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pmid:39411205

Revolutionizing prognostic predictions in colorectal cancer: Macrophage‑driven transcriptional insights from single‑cell RNA sequencing and gene co‑expression network analysis.

Yang Feng · Zhuo Cheng · Jingyuan Gao · Tao Huang · Jun Wang · Qian Tang · Ke Pu · Chang Liu

Oncology letters · 2024 doi:10.3892/ol.2024.14721

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title
Revolutionizing prognostic predictions in colorectal cancer: Macrophage‑driven transcriptional insights from single‑cell RNA sequencing and gene co‑expression network analysis.
doi
10.3892/ol.2024.14721
pmid
39411205
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Yang Feng
[1]
Zhuo Cheng
[2]
Jingyuan Gao
[3]
Tao Huang
[4]
Jun Wang
[5]
Qian Tang
[6]
Ke Pu
[7]
Chang Liu
journal
Oncology letters
year
2024
abstract
Tumor-associated macrophages have become important biomarkers for cancer diagnosis, prognosis and therapy. The dynamic changes in macrophage subpopulations significantly impact the outcomes of cancer immunotherapy. Hence, identifying additional macrophage-related biomarkers is essential for enhancing prognostic predictions in colorectal cancer (CRC) immunotherapy. CRC single-cell RNA sequencing (scRNA-seq) data was obtained from the Gene Expression Omnibus (GEO) database. The data were processed, normalized and clustered using the 'Seurat' package. Cell types within each cluster were annotated using the 'SingleR' package. Weighted gene co-expression network analysis identified modules corresponding to specific cell types. A non-negative matrix factorization algorithm was employed to segregate different clusters based on the selected module. Differentially expressed genes (DEGs) were identified across various clusters and a prognostic model was constructed using lasso regression and Cox regression analyses. The robustness of the model was validated using The Cancer Genome Atlas (TCGA) database and GEO microarrays. Additionally, the prognosis, immune characteristics and response to immune checkpoint inhibitor (ICI) therapy were individually analyzed. The scRNA-seq data from GSE200997, consisting of 23 samples, were analyzed. Dimensionality reduction and cluster identification allowed the isolation of the primary myeloid cell subpopulations. The macrophage-related brown module was identified, which was further divided into two clusters. Using the DEGs from these clusters, a prognostic model was developed, comprising five macrophage-related genes. The robustness of the model was confirmed using microarray datasets GSE17536, GSE38832 and GSE39582, as well as TCGA cohort. Patients classified as high-risk by the present model exhibited poorer survival rates, lower tumor mutation burden, reduced microsatellite instability, lower tumor purity, more severe tumor immune dysfunction and exclusion, and less benefit from ICIs therapy compared with low-risk patients. The present prognostic model shows promise as a biomarker for risk stratification and predicting therapeutic efficacy in patients with CRC. However, further well-designed prospective studies are necessary to validate the findings.
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To streamline the findings, LASSO regression and Cox regression were used to select independent prognostic markers (Fig. 4A).
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[104]
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[118]
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[119]
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[120]
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[122]
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[123]
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[124]
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[125]
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[126]
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[127]
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[128]
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[129]
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[130]
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[131]
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[132]
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[133]
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[134]
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[135]
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[136]
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[137]
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[138]
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[139]
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[140]
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[141]
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[142]
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[143]
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[144]
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[145]
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[146]
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[147]
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[148]
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[149]
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[150]
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[151]
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[152]
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[153]
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[154]
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[155]
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[156]
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[157]
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[158]
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[159]
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[160]
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[161]
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[162]
GSM972100
[163]
GSM972101
[164]
GSM972102
[165]
GSM972103
[166]
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[167]
GSM972105
[168]
GSM972106
[169]
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[170]
GSM972108
[171]
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[172]
GSM972110
[173]
GSM972111
[174]
GSM972112
[175]
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[176]
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[177]
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[178]
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[179]
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[180]
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[181]
GSM972119
[182]
GSM972120
[183]
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[184]
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[185]
GSM972123
[186]
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[187]
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[188]
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[189]
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[190]
GSM972128
[191]
GSM972129
[192]
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[193]
GSM972131
[194]
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[195]
GSM972133
[196]
GSM972134
[197]
GSM972135
[198]
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[199]
GSM972137
[200]
GSM972138
[201]
GSM972139
[202]
GSM972140
[203]
GSM972141
[204]
GSM972142
[205]
GSM972143
[206]
GSM972144
[207]
GSM972145
[208]
GSM972146
[209]
GSM972147
[210]
GSM972148
[211]
GSM972149
[212]
GSM972150
[213]
GSM972151
[214]
GSM972152
[215]
GSM972153
[216]
GSM972154
[217]
GSM972155
[218]
GSM972156
[219]
GSM972157
[220]
GSM972158
[221]
GSM972159
[222]
GSM972160
[223]
GSM972161
[224]
GSM972162
[225]
GSM972163
[226]
GSM972164
[227]
GSM972165
[228]
GSM972166
[229]
GSM972167
[230]
GSM972168
[231]
GSM972169
[232]
GSM972170
[233]
GSM972171
[234]
GSM972172
[235]
GSM972173
[236]
GSM972174
[237]
GSM972175
[238]
GSM972176
[239]
GSM972177
[240]
GSM972178
[241]
GSM972179
[242]
GSM972180
[243]
GSM972181
[244]
GSM972182
[245]
GSM972183
[246]
GSM972184
[247]
GSM972185
[248]
GSM972186
[249]
GSM972187
[250]
GSM972188
[251]
GSM972189
[252]
GSM972190
[253]
GSM972191
[254]
GSM972192
[255]
GSM972193
[256]
GSM972194
[257]
GSM972195
[258]
GSM972196
[259]
GSM972197
[260]
GSM972198
[261]
GSM972199
[262]
GSM972200
[263]
GSM972201
[264]
GSM972202
[265]
GSM972203
[266]
GSM972204
[267]
GSM972205
[268]
GSM972206
[269]
GSM972207
[270]
GSM972208
[271]
GSM972209
[272]
GSM972210
[273]
GSM972211
[274]
GSM972212
[275]
GSM972213
[276]
GSM972214
[277]
GSM972215
[278]
GSM972216
[279]
GSM972217
[280]
GSM972218
[281]
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[282]
GSM972220
[283]
GSM972221
[284]
GSM972222
[285]
GSM972223
[286]
GSM972224
[287]
GSM972225
[288]
GSM972226
[289]
GSM972227
[290]
GSM972228
[291]
GSM972229
[292]
GSM972230
[293]
GSM972231
[294]
GSM972232
[295]
GSM972233
[296]
GSM972234
[297]
GSM972235
[298]
GSM972236
[299]
GSM972237
[300]
GSM972238
[301]
GSM972239
[302]
GSM972240
[303]
GSM972241
[304]
GSM972242
[305]
GSM972243
[306]
GSM972244
[307]
GSM972245
[308]
GSM972246
[309]
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[310]
GSM972248
[311]
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[312]
GSM972250
[313]
GSM972251
[314]
GSM972252
[315]
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[316]
GSM972254
[317]
GSM972255
[318]
GSM972256
[319]
GSM972257
[320]
GSM972258
[321]
GSM972259
[322]
GSM972260
[323]
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[324]
GSM972262
[325]
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[326]
GSM972264
[327]
GSM972265
[328]
GSM972266
[329]
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[330]
GSM972268
[331]
GSM972269
[332]
GSM972270
[333]
GSM972271
[334]
GSM972272
[335]
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[336]
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[337]
GSM972275
[338]
GSM972276
[339]
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[340]
GSM972278
[341]
GSM972279
[342]
GSM972280
[343]
GSM972281
[344]
GSM972282
[345]
GSM972283
[346]
GSM972284
[347]
GSM972285
[348]
GSM972286
[349]
GSM972287
[350]
GSM972288
[351]
GSM972289
[352]
GSM972290
[353]
GSM972291
[354]
GSM972292
[355]
GSM972293
[356]
GSM972294
[357]
GSM972295
[358]
GSM972296
[359]
GSM972297
[360]
GSM972298
[361]
GSM972299
[362]
GSM972300
[363]
GSM972301
[364]
GSM972302
[365]
GSM972303
[366]
GSM972304
[367]
GSM972305
[368]
GSM972306
[369]
GSM972307
[370]
GSM972308
[371]
GSM972309
[372]
GSM972310
[373]
GSM972311
[374]
GSM972312
[375]
GSM972313
[376]
GSM972314
[377]
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[378]
GSM972316
[379]
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[380]
GSM972318
[381]
GSM972319
[382]
GSM972320
[383]
GSM972321
[384]
GSM972322
[385]
GSM972323
[386]
GSM972324
[387]
GSM972325
[388]
GSM972326
[389]
GSM972327
[390]
GSM972328
[391]
GSM972329
[392]
GSM972330
[393]
GSM972331
[394]
GSM972332
[395]
GSM972333
[396]
GSM972334
[397]
GSM972335
[398]
GSM972336
[399]
GSM972337
[400]
GSM972338
[401]
GSM972339
[402]
GSM972340
[403]
GSM972341
[404]
GSM972342
[405]
GSM972343
[406]
GSM972344
[407]
GSM972345
[408]
GSM972346
[409]
GSM972347
[410]
GSM972348
[411]
GSM972349
[412]
GSM972350
[413]
GSM972351
[414]
GSM972352
[415]
GSM972353
[416]
GSM972354
[417]
GSM972355
[418]
GSM972356
[419]
GSM972357
[420]
GSM972358
[421]
GSM972359
[422]
GSM972360
[423]
GSM972361
[424]
GSM972362
[425]
GSM972363
[426]
GSM972364
[427]
GSM972365
[428]
GSM972366
[429]
GSM972367
[430]
GSM972368
[431]
GSM972369
[432]
GSM972370
[433]
GSM972371
[434]
GSM972372
[435]
GSM972373
[436]
GSM972374
[437]
GSM972375
[438]
GSM972376
[439]
GSM972377
[440]
GSM972378
[441]
GSM972379
[442]
GSM972380
[443]
GSM972381
[444]
GSM972382
[445]
GSM972383
[446]
GSM972384
[447]
GSM972385
[448]
GSM972386
[449]
GSM972387
[450]
GSM972388
[451]
GSM972389
[452]
GSM972390
[453]
GSM972391
[454]
GSM972392
[455]
GSM972393
[456]
GSM972394
[457]
GSM972395
[458]
GSM972396
[459]
GSM972397
[460]
GSM972398
[461]
GSM972399
[462]
GSM972400
[463]
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[464]
GSM972402
[465]
GSM972403
[466]
GSM972404
[467]
GSM972405
[468]
GSM972406
[469]
GSM972407
[470]
GSM972408
[471]
GSM972409
[472]
GSM972410
[473]
GSM972411
[474]
GSM972412
[475]
GSM972413
[476]
GSM972414
[477]
GSM972415
[478]
GSM972416
[479]
GSM972417
[480]
GSM972418
[481]
GSM972419
[482]
GSM972420
[483]
GSM972421
[484]
GSM972422
[485]
GSM972423
[486]
GSM972424
[487]
GSM972425
[488]
GSM972426
[489]
GSM972427
[490]
GSM972428
[491]
GSM972429
[492]
GSM972430
[493]
GSM972431
[494]
GSM972432
[495]
GSM972433
[496]
GSM972434
[497]
GSM972435
[498]
GSM972436
[499]
GSM972437
[500]
GSM972438
[501]
GSM972439
[502]
GSM972440
[503]
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[504]
GSM972442
[505]
GSM972443
[506]
GSM972444
[507]
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[508]
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[509]
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[510]
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[511]
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[512]
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[513]
GSM972451
[514]
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[515]
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[516]
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[517]
GSM972455
[518]
GSM972456
[519]
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[520]
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[521]
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[522]
GSM972460
[523]
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[524]
GSM972462
[525]
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[526]
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[527]
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[528]
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[529]
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[530]
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[531]
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[532]
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[533]
GSM972471
[534]
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[535]
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[536]
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[537]
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[538]
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[539]
GSM972477
[540]
GSM972478
[541]
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[542]
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[543]
GSM972481
[544]
GSM972482
[545]
GSM972483
[546]
GSM972484
[547]
GSM972485
[548]
GSM972486
[549]
GSM972487
[550]
GSM972488
[551]
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[552]
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[553]
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[554]
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[555]
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[558]
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[560]
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[561]
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[563]
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[564]
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[565]
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[566]
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[567]
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[568]
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[569]
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[570]
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[571]
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[572]
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[573]
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[574]
GSM972512
[575]
GSM972513
[576]
GSM972514
[577]
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[578]
GSM972516
[579]
GSM972517
[580]
GSM972518
[581]
GSM972519
[582]
GSM972520
[583]
GSM972521
[584]
GSM972522
sample_count
585
group_sizes
null
group_sizes_basis
null
platform
570
taxon
Homo sapiens
title
Gene expression Classification of Colon Cancer defines six molecular subtypes with distinct clinical, molecular and survival characteristics [Expression]
resolution_checked_at
2026-11-01T15:39:54Z
resolution_check
identifier_resolution_only_no_download_no_licence_read
downloaded
false
licence_read
false
gaps array · 8 items
[0]
asset:data:geo:GSE17536 not downloaded
[1]
asset:data:geo:GSE17536 licence not read
[2]
asset:data:geo:GSE200997 not downloaded
[3]
asset:data:geo:GSE200997 licence not read
[4]
asset:data:geo:GSE38832 not downloaded
[5]
asset:data:geo:GSE38832 licence not read
[6]
asset:data:geo:GSE39582 not downloaded
[7]
asset:data:geo:GSE39582 licence not read
analysis object · 4 fields
state
partial
methods array · 5 items
[0] object · 10 fields
id
method-1
family
survival_analysis
specific
cox_proportional_hazards
label
Survival analysis
dsl_operations array · 1 item
[0]
cox_ph
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
time_to_event_with_censoring
evidence object · 3 fields
score
1
band
verbatim
quote
To streamline the findings, LASSO regression and Cox regression were used to select independent prognostic markers (Fig. 4A).
[1] object · 10 fields
id
method-2
family
group_comparison
specific
wilcoxon
label
Group comparison
dsl_operations array · 0 items
missing_operations array · 1 item
[0]
group_comparison_wilcoxon
expressible
false
parameters object · 0 fields
data_shapes array · 1 item
[0]
two_or_more_groups_continuous
evidence object · 3 fields
score
0.8
band
high
quote
Wilcoxon
[2] object · 10 fields
id
method-3
family
differential_expression
specific
limma_voom
label
Differential expression analysis
dsl_operations array · 3 items
[0]
normalise_counts
[1]
differential_expression_fit
[2]
fdr_bh
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
count_matrix_with_condition
evidence object · 3 fields
score
0.8
band
high
quote
limma
[3] object · 10 fields
id
method-4
family
correlation_analysis
specific
pearson
label
Correlation analysis
dsl_operations array · 1 item
[0]
pearson_r
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
paired_numeric_columns
evidence object · 3 fields
score
0.8
band
high
quote
Pearson correlation
[4] object · 10 fields
id
method-5
family
diagnostic_evaluation
specific
roc
label
Diagnostic test evaluation
dsl_operations array · 6 items
[0]
confusion_counts
[1]
roc_curve
[2]
sensitivity_specificity
[3]
solve_confusion_matrix
[4]
classification_metrics
[5]
published_precision_envelope
missing_operations array · 0 items
parameters object · 0 fields
data_shapes array · 1 item
[0]
reference_standard_and_test
evidence object · 3 fields
score
0.8
band
high
quote
ROC curve
targets array · 0 items
gaps array · 1 item
[0]
method.reader_differs_from_extractor
capability object · 3 fields
state
complete
requirements array · 1 item
[0] object · 6 fields
capability_id
capability:computational_analysis
label
Computational analysis platform
assay_families array · 9 items
[0]
meta_analysis
[1]
machine_learning
[2]
survival_analysis
[3]
statistical_regression
[4]
pca
[5]
molecular_docking
[6]
network_analysis
[7]
differential_expression
[8]
gwas
candidate
Automated Assays Laravel application and simulated adapters
gaps array · 0 items
readiness object · 8 fields
state
complete
tier
R1
blocked_on
nothing:replicable_now
axes object · 3 fields
method object · 5 fields
tier
R1
expressible
true
required_operations array · 1 item
[0]
cox_ph
missing_operations array · 0 items
basis
The paper names the method it used and the corpus's method extractor maps it onto the DSL catalogue: cox_proportional_hazards.
capability object · 4 fields
tier
R1
capability_id
capability:computational_analysis
availability_state
owned
basis
The computational analysis is owned. The physical bench the paper used is not, so a physical validation cannot be run here.
data object · 6 fields
tier
R1
reachable
true
accessions array · 4 items
[0]
GSE200997
[1]
GSE17536
[2]
GSE38832
[3]
GSE39582
resolved_accessions array · 4 items
[0]
GSE200997
[1]
GSE17536
[2]
GSE38832
[3]
GSE39582
statements array · 1 item
[0]
The data generated in the present study may be requested from the corresponding author.
basis
The paper names GSE200997, GSE17536, GSE38832, GSE39582 in Gene Expression Omnibus (GEO) and the accession resolves in the repository. No dataset was downloaded and no licence was read.
can_run object · 3 fields
dsl_operations array · 1 item
[0]
cox_ph
assets array · 2 items
[0]
source:pmid:39411205
[1]
data:geo:GSE17536
steps array · 1 item
[0]
run_analysis
must_build object · 5 fields
dsl_operations array · 0 items
converters array · 0 items
readers array · 0 items
capabilities array · 0 items
data_gaps array · 1 item
[0]
data:geo:GSE17536
gaps array · 0 items
review object · 5 fields
state
partial
machine_critique object · 5 fields
kind
extraction_review
reader object · 5 fields
model
MiniMax-M3.1-Flash
provider
minimax
provider_family
minimax
read_at
2026-11-01T19:35:18Z
independence
single_reader
evidence_check object · 2 fields
method
verbatim
availability
verbatim
calls
5
usage object · 3 fields
prompt_tokens
12075
completion_tokens
2095
cached_tokens
264
stages array · 1 item
[0] object · 4 fields
id
extraction
state
complete
reader
MiniMax-M3.1-Flash
provider
minimax
gaps array · 3 items
[0]
review.independent_reader
[1]
review.machine_critique
[2]
review.human_critique
execution object · 5 fields
state
absent
status
not_run
mode
null
runs array · 0 items
gaps array · 1 item
[0]
execution.not_run
provenance object · 7 fields
entities array · 3 items
[0] object · 3 fields
id
pmid:39411205
type
paper
label
pmid:39411205
[1] object · 3 fields
id
source:pmid:39411205
type
manuscript
label
PMID 39411205 full text
[2] object · 3 fields
id
data:geo:GSE17536
type
dataset
label
GEO GSE17536
activities array · 3 items
[0] object · 4 fields
id
activity-reading-sci-2026-11-01
type
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PMID 39411205 full text
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en transcriptional insights from single‑cell RNA sequencing and gene co‑expression network analysis.
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cancer (CRC) immunotherapy. CRC single-cell RNA sequencing (scRNA-seq) data was obtained from the Gene
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n colorectal cancer (CRC) immunotherapy. CRC single-cell RNA sequencing (scRNA-seq) data was obtained from the Gene
[1]
munotherapy. CRC single-cell RNA sequencing (scRNA-seq) data was obtained from the Gene Expression
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ICI) therapy were individually analyzed. The scRNA-seq data from GSE200997, consisting of 23 sample
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robustness of the model was confirmed using microarray datasets GSE17536, GSE38832 and GSE39582, as
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ation and predicting therapeutic efficacy in patients with CRC. However, further well-designed prospect
[1]
risk score were conducted within a cohort of patients with urothelial cancer treated with anti-PD-L1 th
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the MHGS risk score were conducted within a cohort of patients with urothelial cancer treated with anti-PD-
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of 18 signature genes (41). Cell culture and transfection procedures CRC cell lines (RKO, SW480 and Lo
[1]
ained from Shanghai GenePharma Co., Ltd. and transfected using Lipofectamine® 2000 (Invitrogen; Therm
[2]
plasmid DNA per well in a 6-well plate. The transfection was carried out at 37°C for 4–6 h. A 48-h in
[3]
–6 h. A 48-h interval was maintained between transfection and subsequent experimentation. Cell culture
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epithelial cells (HIEC) and 293T cells were cultured in high-glucose DMEM medium (HyClone; Cytiva) s
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o Fisher Scientific, Inc.). HCT15 cells were cultured in RPMI 1640 medium (HyClone; Cytiva) with 10%
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To streamline the findings, LASSO regression and Cox regression were used to select independent prognostic markers (Fig. 4A).
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Complete file
{"sci_version": "1.2", "schema": "https://corpus.bntrg.co.uk/sci/1.0/sci.schema.json", "id": "pmid:39411205", "kind": "science-record", "revision": 3, "content_hash": "sha256:a91ec2df13748ee48efafeac24019b5fcd0717b347896f3d49dc1d0afbee5bb5", "created_at": "2026-11-01T20:29:38Z", "updated_at": "2026-11-01T22:12:56Z", "generator": {"name": "corpus-assay-engine", "run_id": "reading-sci-2026-11-01"}, "paper": {"state": "complete", "title": "Revolutionizing prognostic predictions in colorectal cancer: Macrophage‑driven transcriptional insights from single‑cell RNA sequencing and gene co‑expression network analysis.", "doi": "10.3892/ol.2024.14721", "pmid": "39411205", "authors": ["Yang Feng", "Zhuo Cheng", "Jingyuan Gao", "Tao Huang", "Jun Wang", "Qian Tang", "Ke Pu", "Chang Liu"], "journal": "Oncology letters", "year": 2024, "abstract": "Tumor-associated macrophages have become important biomarkers for cancer diagnosis, prognosis and therapy. The dynamic changes in macrophage subpopulations significantly impact the outcomes of cancer immunotherapy. Hence, identifying additional macrophage-related biomarkers is essential for enhancing prognostic predictions in colorectal cancer (CRC) immunotherapy. CRC single-cell RNA sequencing (scRNA-seq) data was obtained from the Gene Expression Omnibus (GEO) database. The data were processed, normalized and clustered using the 'Seurat' package. Cell types within each cluster were annotated using the 'SingleR' package. Weighted gene co-expression network analysis identified modules corresponding to specific cell types. A non-negative matrix factorization algorithm was employed to segregate different clusters based on the selected module. Differentially expressed genes (DEGs) were identified across various clusters and a prognostic model was constructed using lasso regression and Cox regression analyses. The robustness of the model was validated using The Cancer Genome Atlas (TCGA) database and GEO microarrays. Additionally, the prognosis, immune characteristics and response to immune checkpoint inhibitor (ICI) therapy were individually analyzed. The scRNA-seq data from GSE200997, consisting of 23 samples, were analyzed. Dimensionality reduction and cluster identification allowed the isolation of the primary myeloid cell subpopulations. The macrophage-related brown module was identified, which was further divided into two clusters. Using the DEGs from these clusters, a prognostic model was developed, comprising five macrophage-related genes. The robustness of the model was confirmed using microarray datasets GSE17536, GSE38832 and GSE39582, as well as TCGA cohort. Patients classified as high-risk by the present model exhibited poorer survival rates, lower tumor mutation burden, reduced microsatellite instability, lower tumor purity, more severe tumor immune dysfunction and exclusion, and less benefit from ICIs therapy compared with low-risk patients. The present prognostic model shows promise as a biomarker for risk stratification and predicting therapeutic efficacy in patients with CRC. 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CRC single-cell RNA sequencing (scRNA-seq) data was obtained from the Gene"], "order_basis": "paper_text_order:76", "id": "assay:1"}, {"role": "data_generation", "definition_id": "scrna_seq", "definition_version": "99e1195c7d6f", "definition_digest": null, "required_operations": [], "support_state": "unsupported", "source_hashes": {"assay_lexicon": "99e1195c7d6f"}, "confidence": 0.99, "evidence": ["n colorectal cancer (CRC) immunotherapy. CRC single-cell RNA sequencing (scRNA-seq) data was obtained from the Gene", "munotherapy. CRC single-cell RNA sequencing (scRNA-seq) data was obtained from the Gene Expression", "ICI) therapy were individually analyzed. 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