Skip to content
LB2

Kidney renal papillary cell carcinoma KIRP

Of 60,498 genes, 553 are higher in the tumor than in normal kidney and in whole blood, and are not made by blood immune cells. 236 are protein-coding, and 232 of those have supporting plasma Cell-free RNA (cfRNA)RNA fragments that circulate in blood plasma outside cells. Most come from blood cells; a small share comes from other tissues, including tumors. evidence; the rest are mostly non-coding genes the plasma sources cannot check.

Samples compared

Tumor
288
TCGA primary tumors
Normal tissue
60
32 TCGA tumor-adjacent, 28 GTEx kidney
Whole blood
337
GTEx samples from 328 donors
Survival
283
patients, 55 events (Survival endpointsCurated TCGA outcomes (Liu et al. 2018): OS is overall survival, PFI the progression-free interval and DSS disease-specific survival. LB2 uses whichever has the most events in each cancer type.)

Results in brief

553
candidate genes: 232 with plasma cfRNA evidence, 5 protein-coding without, 316 non-coding
190
genes in the tissue panel, Held-out AUCHow well an elastic-net model separates tumor from normal tissue, measured with nested cross-validation on samples the model did not train on. It describes tissue, not the accuracy of a blood test. 0.994
47
genes associated with progression-free interval (FDR < 0.05, 150 tested)

Candidates

Filter by plasma evidence or discrimination, find a gene, and select any point or row for its full record. The table holds all 553 candidates.

Loading candidates…

How the candidates were selected

60,498 genes tested; 1,225 higher in tumor than in normal kidney; 788 also higher than in whole blood; 553 remain after removing genes expressed in blood immune cells; 232 of these have plasma cfRNA evidence, 5 protein-coding candidates have none, and 316 are non-coding genes the plasma sources cannot list.

Figure 2. Genes remaining after each step in kidney renal papillary cell carcinoma.

Each comparison uses a two-sided Wilcoxon rank-sum test. A gene passes when its q-value is below 0.05, its log2 fold change is at least 1, and the lower bound of the 95% confidence interval for its AUC is at least 0.70.

Genes made by any of 18 sorted blood immune cell types above 1 nTPM are then removed, because blood cells supply most plasma RNA. Genes missing from that reference, mostly non-coding, are kept.

Plasma evidence only reorders the list. A candidate not yet seen in plasma stays in, because every dataset misses genes. Read the full methods.

Tissue classifier panel

Genetoward normaltoward tumorCoef.
  1. PKD1L2+0.29
  2. PAQR9-AS1+0.28
  3. LINC00475+0.24
  4. PSORS1C1+0.22
  5. E2F1+0.22
  6. PRUNE2−0.21
  7. APOC1P1+0.20
  8. RP11-554I8.2+0.20
  9. HSPB8+0.19
  10. RP11-259N19.1+0.18
  11. KCNJ4−0.18
  12. FAM57B+0.18
  13. SPATA12+0.18
  14. CDC45+0.17
  15. CHRNA1+0.17
  16. RP11-20G6.1+0.17
  17. DNASE2B+0.16
  18. PTGFRN+0.16
  19. PMFBP1+0.16
  20. RP3-510D11.2+0.15
Show the other 170 genes
  1. BCHE−0.15
  2. AC079630.2−0.15
  3. BTBD16+0.15
  4. GAS6-AS1+0.15
  5. LRRN4−0.15
  6. RPL37P6+0.14
  7. AHNAK2+0.14
  8. MNX1+0.14
  9. TNFSF9+0.13
  10. TNNI3+0.13
  11. DPCR1−0.13
  12. ZBBX−0.13
  13. ABCA12+0.13
  14. CHRNA6+0.13
  15. SYT14+0.12
  16. LINC00857−0.12
  17. GPR37L1−0.12
  18. RP11-386G11.10+0.12
  19. FAM64A+0.12
  20. EDA2R+0.12
  21. HOXA3−0.12
  22. HS3ST2+0.11
  23. TP73+0.11
  24. RP11-284F21.9+0.11
  25. KCNS3−0.11
  26. ELFN2−0.10
  27. GDAP1L1+0.10
  28. NKAIN1+0.10
  29. DNAJB13−0.10
  30. RASGEF1C−0.10
  31. SPATA18+0.10
  32. SYT12+0.10
  33. PRDM12+0.10
  34. ASPHD1+0.10
  35. RP11-93I21.3+0.10
  36. HTRA4+0.09
  37. CDT1+0.09
  38. CDH6−0.09
  39. BCO2+0.08
  40. GPRIN1+0.08
  41. KIAA0408+0.08
  42. DLX4−0.08
  43. ZMIZ1-AS1+0.08
  44. HOXC11−0.08
  45. SCG5+0.08
  46. DCSTAMP−0.08
  47. SULT1C4+0.08
  48. CCL18+0.08
  49. IL20RB+0.08
  50. LINC01315+0.08
  51. RIMKLA+0.07
  52. BEST4−0.07
  53. LRRC20+0.07
  54. KRT12+0.07
  55. RP11-148B18.4−0.07
  56. SEZ6L2+0.07
  57. TMEM163+0.07
  58. DONSON−0.07
  59. CCDC78+0.07
  60. PLEKHN1+0.07
  61. RP11-14N7.2−0.06
  62. RHBDF1+0.06
  63. SDK1+0.06
  64. SRCIN1+0.06
  65. RP11-149I23.3+0.06
  66. RP5-1112D6.7+0.06
  67. RP11-20I23.1+0.06
  68. TMEM132A−0.06
  69. TFPI2−0.06
  70. LINC01322−0.06
  71. HYDIN−0.06
  72. MET+0.05
  73. WFDC21P+0.05
  74. HRH1−0.05
  75. RP11-1C8.4+0.05
  76. CNTN6+0.05
  77. TRIM9+0.05
  78. HSPB2−0.05
  79. RP11-776H12.1+0.05
  80. ANXA13−0.05
  81. RECQL4−0.05
  82. CRYAB+0.05
  83. PTCHD4+0.05
  84. UCN+0.05
  85. CFB−0.05
  86. CENPA+0.05
  87. RP11-499O7.7+0.04
  88. BMP1−0.04
  89. CA9−0.04
  90. BAMBI−0.04
  91. DNAH2−0.04
  92. AKR1C2+0.04
  93. VSTM2L−0.04
  94. GRM5−0.04
  95. RP11-284F21.10+0.04
  96. C9orf172+0.04
  97. BCO1−0.04
  98. NPIPB6+0.04
  99. DOC2A+0.04
  100. LINC00511+0.04
  101. SPC25+0.04
  102. RP1-140K8.5+0.04
  103. PANO1−0.03
  104. ANKRD13B−0.03
  105. PSORS1C2+0.03
  106. TAS2R19+0.03
  107. RP11-1C8.7+0.03
  108. ANLN−0.03
  109. OXCT2−0.03
  110. PTPRH−0.03
  111. FOXH1−0.03
  112. SPINK13+0.03
  113. SLC17A2−0.03
  114. C19orf67+0.03
  115. FKBP6+0.03
  116. AC108142.1−0.03
  117. MICALL2−0.03
  118. CRNDE−0.03
  119. RP4-584D14.7−0.03
  120. RP11-354K1.1+0.02
  121. SH2D5+0.02
  122. RP11-185E8.2+0.02
  123. PRAME−0.02
  124. FER1L4−0.02
  125. EFCAB10−0.02
  126. SULT4A1−0.02
  127. HGFAC−0.02
  128. CENPF+0.02
  129. KB-1410C5.5−0.02
  130. CCDC74A−0.02
  131. TMPRSS6−0.02
  132. MSX2+0.02
  133. RP11-359E10.1−0.02
  134. RP5-875H18.9−0.02
  135. RP11-6F2.5+0.02
  136. LINC00887−0.02
  137. TRPM8+0.02
  138. OPRD1+0.02
  139. TMEM74B+0.02
  140. SOGA3−0.02
  141. RP11-309M7.1+0.02
  142. RAB42+0.02
  143. NPIPB9+0.02
  144. CLDN3−0.02
  145. MAST1−0.01
  146. RGS17+0.01
  147. PKD2L1−0.01
  148. SLFNL1+0.01
  149. MMP14+0.01
  150. RP11-49I11.1+0.01
  151. DDX11-AS1+0.01
  152. TMEM253+0.01
  153. RP11-1148L6.8+0.01
  154. PCSK4−0.01
  155. POLE−0.01
  156. RNFT2+0.01
  157. LACTB2-AS1+0.01
  158. PAQR9+0.01
  159. SCEL−0.01
  160. RIMS2−0.01
  161. CCND2-AS1+0.01
  162. SRSF12−0.01
  163. EEF1A2−0.01
  164. RP11-320M2.1+0.01
  165. PERM1−0.01
  166. RP11-274H2.5+0.00
  167. RP3-434P1.6+0.00
  168. RP11-626G11.3+0.00
  169. SCN10A+0.00
  170. SH3RF2−0.00
Figure 3. Standardized elastic-net coefficients of the 190 genes selected to separate kidney renal papillary cell carcinoma from normal kidney, largest first.

An elastic-net logistic regression was trained on the 300 top-ranked candidates to tell tumor from normal tissue. Its Held-out AUCHow well an elastic-net model separates tumor from normal tissue, measured with nested cross-validation on samples the model did not train on. It describes tissue, not the accuracy of a blood test. is 0.994, from nested five-fold cross-validation.

Before training, the TCGA-versus-GTEx offset was removed from the expression matrix with the tumor/normal contrast protected, as in the tissue comparison. That correction is fitted once on all samples, so the held-out AUC is not fully independent of it.

This number describes tumor and normal tissue, where separation is expected to be near perfect. It is not the accuracy of a blood test: that has to be measured in plasma from patients and controls.

Association with survival

Gene0.250.5124HR per SD (95% CI)q-value
  1. IQGAP32.28 (1.76–2.95)2.1 × 10−8
  2. FAM64A2.16 (1.64–2.85)1.8 × 10−6
  3. MNX11.97 (1.37–2.83)0.0027
  4. TNFSF91.92 (1.36–2.70)0.0022
  5. BTBD161.72 (1.30–2.27)0.0013
  6. MSX21.52 (1.20–1.93)0.0053
  7. ELFN20.73 (0.61–0.87)0.0048
  8. NBL10.68 (0.54–0.84)0.0045
  9. TMEM178B0.67 (0.53–0.85)0.0058
  10. PPP1R3G0.65 (0.50–0.83)0.0058
  11. ANKRD290.65 (0.52–0.80)7.4 × 10−4
  12. SULT1C40.63 (0.52–0.75)1.1 × 10−5
  13. CNTN60.62 (0.48–0.79)0.0013
  14. PTCHD40.61 (0.48–0.78)7.4 × 10−4
  15. CLDN30.60 (0.49–0.72)1.8 × 10−6
  16. CITED40.59 (0.46–0.75)4.6 × 10−4
  17. SH2D50.58 (0.47–0.73)5.6 × 10−5
  18. RIMS20.58 (0.47–0.72)3.3 × 10−5
  19. SLC6A200.57 (0.48–0.68)2.1 × 10−8
  20. SPATA180.53 (0.46–0.62)1.2 × 10−13
Show the other 27 genes
  1. DONSON1.69 (1.23–2.33)0.0075
  2. RGS171.60 (1.21–2.12)0.0069
  3. NKAIN11.54 (1.16–2.06)0.015
  4. OPRD11.54 (1.15–2.06)0.018
  5. GPRIN11.50 (1.10–2.05)0.036
  6. PRDM121.50 (1.09–2.05)0.041
  7. AKR1C21.46 (1.13–1.89)0.019
  8. LINC004750.77 (0.63–0.94)0.033
  9. FAM189A10.76 (0.61–0.94)0.038
  10. BAMBI0.76 (0.61–0.94)0.036
  11. KIAA04080.73 (0.60–0.90)0.015
  12. MUC120.73 (0.57–0.94)0.045
  13. SOGA30.73 (0.59–0.91)0.021
  14. CCL180.73 (0.58–0.93)0.037
  15. HS3ST20.73 (0.57–0.93)0.040
  16. HGFAC0.72 (0.57–0.92)0.031
  17. PLEKHN10.72 (0.57–0.91)0.026
  18. CHRNA10.71 (0.56–0.90)0.020
  19. GALNT50.70 (0.57–0.88)0.0091
  20. PLCD30.70 (0.55–0.89)0.016
  21. LRRC200.70 (0.56–0.88)0.011
  22. EDA2R0.70 (0.54–0.91)0.026
  23. C3orf670.69 (0.55–0.88)0.013
  24. FFAR40.69 (0.53–0.90)0.026
  25. SYT120.69 (0.54–0.88)0.013
  26. HYDIN0.68 (0.55–0.85)0.0058
  27. MUC3A0.67 (0.53–0.85)0.0058
longer PFIshorter PFI
Figure 4. Hazard ratio per standard deviation of tumor expression with 95% confidence interval, log scale, for the 47 genes with FDR < 0.05.

For the 150 top-ranked candidates, a Cox model relates tumor expression, as a continuous value, to progression-free interval in 283 patients (55 events). No high/low cutpoint is searched for, since optimized cutpoints inflate false positives.

A Hazard ratio per SDFrom a Cox model with the gene’s tumor expression as a continuous variable: the change in hazard for each one-standard-deviation increase. Above 1, higher expression goes with a shorter time to the event; below 1, with a longer one. above 1 means higher expression goes with a shorter progression-free interval. These are associations in tissue, not evidence that a blood level predicts outcome.

Caveats for KIRP

  • With only 60 normal samples, confidence intervals are wide; treat close ranks as ties.
  • The blood comparison sets TCGA tumors against GTEx blood. Study and biology cannot be separated there, which is why genes made by blood immune cells are removed as well.

Limitations that apply to every cancer type