Biomarkers　are　critical　for　rapid　diagnosis　of　tuberculosis　(TB)　and　could　benefit　patients　with　AIDS　where　diagnosis　of　TB　co-infection　is　challenging.　Meta-analysis　is　an　approach　to　combine　the　results　of　the　studies　with　standard　statistical　method　by　weighting　each　study　with　different　sample　size.　This　study　aimed　to　use　meta-analysis　to　integrate　transcriptome　datasets　from　different　studies　and　screen　for　TB　biomarkers　in　patients　who　were　HIV-positive.　Five　datasets　were　subjected　to　meta-analysis　on　whole-blood　transcriptomes　from　640　patients　infected　with　HIV.　A　total　of　293　differentially　expressed　genes　(DEGs)　were　identified　as　significant　(P＜0.0001)　using　the　random　effective　model　to　integrate　the　statistical　results　from　each　study.　DEGs　were　enriched　in　biological　processes　related　to　TB,　such　as　＂Type　I　interferon　signaling＂　and　＂stimulatory　C-type　lectin　receptor　signaling＂.　Eighteen　DEGs　had　at　least　a　two-fold　change　in　expression　between　patients　infected　with　HIV　who　were　TB-positive　and　those　who　were　TB-negative.　GBP4,　SERPING1,　ATF3　and　CDKBN3　were　selected　as　a　biomarker　panel　to　perform　multivariable　logistic　regression　analysis　on　TB　status　and　relative　gene　expression　levels.　The　biomarker　panel　showed　excellent　accuracy　(AUC＞0.90　for　HIV+TB)　in　clinical　trial　and　suggests　that　meta-analysis　is　an　efficient　method　to　integrate　transcriptome　datasets　from　different　studies.