Question about GO terms in clusterProfiler

[shuaizh117]

I was confused by GO terms popped out in my GO analysis using enrichGO. For example, "GO:0141091: transforming growth factor beta receptor superfamily signaling pathway" was reported as one of the top overrepresented GO in my sig DEGs. And below is the gene list "HSPA1A/TNFAIP6/FST/ZBTB7A/HES1/ITGB8/TGFB2/MYOCD/LATS2/THBS1/GREM1" enriched to this term. However, when I refer to PANTHER regarding GO:0141091, I only found TGFB2 in this GO term. If the other genes do not belong to this GO:0141091, then how did this GO get enriched?? I think I'm still not very clear about the behind scene story of how enrichGO perform the calculation.

sessionInfo() R version 4.4.0 (2024-04-24 ucrt) Platform: x86_64-w64-mingw32/x64 Running under: Windows 11 x64 (build 22631)

Matrix products: default

locale: [1] LC_COLLATE=English_United States.utf8 LC_CTYPE=English_United States.utf8
[3] LC_MONETARY=English_United States.utf8 LC_NUMERIC=C
[5] LC_TIME=English_United States.utf8

time zone: America/Chicago tzcode source: internal

attached base packages: [1] stats4 stats graphics grDevices utils datasets methods base

other attached packages: [1] viridis_0.6.5 viridisLite_0.4.2
[3] topGO_2.56.0 SparseM_1.81
[5] GO.db_3.19.1 graph_1.82.0
[7] pathview_1.44.0 enrichplot_1.24.0
[9] clusterProfiler_4.12.3 ggpubr_0.6.0
[11] corrplot_0.92 annotables_0.2.0
[13] RColorBrewer_1.1-3 ggrepel_0.9.5
[15] pheatmap_1.0.12 lubridate_1.9.3
[17] forcats_1.0.0 stringr_1.5.1
[19] dplyr_1.1.4 purrr_1.0.2
[21] readr_2.1.5 tidyr_1.3.1
[23] tibble_3.2.1 tidyverse_2.0.0
[25] ggplot2_3.5.1 org.Hs.eg.db_3.19.1
[27] AnnotationDbi_1.66.0 DESeq2_1.44.0
[29] SummarizedExperiment_1.34.0 Biobase_2.64.0
[31] MatrixGenerics_1.16.0 matrixStats_1.3.0
[33] GenomicRanges_1.56.0 GenomeInfoDb_1.40.0
[35] IRanges_2.38.0 S4Vectors_0.42.0
[37] BiocGenerics_0.50.0

loaded via a namespace (and not attached): [1] rstudioapi_0.16.0 jsonlite_1.8.8 magrittr_2.0.3
[4] farver_2.1.1 rmarkdown_2.26 fs_1.6.4
[7] zlibbioc_1.50.0 vctrs_0.6.5 memoise_2.0.1
[10] RCurl_1.98-1.14 ggtree_3.12.0 rstatix_0.7.2
[13] htmltools_0.5.8.1 S4Arrays_1.4.0 broom_1.0.5
[16] SparseArray_1.4.3 gridGraphics_0.5-1 plyr_1.8.9
[19] httr2_1.0.1 cachem_1.0.8 igraph_2.0.3
[22] lifecycle_1.0.4 pkgconfig_2.0.3 Matrix_1.7-0
[25] R6_2.5.1 fastmap_1.1.1 gson_0.1.0
[28] GenomeInfoDbData_1.2.12 digest_0.6.31 aplot_0.2.2
[31] colorspace_2.1-0 patchwork_1.2.0 RSQLite_2.3.6
[34] labeling_0.4.3 timechange_0.3.0 fansi_1.0.4
[37] httr_1.4.7 polyclip_1.10-6 abind_1.4-5
[40] compiler_4.4.0 bit64_4.0.5 withr_3.0.0
[43] backports_1.4.1 BiocParallel_1.38.0 carData_3.0-5
[46] DBI_1.2.2 ggforce_0.4.2 ggsignif_0.6.4
[49] MASS_7.3-60.2 rappdirs_0.3.3 DelayedArray_0.30.1
[52] HDO.db_0.99.1 tools_4.4.0 ape_5.8
[55] scatterpie_0.2.2 glue_1.6.2 nlme_3.1-164
[58] GOSemSim_2.30.0 grid_4.4.0 shadowtext_0.1.3
[61] reshape2_1.4.4 fgsea_1.30.0 generics_0.1.3
[64] gtable_0.3.5 tzdb_0.4.0 hms_1.1.3
[67] data.table_1.15.4 car_3.1-2 tidygraph_1.3.1
[70] utf8_1.2.3 XVector_0.44.0 pillar_1.9.0
[73] yulab.utils_0.1.6 limma_3.60.0 splines_4.4.0
[76] tweenr_2.0.3 treeio_1.28.0 lattice_0.22-6
[79] bit_4.0.5 tidyselect_1.2.1 locfit_1.5-9.9
[82] Biostrings_2.72.0 knitr_1.46 gridExtra_2.3
[85] xfun_0.43 graphlayouts_1.1.1 statmod_1.5.0
[88] KEGGgraph_1.64.0 stringi_1.8.4 UCSC.utils_1.0.0
[91] lazyeval_0.2.2 ggfun_0.1.4 yaml_2.3.7
[94] evaluate_0.23 codetools_0.2-20 ggraph_2.2.1
[97] qvalue_2.36.0 Rgraphviz_2.48.0 ggplotify_0.1.2
[100] cli_3.6.2 munsell_0.5.1 Rcpp_1.0.12
[103] png_0.1-8 XML_3.99-0.16.1 parallel_4.4.0
[106] blob_1.2.4 DOSE_3.30.0 bitops_1.0-7
[109] tidytree_0.4.6 scales_1.3.0 crayon_1.5.2
[112] rlang_1.1.3 cowplot_1.1.3 fastmatch_1.1-4
[115] KEGGREST_1.44.0