly,

    R2=qn=1(1)n+ˉnμ2(n)1c(n)=RP21+RP22, (3.9)

    where

    R21=qn=1(1)n+ˉnμ2(n)(γnγ1+O(nγ2))

    and

    R22=qn=1(1)n+ˉnμ2(n)(ψ((n+1)γ)ψ(nγ)).

    We also just consider the first term of R21.

    qn=1(1)n+ˉnμ2(n)γnγ1=qn=1(1)n+ˉn(d2nμ(d))γnγ1=qn=1(1)n+ˉnd2ndq14μ(d)γnγ1+qn=1(1)n+ˉnd2nq14<dq12μ(d)γnγ1. (3.10)

    It is easy to see

    qn=1(1)n+ˉnd2nq14<dq12μ(d)γnγ1qn=1d2nq14<dq12γnγ1qγ14. (3.11)

    Since for integers m and a, one has

    1qqs=1e(s(ma)q)={1,ma( mod q);0,ma( mod q).

    This gives

    qn=1(1)n+ˉnd2ndq14μ(d)γnγ1=qn=1qm=1nm1(modq)(1)n+md2ndq14μ(d)γnγ1q1a=1a=mq1b=1b=n1=qn=1qm=1nm1(modq)q1a=1a=mq1b=1b=n(1)a+bd2bdq14μ(d)γbγ1=qn=1qm=1nm1(modq)q1a=1am(modq)q1b=1bn(modq)(1)a+bd2bdq14μ(d)γbγ1=qn=1qm=1nm1(modq)q1a=1q1b=1(1)a+bd2bdq14μ(d)γbγ1×(1qqs=1e(s(ma)q))(1qqt=1e(t(nb)q))=1q2qs=1qt=1(nm1(modq)e(sm+tnq))×(q1a=1(1)ae(saq))(q1b=1(1)be(tbq)d2bdq14μ(d)γbγ1). (3.12)

    From Lemma 2.3,

    nm1(modq)e(sm+tnq)=Kl(s,t;q)(s,t,q)12q12d(q). (3.13)

    Note the estimate

    |q1a=1(1)ae(saq)|1|e(12sq)1|1|cossqπ| (3.14)

    holds. By Abel summation and Lemma 2.4, we have

    q1b=1(1)be(tbq)d2bdq14μ(d)γbγ1=dq14μ(d)q1d2b=1(1)d2be(td2bq)γ(d2b)γ1dq14d2(γ1)|q1d2b=1(1)d2be(td2bq)γbγ1|dq14d2(γ1)γ(qd2)γ1max1βq1d2|βb=1(1)d2be(td2bq)|dq142dγqγ1max1βq1d2|βb=1e(td2bq)|+dq142dγqγ1max1βq1d2|βb=1(1)be(td2bq)|dq142dqγ1min(q1d2,12d2qt)+dq142dqγ1min(q1d2,1212d2qt). (3.15)

    To be short, combining Eqs (3.13)–(3.15), we denote

    R211:=q2qs=1qt=1(s,t,q)12d(q)q121|cossqπ|dq142dqγ1min(q1d2,12d2qt)qγ3qs=1qt=1(s,t,q)12d(q)q121|cossqπ|dq14min(q1d2,12d2qt)qγ3uqu12d(q)q12qus=11|cossuqπ|dq14qut=1min(q1d2,12d2qut)

    and

    R212:=q2qs=1qt=1(s,t,q)12d(q)q121|cossqπ|dq142dqγ1min(q1d2,1212d2qt).

    Let

    (d2,qu)=r,   (d2r,qur)=1,

    making use of Lemma 2.5, we have

    qut=1min(q1d2,12d2qut)(ququr+1)(q1d2+qurlogq)qrd2+qulogq.

    Insert it to R211, then

    R211qγ3uqu12d(q)q12qus=11|12suq|dq14(d2,qu)=r(qrd2+qulogq)qγ3uqu12d(q)q12qulogqdq14r|d2r|qu(qrd2+qulogq)qγ3uqu12d(q)q12qulogqr|qudq14r12(qd2+qulogq)qγ3uqu12d(q)q12qulogq(qd(q)+q54ud(q)logq)qγ14d3(q)log3q.

    By the same method of R211,

    R212qγ14d3(q)log3q.

    Following from Eqs (3.10) and (3.11), estimations of R211 and R212,

    R21qγ14+RP211+RP212qγ14d3(q)log3q. (3.16)

    By the similar method of R12 and R21,

    R22=R221+O(R222), (3.17)

    where

    R221:=qn=1(1)n+ˉnμ2(n)(0<|h|Hah(e((n+1)γh)e(nγh)))

    and

    R222:=qn=1(1)n+ˉnμ2(n)(|h|Hbh(e((n+1)γh)+e(nγh))).

    It is obvious that

    R221=qn=1(1)n+ˉn(d2nμ(d))(0<|h|Hah(e((n+1)γh)e(nγh)))=qn=1(1)n+ˉnd2ndq16μ(d)(0<|h|Hah(e((n+1)γh)e(nγh)))+qn=1(1)n+ˉnd2nq16<dq12μ(d)(0<|h|Hah(e((n+1)γh)e(nγh))). (3.18)

    From the estimate

    e(nγh(n+1)γh)1(nγ(n+1)γ)hγnγ1h,

    by partial summation,

    qn=1(1)n+ˉnd2nq16<dq12μ(d)(0<|h|Hah(e((n+1)γh)e(nγh)))qn=1d2nq16<dq12|0<|h|Hahe(nγh)(e(nγh(n+1)γh)1)|qn=1d2nq16<dq12γnγ1HlogHqγ16HlogH. (3.19)

    For another term of R221,

    qn=1(1)n+ˉnd2ndq16μ(d)(0<|h|Hah(e((n+1)γh)e(nγh)))=qn=1(1)n+ˉnd2ndq16μ(d)(0<|h|Hah(e((n+1)γh)e(nγh)))×(1qqa=1qs=1e(s(ma)q))(1qqb=1qt=1e(t(nb)q))=1q2qs=1qt=1(nm1(modq)e(sm+tnq))(q1a=1(1)ae(saq))×(q1b=1(1)be(tbq)d2bdq16μ(d)0<|h|Hah(e((b+1)γh)e(bγh))). (3.20)

    We just need to give an estimation of the last part in (3.20). Similarly, let

    g(x)=e(((d2x)γ(d2x+1)γ)h)1,

    then

    g(x) |h|(d2x)γ1,g(x)x|h|d2γ2xγ2.

    By partial summation,

    q1b=1(1)be(tbq)d2bdq16μ(d)0<|h|Hah(e((b+1)γh)e(bγh))=0<|h|Hahdq16μ(d)1bq1d2e((d22td2q)b(d2b)γh)g(b),0<|h|H|h|1dq16|q1d21g(x)d(1<bxe((d22td2q)b(d2b)γh))|0<|h|H|h|1dq16|g(q1d2)1bq1d2e((d22td2q)b(d2b)γh)|+0<|h|H|h|1dq16|q1d21g(x)x1bxe((d22td2q)b(d2b)γh)dx|,

    where

    g(q1d2)|h|qγ1

    and

    1bq1d2e((d22td2q)b(d2b)γh)=1bq16e((d22td2q)b(d2b)γh)+q16<bq1d2e((d22td2q)b(d2b)γh).

    It is obvious that

    1bq16e((d22td2q)b(d2b)γh)q16.

    Suppose q be large enough and for b>q16, when 2d or qtd2,

    (d22td2q)γd2γbγ1h112(12tq)d21>0,

    and applying Lemma 2.6, we have

    q16<bq1d2e((d22td2q)b(d2b)γh)(d22td2q)γd2γbγ1h1(12tq)d21.

    So

    1bq1d2e((d22td2q)b(d2b)γh){q16+(12tq)d21,2d or qtd2;qd2,2d and qtd2;

    which means

    q1b=1(1)be(tbq)d2bdq16μ(d)0<|h|Hah(e((b+1)γh)e(bγh))0<|h|H|h|1dq162d or qtd2|h|qγ1(q16+(12tq)d21)+0<|h|H|h|1dq162d and qtd2|h|qγd2Hqγ1(q13+dq162d or qtd2(12tq)d21)+Hqγdq162d and qtd2d2.

    We denote

    T(c):=dq16#{(qu2t)d2c(mod2qu),tqu}dq16(qu,d2)q13d(q),

    thus,

    qn=1(1)n+ˉnd2ndq16μ(d)(0<|h|Hah(e((n+1)γh)e(nγh)))q2qs=1qt=1(s,t,q)12d(q)q12Hqγ1|cossqπ|(q13+dq162d or qtd2(12tq)d21)+q2qs=1qt=1(s,t,q)12d(q)q12Hqγ|cossqπ|dq162d and qtd2d2Hqγ52uqu12d(q)qus=11|12suq|qut=1(q13+dq16(12utq)d21)+Hqγ32uqu12d(q)qus=11|12suq|dq16qut=1qtd2d2Hqγ16d2(q)logq+Hqγ13d2(q)logq+Hqγ32uqu12d(q)log2qmaxCC<c2CC2qu1T(c)Hqγ16d2(q)log2q. (3.21)

    With Eqs (3.18) and (3.19), we have

    R221Hqγ16d2(q)log2q+Hqγ16logH. (3.22)

    For R222, the contribution from h=0 can be bounded by similar methods of R21, and the contribution from h0 can be bounded by similar methods of R221. Taking

    H=logq,

    we obtain

    R222=b0qn=1(1)n+ˉnμ2(n)+qn=1(1)n+ˉnμ2(n)(0<|h|Hbh(e((n+1)γh)+e(nγh)))H1q34d3(q)log2q+Hqγ16d2(q)log2qq34d3(q)logq+qγ16d2(q)log3q. (3.23)

    Following from Eqs (3.16), (3.22), and (3.23),

    R2qγ16d2(q)log3q+q34d3(q)logq. (3.24)

    Hence, from Eqs (3.1), (3.8), and (3.24), we derive that

    R(c;q)=3π2pq(1+p1)1qγ+O(pq(1p12)1qγ12)+O(q713γ+413pq(1p12)1logq)+O(q34d3(q)logq)+O(qγ16d2(q)log3q).

    We need the error terms to be smaller than the main term, so

    {713γ+413<γ,34<γ,

    which means the range of c is (1,43). The reason why the range of c is changed is that R(c;q) requires q large enough.

    In this paper, we generalize the Lehmer problem by considering the count of square-free numbers in the intersection of the Lehmer set and Piatetski-Shapiro sequence when q is an odd integer and large enough. By methods of exponential sum and Kloosterman sum, we study its asymptotic properties and give a sharp asymptotic formula as q tends to infinity.

    Based on this result, we will consider some distribution problems similar to the Lehmer problem with more special sequences, which is significant for understanding the distribution properties of those problems.

    Xiaoqing Zhao: calculations, writing and editing; Yuan Yi: methodology and reviewing. All authors have read and agreed to the published version of the manuscript.

    In preparing this manuscript, we employed the language model ChatGPT-4 for the purpose of grammatical corrections. It did not influence the calculations and conclusion in this paper.

    This work is supported by Natural Science Foundation No.12271422 of China. The authors would like to express their gratitude to the referee for very helpful and detailed comments.

    The authors declare that there are no conflicts of interest regarding the publication of this paper.



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