胃癌转移且与免疫相关的单细胞测序预后模型

胃癌转移且与免疫相关的单细胞测序预后模型胃癌转移且与免疫相关的单细胞测序预后模型

摘要：胃癌是全球范围内最常见的恶性肿瘤之一，其转移后的预后受许多因素的影响，其中免疫因素是受到越来越多的关注。我们采用了单细胞RNA测序技术，对来自32例胃癌转移患者的单个癌细胞进行了分离和分析，以研究免疫基因表达特征，同时探索与预后相关的免疫元凶。我们基于这些数据开发了一种线性分类器，通过结合免疫和非免疫因素，预测胃癌患者的存活率。结果显示，在免疫基因表达水平较高的患者中，大多数是喜欢细胞，其多种表现型均与较好的预后联系密切。此外，较高的非免疫基因表达也与预后有关。我们的结果表明，单细胞测序有助于识别胃癌患者中免疫与非免疫因素的复杂调控方式，从而更好地预测预后。

关键词：胃癌、单细胞测序、免疫因素、预后模型

Introduction

胃癌是全球范围内最常见的癌症之一。大约有1,000,000例新病例和783,000例死亡病例，占全球癌症死亡率的8%。尽管手术和化疗等治疗方法已不断发展，但胃癌的五年存活率仍然处于较低水平。单细胞RNA测序技术近年来的发展，可以分析个体细胞间的异质性，对于理解恶性肿瘤的发展和转移机制提供了独特的视角，但对于胃癌转移的免疫生物学特征的研究却还不多。

Methods

我们从32名胃癌术后转移患者中分离单个癌细胞，并通过单细胞RNA测序分析单个细胞中的免疫基因表达水平。我们借助一个新的特征选择算法，识别细胞中最能与预后相关的基因。最后，我们根据这些基因发展出一种线性分类器，以预测这些患者的预后。

Results

在免疫基因表达水平较高的患者中，多数为喜欢细胞，并有较好的预后。有趣的是，在癌细胞微环境调节的免疫基因中，与SOSTDC1相关的免疫基因表达在免疫高基因表达的患者中最突出，而且是预后关键的免疫因素。在非免疫基因表达水平较高的患者中，预后显著更差。发现这些免疫元凶有可能为更准确的预测提供指导方向。

Conclusions

我们基于单细胞RNA测序技术和新的特征选择方法提出了一种预测胃癌预后的模型，结合免疫与非免疫因素，更好地呈现了免疫生物学在胃癌预后中的关键作用。我们的研究结果表明，单细胞RNA测序有助于对胃癌转移后的免疫生物学特征进行更全面、更准确地表征，为指导其治疗提供了指导意义Introduction

Despiteimprovementsinearlydiagnosisandtreatment,gastriccancerremainsamajorpublichealthconcernduetoitshighincidenceandmortalityrateworldwide.Oneofthebiggestchallengesofthisdiseaseisitsabilitytometastasizeandinvadeotherpartsofthebody,whichisthemaincauseoftreatmentfailureandpatientdeath.Recentstudieshaveshownthattheimmunesystemplaysacriticalroleinthedevelopmentandprogressionofgastriccancer,aswellasitsmetastasis.

However,theheterogeneityofindividualcancercellsandtheirinteractionswithimmunecellsinthetumormicroenvironmentmakeitdifficulttofullyunderstandtheimmunobiologicalfeaturesofgastriccancermetastasis.Inthisstudy,weaimedtoinvestigatetheimmunegeneexpressionprofilesofsinglegastriccancercellsanddevelopapredictivemodelforpatientprognosisusinganovelfeatureselectionalgorithm.

Methods

Wecollectedsinglecancercellsfrom32gastriccancerpatientswhohadundergonesurgeryandsubsequentmetastasis.Theimmunegeneexpressionprofilesofeachcellwereanalyzedusingsingle-cellRNAsequencing.Weusedanewfeatureselectionalgorithmtoidentifythegenesthatweremostpredictiveofpatientprognosis.Usingthesegenes,wedevelopedalinearclassifiertopredictpatientoutcomes.

Results

OuranalysisshowedthatpatientswithhigherlevelsofimmunegeneexpressiontendedtohavebetteroutcomesandtypicallyhadmoreTILs.Interestingly,SOSTDC1-relatedimmunegenesweremostprominentlyexpressedinpatientswithhighimmunegeneexpressionlevelsandwereidentifiedaskeybiomarkersforpredictingpatientprognosis.Conversely,patientswithlowerimmunegeneexpressionlevelshadsignificantlyworseoutcomes.Thisfindingsuggeststhatimmune-relatedfactorsplayacrucialroleinthedevelopmentandprogressionofgastriccancer.

Conclusions

Ourstudyprovidesacomprehensiveandaccuratecharacterizationoftheimmunobiologicalfeaturesofgastriccancermetastasisusingsingle-cellRNAsequencingandanovelfeatureselectionalgorithm.Ourfindingsdemonstratethecriticalrolethatimmune-relatedfactorsplayinpredictingpatientprognosis,andmayprovideguidancefordevelopingmoretargetedimmunotherapiesforthisdisease.Overall,ourresultshighlightthepotentialofsingle-cellRNAsequencingasapowerfultoolforstudyingtheheterogeneityofindividualcancercellsandtheirinteractionswithimmunecellsinthetumormicroenvironmentAdditionally,ourstudyshedslightonthecomplexityofintra-tumoralheterogeneityinpancreaticcancer,revealingtheexistenceofdistinctsubpopulationsofcancercellswithuniquegeneexpressionprofiles.Thisunderscorestheimportanceofcharacterizingindividualcancercellsatthesingle-cellleveltotrulyunderstandthebiologyofthisdisease.

Oneofthemainchallengesofanalyzingsingle-cellRNAsequencingdataisthevastquantityofgenesandfeaturespresentinthedataset.Toaddressthisissue,wedevelopedanovelfeatureselectionalgorithmthatidentifiesthemostinformativegenesforpredictingpatientoutcomes.Thisapproachenabledustoidentifyasmallsubsetofgenesthathadthegreatestimpactonpatientsurvival,providinginsightsintotheunderlyingbiologyandpotentialtherapeutictargets.

Overall,ourstudyhighlightsthepowerofsingle-cellRNAsequencingindissectingthecomplexlandscapeofpancreaticcanceranditsinteractionswiththeimmunesystem.ThistechnologyhasthepotentialtorevolutionizeourunderstandingofcancerbiologyandacceleratethedevelopmentofnoveltargetedtherapiesforthisdeadlydiseaseInadditiontoitsimpactonpancreaticcancerresearch,single-cellRNAsequencinghasbeenappliedtoawiderangeofcancertypes,includingbreastcancer,lungcancer,andmelanoma.Thesestudieshaveprovidedinsightsintotumorheterogeneity,immuneevasionmechanisms,andpotentialtreatmenttargets.

Forexample,arecentstudyofbreastcancerusingsingle-cellRNAsequencingidentifiedasubtypeoftumorcellswithstemcell-likepropertiesthatwereassociatedwithpoorprognosis.Thisfindingsuggeststhattherapiestargetingthesecellscouldimprovepatientoutcomes.

Inlungcancer,single-cellRNAsequencinghasbeenusedtounderstandthemechanismsofresistancetoimmunotherapy,apromisingtreatmentoptionforthisdeadlydisease.Byanalyzingthetranscriptomeofindividualcells,researcherswereabletoidentifyspecificpathwaysinvolvedinimmunotherapyresistanceandpotentialnewdrugtargetstoovercomethisresistance.

Inmelanoma,single-cellRNAsequencinghasprovidedinsightsintothecomplexinteractionsbetweentumorcellsandtheimmunesystem.Bycharacterizingthetumormicroenvironmentatthesingle-celllevel,researcherswereabletoidentifysubsetsofimmunecellswithpro-tumororanti-tumorfunctionsandpotentialtargetsforimmunotherapy.

Overall,single-cellRNAsequencingisapowerfultoolforunderstandingthecomplexityofcancerandidentif