[摘要]"乳腺癌在全球范圍內的發病率持續升高,但其病死率卻逐漸降低,這與乳腺癌診療水平的提高存在密切聯系。影像學技術在乳腺癌的診斷過程中發揮重要作用。在乳腺疾病的檢出和診斷方面,磁共振成像(magnetic"resonance"imaging,MRI)技術比乳腺X線鉬靶、超聲、CT等技術具有更大的優勢。MRI技術在乳腺疾病診斷中的應用價值引起臨床醫師的高度關注。MRI技術的不斷進步和應用推廣使其在乳腺癌的早期發現、分子分型、新輔助化療效果評估、手術前評估及手術后隨訪監測等方面的應用價值逐步得到認可。本文對乳腺癌的流行病學特征及乳腺MRI的成像原理、成像特點和應用現狀等作一綜述,旨在為乳腺癌的早期診斷、治療方案選擇、療效評估及預后隨訪監測提供技術指導和理論支持。
[關鍵詞]"乳腺癌;磁共振成像;診斷
[中圖分類號]"R445.2;R737.9""""""[文獻標識碼]"A""""""[DOI]"10.3969/j.issn.1673-9701.2024.23.031
乳腺癌是一種源于乳腺上皮組織的惡性腫瘤[1]。在全球范圍內,乳腺癌是女性健康的最大威脅者,已成為當今社會的重大公共衛生問題之一。2020年,全球乳腺癌的發病人數和死亡人數約為226萬和68.5萬例,分別占女性惡性腫瘤的24.5%和15.5%[2]。近年來,乳腺癌的發病率呈快速上升趨勢。中國不同地區的經濟發展水平和居民生活習慣導致乳腺癌的發病率存在差異。中國城市女性乳腺癌的年齡標化發病率高于農村[3]。此外,乳腺癌呈現年輕化趨勢[4]。中國乳腺癌的發病和死亡人數較多,社會負擔較重[5]。乳腺癌早期篩查和診療是減輕乳腺癌社會負擔的前提和基礎。乳腺癌的早期診斷主要依賴于影像學技術,乳腺癌的分子異質性和復雜性及形態學上的差異性極大地增加了乳腺癌精準診斷的難度[6-7]。近年來,乳腺磁共振成像(magnetic"resonance"imaging,MRI)技術有了快速發展,其可有效彌補乳腺X線鉬靶、超聲、CT等技術在乳腺疾病診斷方面的不足。乳腺MRI檢查包括多種技術,每種技術診斷乳腺疾病的敏感度和特異性不同。本文對乳腺MRI的成像原理、成像特點和應用現狀等作一綜述。
1""乳腺MRI的成像原理及特點
MRI是一種利用人體內氫質子的磁性特征來獲取圖像的技術。在強磁場作用下,氫質子會排列成一個總磁矩,受射頻脈沖激勵而偏離平衡狀態,出現馳豫現象。當射頻脈沖停止后,氫質子會恢復平衡狀態,同時釋放出無線電信號。這些信號被接收線圈捕獲,經過處理和轉換,最終在顯示器上呈現出人體不同層面的灰度圖像。人體各組織和器官中氫質子含量差異很大,其氫核所產生的T1、T2值不同,可用于區分人體不同組織結構和病變。MRI檢查可對組織進行多序列、多角度和多參數成像[8]。乳腺MRI檢查序列包括常規序列和功能序列。常規序列包括T1加權成像、T2加權成像及T2加權成像壓脂。功能序列包括動態對比增強磁共振成像(dynamic"contrast-enhanced"magnetic"resonance"imaging,DCE-"MRI)、彌散加權成像(diffusion"weighted"imaging,DWI)、彌散張量成像(diffusion"tensor"imaging,DTI)、體素內不相干運動成像、擴散峰度成像、磁共振波譜(magnetic"resonance"spectroscopy,MRS)、磁敏感加權成像、磁共振彈性成像(magnetic"resonance"elastography,MRE)等。
2""乳腺MRI的成像優勢
在臨床工作中,乳腺癌的篩查方式主要有體格檢查和影像學檢查。但有研究表明,體格檢查在乳腺癌的早期診斷中無統計學意義[9-10]。國內外大量研究表明,乳腺組織病理活檢是診斷乳腺癌的金標準。但該檢查價格昂貴、操作過程繁瑣且屬于有創性檢查,術后留有疤痕,會給患者造成不同程度的身心傷害,不易被患者所接受[11]。因此,影像學檢查成為乳腺疾病診斷的主要手段。目前,乳腺癌常用影像學檢查方法包括乳腺X線鉬靶、超聲、紅外線、CT、MRI和核素顯像等[12]。乳腺X線鉬靶具有操作簡便、經濟、無創等特點,而成為乳腺癌篩查和診斷的首選方法;但乳腺X線鉬靶檢查為軟X線攝影,射線穿透力較弱,對致密乳腺組織結構顯示不佳,尤其對乳腺的邊緣部位,如乳頭、乳暈、乳腺深部靠近胸壁和乳腺尾部病變難以清晰顯示,極易造成誤診和漏診;此外,X線鉬靶檢查還存在一定的電離輻射,對人體有潛在危害[13]。乳腺超聲檢查方便、經濟、快捷,可對致密乳腺組織病變的形態、大小、性質、邊緣及其對周邊組織的浸潤情況予以清晰顯示,定位準確;但超聲檢查受主觀因素影響較大,檢查結果很大程度上取決于檢查醫師的臨床經驗,且圖像前后的重復性和可比性較差,直徑<1cm的腫物常被遺漏[14]。乳腺紅外線檢查是一種利用人體熱分布差異獲取可視化圖像的影像學技術。乳腺紅外線檢查具有簡便、無創、靈敏度高、血管影像顯示清晰等優點,可降低漏診的風險;但紅外線檢查也有局限性,其不能清晰反映組織病變的形態、位置和鈣化程度,直徑<2cm乳腺癌組織診斷的準確率較低[15]。乳腺CT檢查密度分辨率高,能避免乳腺組織重疊影響,精確顯示致密乳腺內隱匿病灶的形態、大小和邊緣等情況,通過CT值差異可有效區分囊性、實性和脂肪性腫塊;但當組織成份相近時,通過肉眼或CT值鑒別病變組織較為困難,且CT檢查的輻射劑量較大,易受部分容積效應影響[16-17]。對于新診斷的乳腺癌患者,正電子發射計算機體層顯像儀(positron"emission"tomography"and"computed"tomography,PET/CT)可提供病灶詳細的功能與代謝等信息,提供病灶的精確解剖定位,反映腫瘤的惡性程度。一次PET/CT顯像可獲得全身各方位的斷層圖像,具有靈敏、準確、特異性及定位精確等特點,可達到早期發現病灶和診斷疾病的目的;但PET/CT的輻射劑量也相對較高。相比之下,正電子發射磁共振成像對肝臟和骨轉移的敏感度更高,且輻射劑量只有PET/CT的一半左右[18]。
乳腺MRI檢查對軟組織的分辨率高,在腫瘤的良惡性診斷和分子分型中的應用價值較高,且無電離輻射。乳腺MRI檢查能夠清晰顯示致密型乳腺和假體植入乳腺后的乳腺腫瘤,也能發現X線鉬靶或超聲檢查未能檢出的腫塊,乳腺癌檢出率高,能夠有效降低乳腺腫塊漏診和誤診的發生率。同時,磁共振引導可提高組織活檢的安全性和有效性。乳腺MRI技術不斷改進,特別是在高場設備設備、超高場設備、乳腺專用線圈、柔性線圈、快速成像序列和人工智能(artificial"intelligence,AI)的支持下,其敏感度和特異性都得到顯著提高。乳腺MRI技術能同時對雙側乳腺進行成像,從任意角度均可觀察乳腺結構,不會受到患者體型和病灶位置的限制[19]。乳腺MRI檢查可進行多序列聯合掃描,不同序列功能各異,聯合掃描可彌補各自的不足,如常規MRI對鈣化不敏感,但磁敏感加權成像能夠發現微小鈣化病灶。乳腺MRI不僅可對病變組織進行形態學觀察,還可對病變組織進行功能學評估和定性診斷。DWI可通過判定組織中水分子的隨機運動情況來判定病變組織的性質,用于區分乳腺良性和惡性病變,并可減少傳統DCE-MRI的假陽性結果,減少不必要的活檢操作[20-21]。與傳統DWI相比,DTI可提供有關擴散方向性的更多信息。有研究者在3T"MRI上應用量化的DTI參數區分良性和惡性乳腺病變[22]。體素內不相干運動可使用多b值DWI分析組織的血流灌注和擴散率,通過雙指數曲線擬合得到真實擴散系數、灌注相關擴散系數和灌注分數等參數,有助于區分乳腺病變[23]。擴散峰度成像可描述非高斯分布的擴散特征,使用平均峰度和平均擴散等參數提高乳腺惡性病變診斷的敏感度和準確性。研究表明,與傳統DWI、DCE-MRI相比,平均峰度在乳腺癌檢測方面具有更高的準確性,曲線下面積為0.979,具有預測腫瘤侵襲性成像標志物的潛在效用[24]。MRS是可用來評估組織化學成分的專用MRI序列,MRS通過檢測病變組織中的膽堿及其衍生物分析病變組織的成分,從而判定病變的性質[25]。MRS可作為常規乳腺MRI的補充序列,提高診斷特異性,從而減少不必要的穿刺活檢。另有研究表明,MRS有助于評估乳腺MRI發現的可疑非腫塊樣改變[26]。
3""乳腺MRI在乳腺癌中的應用現狀
乳腺MRI常規序列利用組織的T1和T2值產生圖像對比。功能序列分為兩類,一類是DCE-MRI,通過釓基造影劑反映乳腺腫瘤的血管充盈情況,提供腫瘤血液的動力學信息;另一類是通過提供腫瘤組織的密度、硬度、細胞特征和代謝信息,揭示腫瘤微觀結構的異質性。多項研究表明,乳腺MRI可檢測出大多數腋窩腺癌女性的原發乳腺癌[27-29]。乳腺MRI可術前確定病變范圍,了解腫瘤蔓延及胸肌筋膜、肌肉受累情況,有助于手術計劃的制定[30]。對于存在乳腺癌易感基因1/2突變或其他高危遺傳綜合征患者,術前乳腺MRI有助于發現同側或對側乳腺癌[31-32]。研究指出,時間-信號強度曲線半定量參數聯合DWI可用于區分惡性和良性乳腺病變,其診斷的靈敏度為83.33%,特異度為97.14%[33]。MRE獲得的基于結構的組織硬度定量生物標志物提供彈性、黏度、復剪切模量的幅度和相角參數,這些參數可用于區分良性和惡性乳房腫瘤。研究顯示,使用相角參數可提高乳腺癌病變的診斷準確性,結合MRE和MRI的曲線下面積從僅用MRI"BI-RADS的0.84提升到0.92[34]。
隨著AI技術和影像組學的發展,利用AI手段對影像數據進行分析可提高病變組織檢出和診斷的準確率[35]。Shoshan等[36]研究發現,AI能夠提高放射科醫師的工作效率。在一項多中心回顧性研究中,應用結合機器學習的多參數MRI放射組學分析,研究者可更好地評估臨床乳腺MRI建議活檢的可疑增強乳腺腫瘤,有助于準確診斷乳腺癌,同時減少不必要的良性乳腺組織活檢[37]。Fan等[38]使用一種基于DCE-MRI的放射組學分析技術預測乳腺癌的分子亞型。Peterson等[39]將乳腺MRI放射組學特征連同人口統計學、化療、病理數據輸入TumorScope"Predict生物物理模擬平臺,預測不同亞型乳腺癌對新輔助治療的體積反應。Chitalia等[40]對95例原發性、侵襲性乳腺癌患者術前行DCE-MRI檢查,并進行至少10年隨訪,發現不同影像學表型患者的無復發生存率有顯著差異,高異質性表型患者的預后最差。
雖然乳腺MRI較X線鉬靶、超聲及CT有諸多優勢,但也存在一些局限性,限制了其應用和推廣。乳腺MRI檢查存在一些絕對或相對禁忌證患者,主要包括體內或體表裝有起搏器或其他鐵磁性物質者、妊娠早期婦女、幽閉恐懼癥者、對釓螯化合物過敏者。另外,乳腺MRI檢查的靈敏度較高,會增加不必要的組織活檢或手術,可能會延誤確定性治療或導致過度治療[41]。同時,乳腺MRI檢查費用相對較高、耗時長、操作復雜等因素也限制了其廣泛應用和推廣。
4""小結與展望
乳腺癌已位居我國女性腫瘤發病之首位。乳腺癌篩查對于早期發現和治療至關重要。乳腺X線鉬靶和超聲是良好的初篩手段,MRI和CT能進一步明確病變性質及腫瘤分期,對于深部、多中心、多病灶、致密型乳腺病變,MRI比X線鉬靶、超聲、CT更能準確檢測出乳腺癌,其敏感度高達0.97[42]。目前,乳腺MRI快速檢查方案已在臨床得到應用,影響乳腺MRI應用和推廣的因素也在減少。借助AI和影像組學等技術,乳腺MRI技術在乳腺病變的診斷、治療方案的選擇、療效評估和預后判斷等方面展現出更加廣闊的應用前景。
利益沖突:所有作者均聲明不存在利益沖突。
[參考文獻]
[1] WINTERS"S,"MARTIN"C,"MURPHY"D,"et"al."Breast"cancer"epidemiology,"prevention,"and"screening[J]."Prog"Mol"Biol"Transl"Sci,"2017,"151:"1–32.
[2] SUNG"H,"FERLAY"J,"SIEGEL"R"L,"et"al."Global"cancer"statistics"2020:"GLOBOCAN"estimates"of"incidence"and"mortality"worldwide"for"36"cancers"in"185"countries[J]."CA"Cancer"J"Clin,"2021,"71(3):"209–249.
[3] WANG"Y,"YAN"Q,"FAN"C,"et"al."Overview"and"countermeasures"of"cancer"burden"in"China[J]."Sci"China"Life"Sci,"2023,"66(11):"2515–2526.
[4] 何思怡,"李賀,"曹毛毛,"等."全球及我國女性乳腺癌疾病負擔年齡分布及變化趨勢[J]."中國腫瘤,"2023,"32(1):"1–7.
[5] 劉威,"王黎君,"齊金蕾,"等."1990-2017年中國女性乳腺癌疾病負擔分析[J]."中華流行病學雜志,"2021,"42(7):"1225–1230.
[6] NOLAN"E,"LINDEMAN"G"J,"VISVADER"J"E."Deciphering"breast"cancer:"From"biology"to"the"clinic[J]."Cell,"2023,"186(8):"1708–1728.
[7] C?MERT"D,"VAN"GILS"C"H,"VELDHUIS"W"B,"et"al."Challenges"and"changes"of"the"breast"cancer"screening"paradigm[J]."J"Magn"Reson"Imaging,"2023,"57(3):"706–726.
[8] HENDRICK"R"E."Breast"MRI:"Fundamentals"and"technical"aspects[M]."New"York:"Springer,"2008.
[9] 高道利,"王文婉,"胡永偉,"等."乳腺癌二級預防——上海266064名婦女乳房自我檢查效果的評估[J]."中國腫瘤,"2008,"17(4):"264–269.
[10] 沈松杰,"孫強."中國女性乳腺癌篩查現狀及適宜模式探索[J]."協和醫學雜志,"2018,"9(4):"298–302.
[11] PESAPANE"F,"SUTER"M"B,"ROTILI"A,"et"al."Will"traditional"biopsy"be"substituted"by"radiomics"and"liquid"biopsy"for"breast"cancer"diagnosis"and"characterisation?[J]."Med"Oncol,"2020,"37(4):"29.
[12] 周嘉音,"尤超,"顧雅佳."影像組學在乳腺癌的應用研究進展[J]."國際醫學放射學雜志,"2022,"45(2):"174–179.
[13] SECHOPOULOS"I,"TEUWEN"J,"MANN"R."Artificial"intelligence"for"breast"cancer"detection"in"mammography"and"digital"breast"tomosynthesis:"State"of"the"art[J]."Semin"Cancer"Biol,"2021,"72(6):"214–225.
[14] GUO"R,"LU"G,"QIN"B,"et"al."Ultrasound"imaging"technologies"for"breast"cancer"detection"and"management:"A"review[J]."Ultrasound"Med"Biol,"2018,"44(1):"37–70.
[15] 曹學勝,"劉彪,"鄭進天,"等."乳腺癌影像學檢查方法的應用及最新進展[J]."中國醫藥指南,"2016,"14(13):"36–37.
[16] BOONE"J"M,"KWAN"A"L,"YANG"K,"et"al."Computed"tomography"for"imaging"the"breast[J]."J"Mammary"Gland"Biol"Neoplasia,"2006,"11(2):"103–111.
[17] 辛宏偉,"徐啟懷,"徐曉劍,"等."早期乳腺癌CT檢查的診斷價值分析[J]."中國現代醫生,"2013,"51(3):"92–93.
[18] MELSAETHER"A"N,"RAAD"R"A,"PUJARAnbsp;A"C,"et"al."Comparison"of"whole-body"18F"FDG"PET/MR"imaging"and"whole-body"18F"FDG"PET/CT"in"terms"of"lesion"detection"and"radiation"dose"in"patients"with"breast"cancer[J]."Radiology,"2016,"281(1):"193–202.
[19] OBERMANN"M,"NOHAVA"L,"FRASS-KRIEGL"R,"et"al."Panoramic"magnetic"resonance"imaging"of"the"breast"with"a"wearable"coil"vest[J]."Invest"Radiol,"2023,"58(11):"799–810.
[20] CLAUSER"P,"KRUG"B,"BICKEL"H,"et"al."Diffusion-weighted"imaging"allows"for"downgrading"MR"BI-RADS"4"lesions"in"contrast-enhanced"MRI"of"the"breast"to"avoid"unnecessary"biopsy[J]."Clin"Cancer"Res,"2021,"27(7):"1941–1948.
[21] RAHBAR"H,"ZHANG"Z,"CHENEVERT"T"L,"et"al."Utility"of"diffusion-weighted"imaging"to"decrease"unnecessary"biopsies"prompted"by"breast"MRI:"A"trial"of"the"ECOG-ACRIN"cancer"research"group"(A6702)[J]."Clin"Cancer"Res,"2019,"25(6):"1756–1765.
[22] TSOUGOS"I,"BAKOSIS"M,"TSIVAKA"D,"et"al."Diagnostic"performance"of"quantitative"diffusion"tensor"imaging"for"the"differentiation"of"breast"lesions"at"3 T"MRI[J]."Clin"Imaging,"2019,"53:"25–31.
[23] IIMA"M,"YANO"K,"KATAOKA"M,"et"al."Quantitative"non-Gaussian"diffusion"and"intravoxel"incoherent"motion"magnetic"resonance"imaging:"Differentiation"of"malignant"and"benign"breast"lesions[J]."Invest"Radiol,"2015,"50(4):"205–211.
[24] HUANG"Y,"LIN"Y,"HU"W,"et"al."Diffusion"Kurtosis"at"3.0T"as"an"in"vivo"imaging"marker"for"breast"cancer"characterization:"Correlation"with"prognostic"factors[J]."J"Magn"Reson"Imaging,"2019,"49(3):"845–856.
[25] BARTELLA"L,"THAKUR"S"B,"MORRIS"E"A,"et"al."Enhancing"nonmass"lesions"in"the"breast:"Evaluation"with"proton"1H"MR"spectroscopy[J]."Radiology,"2007,"245(1):"80–87.
[26] GERAGHTY"P"R,"VAN"DEN"BOSCH"M"A,"SPIELMAN"D"M,"et"al."MRI"and"1H"MRS"of"the"breast:"Presence"of"a"choline"peak"as"malignancy"marker"is"related"to"k21"value"of"the"tumor"in"patients"with"invasive"ductal"carcinoma[J]."Breast"J,"2008,"14(6):"574–580.
[27] LEE"S"G,"OREL"S"G,"WOO"I"J,"et"al."MR"imaging"screening"of"the"contralateral"breast"in"patients"with"newly"diagnosed"breast"cancer:"Preliminary"results[J]."Radiology,"2003,"226(3):"773–778.
[28] OREL"S"G,"WEINSTEIN"S"P,"SCHNALL"M"D,"et"al."Breast"MR"imaging"in"patients"with"axillary"node"metastases"and"unknown"primary"malignancy[J]."Radiology,"1999,"212(2):"543–549.
[29] MORRIS"E"A,"SCHWARTZ"L"H,"DERSHAW"D"D,"et"al."MR"imaging"of"the"breast"in"patients"with"occult"primary"breast"carcinoma[J]."Radiology,"1997,"205(2):"437–440.
[30] MORRIS"E"A,"SCHWARTZ"L"H,"DROTMAN"M"B,"et"al."Evaluation"of"pectoralis"major"muscle"in"patients"with"posterior"breast"tumors"on"breast"MR"images:"Early"experience[J]."Radiology,"2000,"214(1):"67–72.
[31] SASLOW"D,"BOETES"C,"BURKE"W,"et"al."American"Cancer"Society"guidelines"for"breast"screening"with"MRI"as"an"adjunct"to"mammography[J]."CA"Cancer"J"Clin,"2007,"57(2):"75–89.
[32] BEVERS"T"B,"HELVIE"M,"BONACCIO"E,"et"al."Breast"cancer"screening"and"diagnosis,"version"3.2018,"NCCN"clinical"practice"guidelines"in"oncology[J]."J"Natl"Compr"Canc"Netw,"2018,"16(11):"1362–1389.
[33] 焦鎏鎏,"張禹."MRI動態增強曲線半定量參數聯合DWI在乳腺良惡性腫瘤診斷中的作用[J]."中國臨床醫學影像雜志,"2022,"33(9):"633–637.
[34] BALLEYGUIER"C,"LAKHDAR"A"B,"DUNANT"A,"et"al."Value"of"whole"breast"magnetic"resonance"elastography"added"to"MRI"for"lesion"characterization[J]."NMR"Biomed,"2018,"31(1):"e3795.
[35] VERBURG"E,"VAN"GILS"C"H,"VAN"DER"VELDEN"B"H"M,"et"al."Validation"of"combined"deep"learning"triaging"and"computer-aided"diagnosis"in"2901"breast"MRI"examinations"from"the"second"screening"round"of"the"dense"tissue"and"early"breast"neoplasm"screening"trial[J]."Invest"Radiol,"2023,"58(4):"293–298.
[36] SHOSHAN"Y,"BAKALO"R,"GILBOA-SOLOMON"F,"et"al."Artificial"intelligence"for"reducing"workload"in"breast"cancer"screening"with"digital"breast"tomosynthesis[J]."Radiology,"2022,"303(1):"69–77.
[37] DAIMIEL"NARANJO"I,"GIBBS"P,"REINER"J"S,"et"al."Radiomics"and"machine"learning"with"multiparametric"breast"MRI"for"improved"diagnostic"accuracy"in"breast"cancer"diagnosis[J]."Diagnostics"(Basel),"2021,"11(6):"919.
[38] FAN"M,"ZHANG"P,"WANG"Y,"et"al."Radiomic"analysis"of"imaging"heterogeneity"in"tumours"and"the"surrounding"parenchyma"based"on"unsupervised"decomposition"of"DCE-MRI"for"predicting"molecular"subtypes"of"breast"cancer[J]."Eur"Radiol,"2019,"29(8):"4456–4467.
[39] PETERSON"J"R,"COLE"J"A,"PFEIFFER"J"R,"et"al."Novel"computational"biology"modeling"system"can"accurately"forecast"response"to"neoadjuvant"therapy"in"early"breast"cancer[J]."Breast"Cancer"Res,"2023,"25(1):"54.
[40] CHITALIA"R"D,"ROWLAND"J,"MCDONALD"E"S,"et"al."Imaging"phenotypes"of"breast"cancer"heterogeneity"in"preoperative"breast"dynamic"contrast"enhanced"magnetic"resonance"imaging"(DCE-MRI)"scans"predict"10-year"recurrence[J]."Clin"Cancer"Res,"2020,"26(4):"862–869.
[41] O'FLYNN"E"A,"LEDGER"A"E,"DESOUZA"N"M."Alternative"screening"for"dense"breasts:"MRI[J]."AJR"Am"J"Roentgenol,"2015,"204(2):"W141–W149.
[42] ZHANG"X"H,"XIAO"C."Diagnostic"value"of"nineteen"different"imaging"methods"for"patients"with"breast"cancer:"A"network"Meta-analysis[J]."Cell"Physiol"Biochem,"2018,"46(5):"2041–2055.
(收稿日期:2023–11–09)
(修回日期:2024–06–01)

(上接第134頁)
[25] Tahiliani"M,"Koh"K"P,"Shen"Y,"et"al."Conversion"of"5-methylcytosine"to"5-hydroxymethylcytosine"in"mammalian"DNA"by"MLL"partner"Tet1[J]."Science,"2009,"324(5929):"930–935.
[26] CHEN"Y"T,"SHEN"J"Y,"CHEN"D"P,"et"al."Identification"of"cross-talk"between"m6A"and"5mC"regulators"associated"with"onco-immunogenic"features"and"prognosis"across"33"cancer"types[J]."J"Hematol"Oncol,"2020,"13(1):"22.
[27] Erstad"D"J,"Fuchs"B"C,"Tanabe"K"K."Molecular"signatures"in"hepatocellular"carcinoma:"A"step"toward"rationally"designed"cancer"therapy[J]."Cancer,"2018,"124(15):"3084–3104.
[28] Petri"B"J,"Klinge"C"M."M6A"readers,"writers,"erasers,"and"the"m6A"epitranscriptome"in"breast"cancer[J]."J"Mol"Endocrinol,"2023,"70(2):"e220110.
[29] CHEN"M,"WONG"C"M."The"emerging"roles"of"N6-methyladenosine"(m6A)"deregulation"in"liver"carcinogenesis[J]."Mol"Cancer,"2020,"19(1):"44.
[30] Shen"L,"Song"C"X,"He"C,"et"al."Mechanism"and"function"of"oxidative"reversal"of"DNA"and"RNA"methylation[J]."Annu"Rev"Biochem,"2014,"83:"585–614.
[31] Song"Y,"He"S,"Ma"X,"et"al."RBMX"contributes"to"hepatocellular"carcinoma"progression"and"sorafenib"resistance"by"specifically"binding"and"stabilizing"BLACAT1[J]."Am"J"Cancer"Res,"2020,"10(11):"3644–3665.
[32] Liu"Z,"Wang"Q,"Wang"X,"et"al."Circular"RNA"cIARS"regulates"ferroptosis"in"HCC"cells"through"interacting"with"RNA"binding"protein"ALKBH5[J]."Cell"Death"Discov,"2020,"6:"72.
[33] Lee"C"C,"Chang"W"H,"Chang"Y"S,"et"al."Alternative"splicing"in"human"cancer"cells"is"modulated"by"the"amiloride"derivative"3,"5-diamino-6-chloro-"N-(N-(2,6-dichlorobenzoyl)carbamimidoyl)pyrazine-2-carboxide[J]."Mol"Oncol,"2019,"13(8):"1744–1762.
[34] Mezzalira"S,"De"Mattia"E,"Guardascione"M,"et"al."Circulating-free"DNA"analysis"in"hepatocellular"carcinoma:"A"promising"strategy"to"improve"patients’"management"and"therapy"outcomes[J]."Int"J"Mol"Sci,"2019,"20(21):"5498.
[35] Singh"P,"Kairuz"D,"Arbuthnot"P,"et"al."Silencing"hepatitis"B"virus"covalently"closed"circular"DNA:"The"potential"of"an"epigenetic"therapy"approach[J]."World"J"Gastroenterol,"2021,"27(23):"3182–3207.
(收稿日期:2023–11–13)
(修回日期:2024–05–31)