Background:　Targeted　therapy　has　revolutionized　cancer　treatment,　greatly　improving　patient　outcomes　and　quality　of　life.　Lung　cancer,　specifically　non-small　cell　lung　cancer,　is　frequently　driven　by　the　G12C　mutation　at　the　KRAS　locus.　The　development　of　KRAS　inhibitors　has　been　a　breakthrough　in　the　field　of　cancer　research,　given　the　crucial　role　of　KRAS　mutations　in　driving　tumor　growth　and　progression.　However,　over　half　of　patients　with　cancer　bypass　inhibition　show　limited　response　to　treatment.　The　mechanisms　underlying　tumor　cell　resistance　to　this　treatment　remain　poorly　understood.Methods:　To　address　above　gap　in　knowledge,　we　conducted　a　study　aimed　to　elucidate　the　differences　between　tumor　cells　that　respond　positively　to　KRAS　(G12C)　inhibitor　therapy　and　those　that　do　not.　Specifically,　we　analyzed　single-cell　gene　expression　profiles　from　KRAS　G12C-mutant　tumor　cell　models　(H358,　H2122,　and　SW1573)　treated　with　KRAS　G12C　(ARS-1620)　inhibitor,　which　contained　4297　cells　that　continued　to　prolif-erate　under　treatment　and　3315　cells　that　became　quiescent.　Each　cell　was　represented　by　the　expression　levels　on　8687　genes.　We　then　designed　an　innovative　machine　learning　based　framework,　incorporating　seven　feature　ranking　algorithms　and　four　classification　algorithms　to　identify　essential　genes　and　establish　quantitative　rules.Results:　Our　analysis　identified　some　top-ranked　genes,　including　H2AFZ,　CKS1B,　TUBA1B,　RRM2,　and　BIRC5,　that　are　known　to　be　associated　with　the　progression　of　multiple　cancers.Conclusion:　Above　genes　were　relevant　to　tumor　cell　resistance　to　targeted　therapy.　This　study　provides　important　insights　into　the　molecular　mechanisms　underlying　tumor　cell　resistance　to　KRAS　inhibitor　treatment.