CAF-associated genes putatively representing distinct prognosis by in silico landscape of stromal components of colon cancer

Kota Okuno; Kyonosuke Ikemura; Riku Okamoto; Keiko Oki; Akiko Watanabe; Yu Kuroda; Mikiko Kidachi; Shiori Fujino; Yusuke Nie; Tadashi Higuchi; Motohiro Chuman; Marie Washio; Mikiko Sakuraya; Masahiro Niihara; Koshi Kumagai; Takafumi Sangai; Yusuke Kumamoto; Takeshi Naitoh; Naoki Hiki; Keishi Yamashita

doi:10.1371/journal.pone.0299827

. 2024 Apr 1;19(4):e0299827. doi: 10.1371/journal.pone.0299827

CAF-associated genes putatively representing distinct prognosis by in silico landscape of stromal components of colon cancer

Kota Okuno ¹, Kyonosuke Ikemura ¹, Riku Okamoto ¹, Keiko Oki ¹, Akiko Watanabe ¹, Yu Kuroda ¹, Mikiko Kidachi ¹, Shiori Fujino ¹, Yusuke Nie ¹, Tadashi Higuchi ², Motohiro Chuman ², Marie Washio ², Mikiko Sakuraya ², Masahiro Niihara ², Koshi Kumagai ², Takafumi Sangai ³, Yusuke Kumamoto ⁴, Takeshi Naitoh ⁵, Naoki Hiki ², Keishi Yamashita ^1,^*

Editor: Chen Li⁶

PMCID: PMC10984474 PMID: 38557819

Abstract

Comprehensive understanding prognostic relevance of distinct tumor microenvironment (TME) remained elusive in colon cancer. In this study, we performed in silico analysis of the stromal components of primary colon cancer, with a focus on the markers of cancer-associated fibroblasts (CAF) and tumor-associated endothelia (TAE), as well as immunological infiltrates like tumor-associated myeloid cells (TAMC) and cytotoxic T lymphocytes (CTL). The relevant CAF-associated genes (CAFG)(representing R index = 0.9 or beyond with SPARC) were selected based on stroma specificity (cancer stroma/epithelia, cS/E = 10 or beyond) and expression amounts, which were largely exhibited negative prognostic impacts. CAFG were partially shared with TAE-associated genes (TAEG)(PLAT, ANXA1, and PTRF) and TAMC-associated genes (TAMCG)(NNMT), but not with CTL-associated genes (CTLG). Intriguingly, CAFG were prognostically subclassified in order of fibrosis (representing COL5A2, COL5A1, and COL12A1) followed by exclusive TAEG and TAMCG. Prognosis was independently stratified by CD8A, a CTL marker, in the context of low expression of the strongest negative prognostic CAFG, COL8A1. CTLG were comprehensively identified as IFNG, B2M, and TLR4, in the group of low S/E, representing good prognosis. Our current in silico analysis of the micro-dissected stromal gene signatures with prognostic relevance clarified comprehensive understanding of clinical features of the TME and provides deep insights of the landscape.

Introduction

Cancer is widely recognized as a genetic disease, arising from a combination of inherited (germline mutations) and acquired (somatic mutations) genetic aberrations. These anomalies include gene amplifications [1–3], gene losses [4–6], and gene mutations [7–11], which cumulatively lead to the transformation of cells. Interestingly, despite the accumulation of these genetic alterations, a mathematical model has suggested a relatively low selective advantage (0.004) during tumor progression [12]. Consistent with these findings, single driver gene, for example c-MYC expression did not show prognostic relevance at all in human colorectal cancer (CRC) [13, 14], although its deletion rescued tumorigenesis of APC deficiency [15]. These finding suggested that single driver gene expression alone can not phenotypically affect cancer metastasis.

On the other hand, minimal functional driver gene heterogeneity of mutations was recently confirmed among the metastasis of the individual cancer patients [16, 17], and hence tumor progression is rather associated not with genetic aberrations, plus with epigenetic [18] and/or tumor microenvironment (TME) abnormality [19]. In our current study, we therefore focused on the TME affecting patient prognosis of CRC. The TME are mainly composed of cancer-associated fibroblasts (CAF), tumor-associated endothelia (TAE), cytotoxic T lymphocytes (CTL), and tumor-associated myeloid cells (TAMC) and their derivatives or subpopulations.

The public databases of stromal expression after microdissection of the 13 CRC tumors (GSE35602) [20] was herein used for in silico landscape of stromal components of colon cancer together with comprehensive prognostic relevance was assessed from the bulky tissues in the 232 colon cancer patients (GSE17538) [21] to clarify their comprehensive prognostic roles of the TME. Objectives of in silico analysis of CRC tumors were to obtain results from the same assessment, and public database can be accessed and obtained by other researchers.

Material and methods

Expression profiles of the microdissection tissues of the 13 CRC tumors (GSE35602)

The public databases of stromal (cancer str) expression after microdissection of the 13 CRC tumors (GSE35602) were used in the microarray (Human Genome, Whole 4x44K, Agilent Inc, Santa Clara, CA) harboring 45 015 genes [20], where cancer str/epi expression ratio (cS/E ratio) was calculated. Signal intensities were adjusted by single GAPDH probe (A_23_P13899).

Prognostic analysis from the bulk tissues of the 232 colon cancer tumors (GSE17538)

The public database (GSE17538) was used for prognostic analysis of 232 colon tumor tissues in the microarray base (Human Genome U133 Plus 2.0 Array, Thermo Fisher Scientific Inc, Waltham, MA) [21]. Signal intensities were adjusted by single GAPDH probe (212581_x_at). This database included clinicopathological factors (age, sex, T factor, N factor, M factor, and prognosis representing death). Area under curve (AUC) was calculated for prediction of death according to the individual probes, and the best optimized cut-off value was determined.

Statistical analysis

Follow up data were evaluated in terms of overall survival. The follow-up time was calculated from the date of surgery to death or end-point. Overall survival was estimated by the Kaplan-Meier method, and compared using the log-rank test. Variables suggesting potential prognostic factors on univariate analyses were subjected to a multivariate analysis using a Cox proportional-hazards model. A P-value <0.05 was considered to indicate statistical significance. All statistical analyses were conducted using the SAS software package (JMP Pro16, SAS Institute, Cary, NC).

Ethics statement

Ethical approval has not been obtained because this is an in silico study using only the Public Database.

Results

CAF-associated genes (CAFG) and other TME-associated genes in cancer str of the CRC tumors by in silico analysis

The TME is composed of CAF and TAE representing tumor angiogenesis as well as immunological infiltrates such as myeloid cells, T cells, and B cells or their subpopulations. We initially identified the most relevant CAF-associated genes (CAFG, 256 probes in S1 Table) after selection by stroma specific (cS/E = 10 or beyond) and their strong association (R index = 0.9 or beyond) with well-known stromal marker representing fibroblasts [22], SPARC expression in cancer str of the CRC tumors after microdissection in the array-based public database (GSE35602) [20]. Intriguingly, they included well-established CAF markers including VIM, ACTA2 (aSMA), PDGFRB, and FAP [23] (black color in S1 Table), suggesting that SPARC expression may represent CAF activation in the TME.

The 4 CAF markers have been all demonstrated to be pan-CAF ones, where single cell RNA sequencing (scRNA) clarified that they were expressed in all 4 CAF subpopulations (vascular CAF, matrix CAF, cycling CAF, and developmental CAF) [24]. The 161 CAFG probes with the highest expression amounts (signal intensity/GAPDHx100 = 40 or beyond in NCC210, S1 Table, red letters) included 115 SYMBOL genes (including SPARC), among which the top 76 genes according to expression amounts were finally selected for prognostic analysis using the public database of 232 colon cancer patients (GSE17538) [21]. Among the 76 genes, 62 genes showed negative prognostic impact (statistically significant, p<0.05), and the 27 genes were commonly shared with any TME markers-associated genes (selected by cS/E ratio = 10 or beyond and high expression amounts = 40 or beyond similarly with CAFG) (Fig 1A).

Fig 1 — **(a)** The 27 CAFG that also highly (R = 0.9 or beyond) correlate with TME markers. They are listed in order of the best AUC values determined individually by prognostic analysis. In the contest of *CD3G*, fibrosis CAFG ranked as the most aggressive negative prognostic factors in contrast to non-fibrosis CAFG. **(b)** Common and unique relations to CAFG in the individual TME-associated genes.

The individual TME markers and their associated genes were selected from S2 Table for PECAM1 (CD34) representing TAE (ex: PTRF, VIM, COL4A2, ANXA1) [25], S3 Table for CD8A representing CTL (ex: IGFBP3, C3, FBN1, CYBRD1), S4 Table for CD14 representing TAMC (ex: DCN, IGFBP7, NNMT, DKK3) [22], S5 Table for CD3G representing tumor infiltrating T lymphocytes (CD3 TIL)(ex: DCN, SPARC, LUM, COL1A1), S8 Table for ARG1 representing functional myeloid derived suppressor cell (fMDSC)(ex: A2M, ACTG2, CAV1), S9 Table for CD33 representing immature myeloid derived suppressor cell (iMDSC)(ex: IGFBP7, VIM, ACTA2, LAMA4), S10 Table for FoxP3 representing regulatory T cell (Treg)(ex: ACTG2, CNN1, MEG3), S11 Table for MS4A1 representing tumor infiltrating B lymphocytes (B-TIL)(ex: NOX1) [26], and S12 Table for S100A9 representing tumor-associated neutrophil (TAN)(ex: DEFB1) [25].

In the microarray-based database, for example, one of the CAFG, COL3A1 expression was closely associated with SPARC (0.89<R<0.99) among the 3 different sets (10 separate kinds of sequences/set) of microarray probes and R indexes between the 3 probe sets were ranged between 0.86 and 0.95 each other (Fig 2A), suggesting that association indicating R = 0.9 may represent same molecular relevance. We therefore thought that gene numbers of the specific TME marker-associated genes (R index = 0.9 or beyond) may represent their molecular signature impacts representing the similar stromal molecular phenotypes (Fig 2B).

CAFG are partially shared with TAE-associated genes (TAEG) representing tumor angiogenesis in colon cancer

As shown in Fig 2B, SPARC expression putatively representing CAF activation was closely associated with 5 256 genes (R = 0.9 or beyond) (S1 Table), while PECAM1 (CD34) expression represented as TAE activation was associated with 719 genes (S2 Table). Moreover, SPARC expression amount was much higher than that of PECAM1 as well as other representative TME markers (in NCC210) (Fig 2C). Thus, CAF biology should play a central role in TME activation among the stromal components of the CRC tumors.

As PECAM1 showed high cS/E ratio of 15.2 like CAFG, tumor angiogenesis was highly specific to cancer str of the CRC tumors (Fig 2D). We therefore explored genes closely associated with PECAM1 with R = 0.9 or beyond and with cS/E = 10 or beyond (same condition with CAFG) as TAEG according to high expression amounts with signal intensity (40 or beyond, purple color in S2 Table), which identified 36 gene probes (25 SYMBOL).

Intriguingly, the 25 genes identified as TAEG were overlapped with 10 CAFG (Fig 1B), among which 7 genes were negative prognostic factors (p<0.05) and PLAT, ANXA1, and PTRF (CAVIN1) were the strongest regarding AUC (Fig 1A). On the other hand, PECAM1 itself was not statistically significant for prognosis (p = 0.15) (Fig 2E). These findings suggested that tumor angiogenesis may be greatly affected by CAF biology, putatively because vascular CAF is the most dominant CAF subpopulation [24].

Blood vessels are composed of endothelia, pericytes, and surrounding basement membrane (BM) including type IV collagen [27] (Fig 3A), where they are marked by PECAM1/CDH5 (VE-cadherin)/TIE1, RGS5, and COL4A1/COL4A2 in the scRNA analysis [24], respectively. PECAM1 expression was closely associated with other well-established TAE markers such as CDH5 (R = 0.94) and TIE1 (R = 0.93) in cancer str of the CRC tumors (Fig 3B). Pericyte-specific marker (RGS5) expression is also associated with PECAM1 (R = 0.95) (Fig 3C), and BM markers (COL4A1/COL4A2) were tightly correlated in expression with PECAM1 (R = 0.97), either (Fig 3D). These findings suggested that PECAM1 may represent mature vascular structure in cancer str.

Among the 719 TAEG (Fig 2B), on the other hand, PECAM1 was the most strongly associated with CAV1 (0.97<R<0.99) in cancer str of the CRC tumors (S6 Table according to R index), suggesting that CAV1 (12<cS/E<17) plays a critical role in tumor angiogenesis in the TME as previously shown [28, 29]. Multiple different probes for CAV1 were enriched (10/20) as the top priority genes, which are accompanied by its close association with PTRF (CAVIN1), a component of caveola, ascribed to CAFG [30] (Fig 3E). These data indicated significant caveola contribution to tumor angiogenesis in CRC.

PECAM1 expression was also closely associated with ETS1/PLAT (Fig 3F) and CD274 (PDL1)/IFNGR1 (Fig 3G), indicating close involvement of tumor angiogenesis with unique proteolysis [31] and tumor immunity [32]. Nevertheless, their prognostic relevance was conditional; PTRF showed a potent negative prognostic factor putatively reflecting CAFG molecular features, whereas PDL1 was rather a positive prognostic factor in the same database (GSE17538) (Fig 3H). Finally, the 15 unique TAEG in Fig 1B (green color) are shown according to expression amounts (Fig 3I). Intriguingly, PDGFRA, a matrix-CAF marker [24] was included among the TAEG.

CAFG was prognostically independent of CTL-associated genes (CTLG) in colon cancer

The tumor infiltrates marked by CD8A and CD14 were then explored as markers representing CTL and TAMC, because they were demonstrated to be their specific markers by scRNA analysis in CRC, respectively [22]. CD8A and CD14 expressions were closely associated with 641 and 1 283 genes (R = 0.9 or beyond), respectively (Fig 2B), while CD8A expression amount was higher than CD14 expression in this microarray database (NCC210) (Fig 2C). They were both specific to cancer str (showing cS/E = 4.7), but the values were much lower than those of SPARC and PECAM1 (Fig 2D).

As expectedly, high CD8A expression showed significantly better prognosis than low CD8A expression in colon cancer (Fig 4A), as CD8 CTL was demonstrated to suppress tumorigenesis immunologically [33]. On the other hand, CD8A expression was not associated with a CAFG, SPARC except NCC210 in cancer str of the CRC tumors (R = 0.66, Fig 4B). This finding suggested that CAFG and CD8A may exhibit their independent contribution to prognosis, because most CAFG (75 among the 76 top CAF markers) were negative prognostic factors differently from CD8A. As expectedly, CD8A in combination with the strongest negative prognostic CAFG, COL8A1 expression exhibited additional stratification of prognosis, especially in cases with low COL8A1 expression (Fig 4C, both side black arrows). Interestingly, CD8A did not show such stratification in case of high COL8A1 expression, suggesting that CTL effect can not overcome that of CAF for prognosis.

CTLG were initially selected similarly as 37 gene probes (25 SYMBOL genes) with cS/E ratio = 10 or beyond and expression amounts = 40 or beyond (purple colors in S3 Table), where the 25 CTLG were not overlapped with any CAFG (red brown box in Fig 1B). These findings suggested that CTL activity is independent of CAF activation (Fig 4D). On the other hand, intriguingly, ITGB1 (cS/E ratio = 4.0) association with CD8A was shared with CAFG (Fig 4D), and ITGB1 was of negative prognostic relevance (p = 0.037), suggesting that ITGB1 may link CAF activation to CTL induction. Thus, CTL mobilization may be mediated by CAFG-associated ITGB1 induction of T lymphocytes. On the other hand, COL4A1/ETS1 association with CD8A was shared with TAEG as shown in Fig 4D, indicating that CAF activation may mediate angiogenesis with tissue destruction to CTL mobilization.

Among the top37 gene probes according to expression amounts (S3 Table), multiple probe sets of IGFBP3 were enriched as top genes (11/37) of the CTLG (S3 Table, and Fig 5A). Although IGFBP3 expression was strongly associated with CD8A expression (0.93<R<0.99) in cancer str of the CRC tumors (representative in Fig 5B), IGFBP3 knockdown (KD) unexpectedly increased CTL mobilization accompanied by attenuated tumorigenesis [34]. These findings suggested that IGFBP3 expression is not the cause of CTL mobilization during tumorigenesis, and may rather reflect host responses to CTL accumulation.

Fig 5 — **(a)** CTLG was closely associated with a CTL marker, *CD8A* (R = 0.9 or beyond), in cancer str of the CRC tumors, and the top 25 gene probes of CTLG with cS/E = 10 or beyond are shown according to expression amounts of NCC210. **(b)** Representative genes showed correlation to *CD8A* in cancer str of the CRC tumors. Green letters represent negative prognostic factors, while red letters represent positive prognostic factors in colon cancer (GSE17358). **(c)** Survival curves of the positive prognostic CTLG with cS/E = 10 or beyond in colon cancer (GSE17358). **(d)** Representative genes of CTLG with cS/E below 5 according to expression amounts of NCC210 (left panel), and red bars (*B2M*, *TLR4*, and *IFNG*) represent positive prognostic factors in colon cancer (GSE17358). High *B2M* expression showed significantly better prognosis than its low expression in colon cancer (right panel).

Consistent with the report [34], in primary colon cancer, high IGFBP3 expression showed significantly poorer prognosis than low IGFBP3 expression (p = 0.0028) totally differently from CD8A (Fig 4E). This may be partially explained by different cS/E between the 2 genes (the former was high cS/E, while the latter was low cS/E). Similarly negative prognostic effects were also confirmed for the CTLG (with high cS/E) such as FBN1 (p = 0.0014), CYBRD1 (p = 0.0004), FNDC1 (p = 0.013), MATN3 (p = 0.01), CFH (p = 0.022), NOX4 (p = 0.022), OLFML2B (p = 0.011), and F2R (p = 0.012)(green bars indicating significant negative prognostic factors in Fig 5A). These findings suggested that CTLG with high cS/E have promoting role during tumor progression, and did not reflect the antitumor function of CTL.

CTLG affecting good prognosis in colon cancer

CD8A expression was the most strongly associated with IFNG expression (0.97<R<0.99, 2.1<cS/E<2.5, S7 Table according to R index and Fig 4F), suggesting that IFNG could be an excellent indicator of CTL activity as previously shown [35]. As in S7 Table, multiple 10 different probes for IFNG were enriched as the top association with CD8A as well as 11 probes for STAT1 (R>0.97), a representative IFN-stimulated gene (ISG). S3 Table actually included other IFN pathway genes (TLR3,4,7/IFI44L/IFIT3/BST2/CXCL10) [36–38], suggesting that CTL activity may represent IFN pathway activation. From a prognostic point of view, however IFNG expression was the most likely to represent CTL activity (p<0.0001) followed by TLR4 (p = 0.039) than STAT1 (p = 0.09) (Fig 4G).

We further explored CTLG associated with good prognosis like CD8A and IFNG, because such genes may alternatively represent CTL activity in vivo. Among the CTLG with cS/E = 10 or beyond and high expression (40 or beyond) (Fig 5A), GBP5, PTPRC (CD45), and TBC1D10C (CARABIN) were identified (red bars), among which prognostic difference of only TBC1D10C was statistically significant (p = 0.026) (Fig 5C). TBC1D10C knockout mice showed accumulated CTL like IGFBP3 [39]. We then explored group of genes with low cS/E for CTL activation indicators (red letters in S3 Table), because CD8A is ascribed to the group (Fig 2D).

Among the low cS/E genes, CD8A expression was closely (R = 0.9 or beyond) associated with B2M, followed by FN1, ITGB1, STAT1, CCPG1, LCK, CXCL12, SMAD4, and IFNG as the multiple different probes (S3 Table), among which expression intensity of B2M was uniquely the highest (Fig 5D, left panel). Intriguingly, high B2M expression showed significantly better prognosis than low B2M expression in colon cancer (p = 0.0021, Fig 5D, right panel) similarly with CD8A, IFNG and TLR4 (Fig 4A and 4G).

The CD8 CTL is a subpopulation of tumor infiltrating T lymphocytes (TIL), which were commonly marked by CD3 (CD3G was used in our study, because it was the most abundant in the microarray). CD3 TIL-associated genes (CD3 TILG) were overlapped with 26 CAFG that included COL family (S5 Table), the most potent negative prognostic factors among the CAFG (designated as fibrosis-CAFG in Fig 1A). CD3 TILG were alternately overlapped with CAFG related to TAMCG definitely from fibrosis-CAFG (non-fibrosis-CAFG in Fig 1A). These findings suggested that CD3 TIL may have heterogenous subpopulations that are prognostically distinct with or without COL family association. Thus, we did not perform further subpopulation analysis of CD3G.

TAMC-associated genes (TAMCG) were partially overlapped with CAFG

In this study, we used CD14 as a myeloid cell marker according to the previous report [24]. High CD14 expression showed poorer prognosis than low CD14 expression in colon cancer as expectedly, because TAMC was demonstrated to be conditionally involved in cancer promotion [40]. However, the prognostic difference was not statistically significance (p = 0.11), suggesting that TAMC contribution to prognosis may be weaker like TAE than CAF and/or CTL (Fig 6A).

Fig 6 — **(a)** Survival curves for the TAMC marker *CD14* and the M2 macrophage marker *CD163* in colon cancer. **(b)** *CD14* and *CD163* expressions were closely associated in cancer str of the CRC tumors. **(c)** *CD33*, an iMDSC marker, and *ARG1*, a fMDSC marker, correlation with *CD14* are shown in cancer str of the CRC tumors. **(d)** *CAV1* expression was the most closely associated with *ARG1* in cancer str of the CRC tumors. **(e)** *ARG1* expression was tightly (R = 0.99) associated with expressions of inflammatory cytokines such as *IL1A* and *NRG1* including unique TAMC markers (*CCL1* and *CRP*) in cancer str of the CRC tumors. **(f)** Survival curves are shown for *ARG1* (upper panel) and *CD33* (lower panel) in colon cancer. **(g)** Survival curve is shown for *FoxP3* expression in colon cancer. **(h)** Survival curves are shown for *MS4A1*, a B TIL marker (upper panel) and *S100A9*, a TAN marker (lower panel) in colon cancer.

TAMCG were identified as 32 gene probes (27 SYMBOL genes) (S4 Table), and intriguingly, the 27 TAMCG were overlapped with 25 CAFG, excluding CXCR4 and CD163. CD163 is an alternate well-established M2 macrophage marker, and its significance of prognostic stratification was confirmed differently from CD14 (p = 0.027, Fig 6A). This finding may represent functional aspects of CD163 [41, 42] in contrast to CD14 [43], although their expression was closely associated with each other in the TME (R = 0.95, Fig 6B). Intriguingly, CD163 expression was much higher than CD14.

Recent scRNA analysis revealed that CXCR4 was expressed in tumor infiltrates including myeloid cells as well as T-cells and B-cells in the TME [26], suggesting that CD14 TAMC uniquely may be accompanied by lymphocytes marked by CXCR4. Consistent with this hypothesis, non-fibrosis CAFG were commonly characterized by association with CD3 TIL and TAMC markers (Fig 1A). Prognostic relevance was not overlapped between CD3 TIL alone, TAMC alone, and both-associated CAFG (Fig 1A), suggesting that CAFG may represent subpopulations with differential immune infiltrates.

ARG1 and CD33 putatively representing differential myeloid derived suppressor cells (MDSC) subpopulations unexpectedly exhibited good prognosis in colon cancer

Among the myeloid cells, MDSC inhibit tumor immunity by being mobilized from myeloid to peripheral tissues, and they can be marked by CD33 or arginase1 (ARG1), while all the cells marked by them did not strictly represent MDSC [40]. CD33 expression represented immature myeloid cells recruited from bone marrow, and showed more closely associated with CD14 (R = 0.86), than ARG1 (R = 0.76) did (Fig 6C). Moreover, expressions of both MDSC markers, especially ARG1, were much lower than CD14 (Fig 2C). These findings suggested that ARG1 macrophage may be small subpopulation among the TAMC.

ARG1 has been demonstrated to be a fMDSC marker, because nutritional use of L-arginin, and L-arginin depletion in the TME reduces nutrition of other immunological cells such as CTL [44]. ARG1-associated genes (ARG1G) putatively representing fMDSC were not overlapped with any CAFG totally differently from CD14 (Fig 1B). On the other hand, CAV1 expression was enriched as top priority (S8 Table), and was closely associated with ARG1 expression (Fig 6D). As a result, ARG1G were shared with TAEG representing tumor angiogenesis (Fig 4D). This finding is consistent with the hypothesis that angiogenesis is critical for mobilization of ARG1 macrophage from myeloid tissues into the tumor stroma.

ARG1 expression was the most strongly associated with IL1A, NRG1, CCL1, and CRP in cancer str of the CRC tumors (Fig 6E) among the numerous associated genes (13 401 genes, Fig 2B). CCL1 could be an aggressive TAMC marker as recently shown [45], and fluorescent double immunostaining revealed that CCL1 with myeloid markers of CD204 were colocalized in human cancer tissues [45]. Nevertheless, unexpectedly ARG1 expression did not exhibit negative prognostic relevance in colon cancer (Fig 6F, top panel).

CD33, an alternate MDSC marker, had molecular characteristics (CD33-associated genes = 1 753, expression amounts in NCC210 = 3.76, and cS/E = 5.2 as shown in S9 Table), which were rather close to CD14 than ARG1, and CD33-associated genes were shared with the most abundantly expressed CAFG, IGFBP7. On the other hand, CD33-associated genes expression was uniquely associated with angiogenic CAFG such as VIM, PLAT, and MSN, suggesting that CAFG-induced angiogenesis may be involved in mobilization of CD33 macrophage. CD33 was not significant negative prognostic factors, either, and rather a good prognostic factor (Fig 6F, lower panel), suggesting that MDSC mobilization may be insufficient to immunologically suppress tumors in the TME of clinical tumors.

T cell subpopulations (Treg), B-TIL, and TAN and prognosis in colon cancer

FoxP3 expression representing Treg was very low like ARG1 expression representing fMDSC (Fig 2C) and showed low cS/E = 1.9 (Fig 2D). Both markers had a large number of the related genes (18 281 and 13 401 genes) in contrast to other TME markers (Fig 2B), and were overlapped with very few CAFG with high cS/E (S8 and S10 Tables, and Fig 1B). ARG1G were partially shared with Treg-associated genes (TregG) marked by FoxP3, and interestingly, many common genes between them were ascribed to low cS/E group (green box, S8 Table and S1A Fig), which included both ATF4 and H3F3A as representatives. Both gene expressions were closely associated with ARG1 expression (S1B Fig) and FoxP3 expression (S1C Fig), respectively, in cancer str of the CRC tumors.

We identified B-TIL-associated genes as only 46 genes as close association with B-TIL marker, MS4S1 (S11 Table), all of which did not show high or middle cS/E. In our prognostic analysis, MS4S1 expression did not show negative prognostic relevance (Fig 6H, upper panel). TAN-associated genes were also identified as only 1 gene (DEFB1) as its close association with S100A9 (S12 Table). S100A9 expression showed negative prognostic trend, however the difference was not statistically different (Fig 6H, lower panel).

Discussion

Although contribution of the differential TME components to patient prognosis remains elusive, our current study proposed that CAF activation represented by COL8A1 in addition to CTL activation reflected by CD8A are critical determinants of prognosis in colon cancers. Endothelial marker, PECAM1, and myeloid marker, CD14, did not show such strong negative prognostic relevance, but their associated genes are partially shared with CAFG that were of prognostic importance (Fig 1A), and the shared TAEG and TAMCG may be involved in tumor aggressiveness controlled by CAFG.

scRNA analysis recently demonstrated that CAF subpopulations were composed of vascular CAF, matrix CAF, cycling CAF, and developmental CAF, and the most major component (~60%) was vascular CAF [24]. This finding suggested that CAF plays an important role in tumor angiogenesis, and the common genes between CAFG and TAEG included PLAT followed by ANXA1 and PTRF according to their prognostic importance (Fig 1A). Angiogenesis has been demonstrated to be promoted recently by PLAT [46] and classically by ANXA1 [47]. These findings suggested that angiogenesis itself is not a potent prognostic relevance, but overlapped features of CAFG and TAEG represent aggressiveness of CRC.

The TAEG well represented tumor angiogenesis. The established TAE markers (PECAM1, CDH5, and TIE1) were correlated each other, accompanied by close association with pericyte marker (RGS5) and BM markers (COL4A1/COL4A2). The TAEG included genes involved in unique proteolysis (ETS1 and PLAT) and immunity (IFNGR1 and PDL1) as well as caveolin formation (Fig 3E–3G). Caveolin has been well known to be required for angiogenesis [28, 29], while unique proteolysis by ETS1 was recently demonstrated to be involved in transcriptional regulation of tissue destructive genes [31] that might include PLAT. Interestingly, degradation of BM including type IV COL is rate-limiting step for cancer intravasation into blood in metastasis [48], and PLAT as well as PLAU may be the initial members of the protease cascade [49].

In our current study, CAFG always showed negative prognostic relevance, among which COL family genes were uniquely enriched as the most aggressive phenotypes (designated as fibrosis CAFG in this study). Previous reports suggested that CAF induce collagen fibers and fibrosis of the tissue, which hardens the ECM and contributes to malignancy, suggesting that Fibrosis-CAFG is a poor prognostic factor via the COL family [50]. Such fibrosis CAFG comprising of COL5A2, COL5A1, COL12A1, COL3A1, and COL1A1 have recently demonstrated that they have unique molecular mechanism to CAF activation, respectively [51–53]. Moreover, fibrosis CAFG may include SPARC and TAGLN in addition to COL family genes themselves (Fig 1A), because they have been demonstrated to be involved in COL family expression induction [54, 55].

The fibrosis CAFG were shared with CD3 TILG but not with TAMCG, indicating that cancer prognosis may be greatly affected by definite components of TME. From a prognostic point of view, association with CD3 TILG alone, TAMCG alone and both may have differential CAFG molecular features, hence exhibiting different tumor immunity. In our data, CXCR4 was included among the TAMCG, and scRNA assay recently clarified that CXCR4 is expressed in lymphocytes as well as myeloid cells [26], suggesting that CD14 TAMC uniquely may be accompanied by lymphocytes marked by CXCR4. Consistent with this hypothesis, non-fibrosis CAFG were commonly characterized by association with CD3 TIL and TAMC markers (Fig 1A).

Among the CTLG, positive prognostic factors similarly with CD8A were rather few, where we identified such genes as B2M (the highest expression amounts of 40 or beyond) followed by TLR4 and IFNG (all of which belonged to low cS/E group, Fig 5D). IFNG is a well-established CTL activation indicator [35], and TLR4 was demonstrated to be involved in IFN pathway activation, mediated by IRF3 and IRF7 [38]. Intriguingly IFNGR1 was included among the TAEG, indicating that CTL effects may be the most greatly demanded at angiogenic sites (Fig 3G).

B2M truncating mutations were recently discovered in melanoma, resulting in loss of surface expression of major histocompatibility complex (MHC Class I) [56], and loss of such MHC Class I-mediated antigen presentation frequently recognized in MMR-deficient colon cancer rendered these tumors resistant to CTL-mediated tumor immunity [57]. Intriguingly, γδ T cells are proved to be effectors of immunotherapy in cancers with HLA Class I defects [58]. Our data showed that B2M expression was strongly associated with CD8A expression in the multiple probes (0.9<R<0.93, Fig 5D) not in the tumor cells, but in cancer str of the CRC tumors. The association in our study was not necessarily accompanied by MHC Class I antigen expression, suggesting that B2M expression may be response against CTL in stromal cells. As tumor of host sensing of IFNG was redundant and tumors were controlled without direct T cell cytotoxicity, multiple cell type targeted by IFNG should be controlled for tumor equilibrium [59]. Thus, IFNG expression was more potent than CD8A expression itself as a prognostic factor.

In this study, we explored ARG1G marked by ARG1 and TregG marked by FoxP3. They are considered to be subpopulations of TAMC and CD3 TIL, respectively and the marker genes showed very small amounts of expression as compared of CD14 and CD3G expression (Fig 2C) with low cS/E ratio (Fig 2D), however such trace expressions had many related genes (Fig 2B). ACTG2 was commonly associated with both markers (Fig 1B), although there have been no reports describing relations between both markers and ACTG2. On the other hand, many common genes associated with both ARG1 and FoxP3 were identified in gene groups with low cS/E expression ratio (S1A Fig), among which ATF4 is of particular interest, because MDSC function was recently demonstrated to be regulated by ATF4 [60].

MDSC was demonstrated to promote cross-tolerance in cancer by expanding Treg [61], and immune tolerance to tumors is often associated with accumulation of MDSC and an increase in the number of Treg [62]. Consistent with this, both ARG1G and TregG included common genes (S1A Fig). Moreover, Treg marked by FoxP3 showed potent positive prognostic factor in colon cancer (p = 0.0046, Fig 6G), which recapitulated the previous report [63]. These findings suggested that Treg mobilization may be insufficient to immunologically suppress tumors in the TME of clinical tumors like MDSC, either.

Our findings clarified that that both CAFG and CTLG, but other components of the TME were dependent on either factor. Among them, CTLG may be a good marker to predict Immune checkpoint Inhibitors efficacy, while CAFG remains elusive to control, as previous reports have shown that CRC patients with CAF infiltration have a poor prognosis [64, 65]. Among CAFG, the COL family is a particularly poor prognostic factor, suggesting that it could be used as a prognostic marker.

Many common genes were identified in both CAFG and CD3 TILG, indicating a heterogeneous genetic subpopulation (Fig 1A). In previous reports, these common genes are associated with poor prognosis in colorectal cancer. For example, COL1A1 has been reported to be linked with immune infiltrating cells [66], and RAB31 is expressed in CAFs, contributing to the malignant potential of colorectal cancer through the secretion of HGF in the tumor stroma [67].

In the present analysis, the results show that CTL is more strongly related to prognosis than TAE. This is consistent with a previous report that showed a clinical prognostic benefit of immune checkpoint inhibitors over anti-VEGF antibodies [68, 69]. In addition, although no treatment to control CAF has been realized, there is a report showing that control of secretome of CAF has anti-tumor effects [70], which may contribute to the development of novel therapies in the future.

This study has limitations. The selection criteria, such as the stroma/epithelia ratio or R-index of other CAFG, may introduce bias. SPARC was used as a criterion for CAFG selection because SPARC has been identified as an important fibroblast marker in previous reports [22]. It is possible that the selection of CAFG may differ slightly if other markers are used as criteria. Future research should explore alternative criteria for a more comprehensive understanding.

In conclusion, our current in silico analysis of the micro-dissected stromal molecular signatures with prognostic relevance elucidated comprehensive interrelations among the TME components and provides deep insights of the beautiful molecular landscape of stromal biology.

Supporting information

S1 Fig. Genes commonly associated with ARG1 and FoxP3 in low S/E group.

(TIF)

pone.0299827.s001.tif^{(330KB, tif)}

S1 Table. List of CAFG.

(XLSX)

pone.0299827.s002.xlsx^{(1.3MB, xlsx)}

S2 Table. List of TAEG.

(XLSX)

pone.0299827.s003.xlsx^{(192.6KB, xlsx)}

S3 Table. List of CTLG.

(XLSX)

pone.0299827.s004.xlsx^{(170.4KB, xlsx)}

S4 Table. List of TAMCG.

(XLSX)

pone.0299827.s005.xlsx^{(331.7KB, xlsx)}

S5 Table. List of CD3 TIL-associated genes.

(XLSX)

pone.0299827.s006.xlsx^{(241.7KB, xlsx)}

S6 Table. List of genes in TAEG in order of highest correlation with PECAM1.

(XLSX)

pone.0299827.s007.xlsx^{(197.4KB, xlsx)}

S7 Table. List of genes in CTLG in order of highest correlation with CD8A.

(XLSX)

pone.0299827.s008.xlsx^{(175KB, xlsx)}

S8 Table. List of ARG1G.

(XLSX)

pone.0299827.s009.xlsx^{(2.7MB, xlsx)}

S9 Table. List of iMDSC-associated genes.

(XLSX)

pone.0299827.s010.xlsx^{(384.8KB, xlsx)}

S10 Table. List of TregG.

(XLSX)

pone.0299827.s011.xlsx^{(3.8MB, xlsx)}

S11 Table. List of BTIL-associated genes.

(XLSX)

pone.0299827.s012.xlsx^{(21.8KB, xlsx)}

S12 Table. List of TAN-associated genes.

(XLSX)

pone.0299827.s013.xlsx^{(23.8KB, xlsx)}

Data Availability

GSE35602: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE35602 GSE17538: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17538.

Funding Statement

The author(s) received no specific funding for this work.

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PONE-D-23-42477CAF-associated genes putatively representing distinct prognosis by in silico landscape of stromal components of colon cancer.PLOS ONE

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: The manuscript "CAF-associated genes putatively representing distinct prognosis by in silico landscape of stromal components of colon cancer" provides an in-depth analysis of the tumor microenvironment (TME) in colon cancer. It emphasizes the prognostic relevance of cancer-associated fibroblasts (CAF) and their associated genes, exploring their interactions with other components like tumor-associated endothelia, myeloid cells, and T lymphocytes. The study utilizes in silico analysis of micro-dissected stromal gene signatures, offering novel insights into the clinical features of the TME in colon cancer. This approach could significantly enhance the understanding of colon cancer progression and prognosis, potentially guiding more effective therapeutic strategies.

However, the paper should consider and discuss some additional details. Minor revisions based on the following points are recommended for acceptance:

1. How were the cancer-associated fibroblast genes (CAFGs) selected, and could the selection criteria bias the results?

2. How do different components of the TME, such as tumor-associated endothelia and myeloid cells, interact with CAFGs, and what implications does this have for colon cancer progression?

3. The manuscript mentions the subclassification of CAFGs based on fibrosis. How does this subclassification enhance the understanding of colon cancer prognosis?

4. Given the study's findings, what are the potential clinical applications, particularly in terms of targeted therapies or prognostic markers?

Reviewer #2: This study conducted an in silico analysis of stromal components in primary colon cancer, focusing on markers of cancer-associated fibroblasts and other tumor-associated elements. Authors found that CAF-associated genes generally have a negative impact on prognosis and overlap partially with genes associated with tumor-associated endothelia and myeloid cells, but not with cytotoxic T lymphocytes genes. The research highlights the prognostic significance of various stromal elements in the tumor microenvironment of colon cancer, offering new insights into its clinical features.

Comments

1. The statement "Cancer is a genetic disease" is inappropriate, being partially correct, but it requires some clarification. Cancer is indeed caused by genetic mutations, but these mutations can be either inherited (germline mutations) or acquired (somatic mutations).

2. Objectives or aims of in silico analysis of CRC tumors were not clearly stated, what’s lacking is the rational between the experiment and the identification of CAF-associated genes.

3. The resolution of data is not high enough to allow reviewers to acquire the information and make further evaluations. For example, the texts in all Figures are not readable.

4. For the presenting of the microarray-based database, the authors are encouraged to describe the big picture or broader view prior to listing specific examples. Other data or examples may also need to be discussed briefly.

5. The authors are suggested to provide more interpretation or explanations of their results and discuss the biological impact of their findings rather than simply displaying the number of genes being identified from their experiments.

6. Many genes have been identified and mentioned in this manuscript, however more detailed information regarding the known function of the top hits is lacking. To increase the biological significance of current study, the authors can briefly hypothesize potential involvement of top genes in CRC tumor pathogenesis or therapeutic applications.

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Reviewer #1: No

Reviewer #2: No

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Attachment

Submitted filename: Review for PO.docx

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PLoS One. 2024 Apr 1;19(4):e0299827. doi: 10.1371/journal.pone.0299827.r002

Author response to Decision Letter 0

5 Feb 2024

PONE-D-23-42477　　CAF-associated genes putatively representing distinct prognosis by in silico landscape of stromal components of colon cancer.

PLOS ONE

Thank you for your positive comments. I responded to your advice one by one, and revised our manuscript extensively for publication.

However, the paper should consider and discuss some additional details. Minor revisions based on the following points are recommended for acceptance:

Thank you for your positive comments.

1. How were the cancer-associated fibroblast genes (CAFGs) selected, and could the selection criteria bias the results?

[Response]

Thank you for your comment. CAFGs were selected as (1) stroma/epithelia ratio=10 or beyond in GSE35602 (microdissected CRC tumors), (2) R-index with SPARC in cancer stroma=0.9 or beyond, which were included in the original paper on page 4, line 105-109 in the revised paper. As we prioritized expression amounts, we compared the expression values of NCC210, which has the highest gene expression of the 13 cases of GSE35602. 76 genes with expression levels of 100 or beyond were further used for prognostic analysis, and 62 genes were proved to be poor prognostic factor. Such information was also included in the original paper on page 4, line 115-122. The selection of CAFG was based on SPARC among the CAFG in this study, however the results may be biased if other CAFGs were used. Therefore, such limitation was added on page 15-16, line 498-502 in the revised paper.

2. How do different components of the TME, such as tumor-associated endothelia and myeloid cells, interact with CAFGs, and what implications does this have for colon cancer progression?

[Response]

Thank you for your comment. Ben figures identified overlap between CAFG and other components of the TME as shown in Fig. 1b. Such genes may be considered to represent CAFG affecting angiogenic function like ANXA1 (CAF and TAE). In our data, their prognostic relevance was confirmed, suggesting that overlapped features of CAF and TAE represent more aggressiveness than TAE markers themselves in CRC. We have added this interpretation on page 13, line 418-420.

3. The manuscript mentions the subclassification of CAFGs based on fibrosis. How does this subclassification enhance the understanding of colon cancer prognosis?

[Response]

Thank you for your comment. Fibrosis representing COL family genes were enriched in CAFG with the highest AUC, so colon cancer with the highest COL family gene expression showed the most aggressive phenotypes, suggesting COL family gene is involved to assist cancer cells rather than protection from cancer cells. Previous reports have also supported our current data. We described such hypothesis on page 14, line 432-434.

4. Given the study's findings, what are the potential clinical applications, particularly in terms of targeted therapies or prognostic markers?

[Response]

Thank you for your comment. Our findings clarified that both CAFG and CTLG were the potent prognostic factors, while other components of the TME were dependent on either factor. Among them, CTLG may be a good marker to predict Immune checkpoint Inhibitor efficacy, while CAFG remains elusive to control. Among CAFG, the COL family is a particularly poor prognostic factor, suggesting that they have excellent potential as prognostic biomarkers. We added this description on page 15, line 481-486.

Thank you for your pertinent comments.

Comments

[Response]

Thank you for your pertinent comment. Cancer is a genetic disease, which was either inherited (germline mutations) or acquired (somatic mutations). We added this description on page 2, line 48-53.

2. Objectives or aims of in silico analysis of CRC tumors were not clearly stated, what’s lacking is the rational between the experiment and the identification of CAF-associated genes.

[Response]

Thank you for your comment. Objectives of in silico analysis of CRC tumors were to obtain comprehensive results from the same assessment, and public database can be accessed and obtained by other researchers. We included these objectives on page 3, line 69-70.

3. The resolution of data is not high enough to allow reviewers to acquire the information and make further evaluations. For example, the texts in all Figures are not readable.

[Response]

Thank you for your advice. We will immediately improve the quality/resolution of all the figures. Also, the resolution is poor in the integrated PDF file, but the resolution seems to be clear in the individual TIF files. We would appreciate your confirmation.

[Response]

Thank you for your suggestion. As you pointed out, some parts are difficult to comprehend, so we have added representative examples of each TME-associated genes as broader view on page 4-5, line 124-132, instead of only listing them.

[Response]

Thank you for your thoughtful feedback. We acknowledge the importance of providing a more in-depth interpretation of our results and discussing the biological implications of our findings. In response to your comments, we would like to add descriptions on page 15, lines 492-497, regarding the biological impact of our findings.

[Response]

Thank you for your comment. Some of the CAFG listed in Fig. 1a were discussed in the manuscript (PLAT and ANXA1: page13, line 415-420. COL5A2, COL5A1, COL12A1, COL3A1 and COL1A1: page14, line 434-436. SPARC and TAGLIN: page14, line 437-439). Additionally, we have provided details about certain genes (COL1A1 and RAB31) and their impacts on CRC, along with references and discussion on page 15, lines 487-491.

Attachment

Submitted filename: Response to Reviewers.docx

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PLoS One. doi: 10.1371/journal.pone.0299827.r003

Decision Letter 1

Chen Li

16 Feb 2024

CAF-associated genes putatively representing distinct prognosis by in silico landscape of stromal components of colon cancer.

PONE-D-23-42477R1

Dear Dr. Keishi Yamashita,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

Within one week, you’ll receive an e-mail detailing the required amendments. When these have been addressed, you’ll receive a formal acceptance letter and your manuscript will be scheduled for publication.

An invoice for payment will follow shortly after the formal acceptance. To ensure an efficient process, please log into Editorial Manager at http://www.editorialmanager.com/pone/, click the 'Update My Information' link at the top of the page, and double check that your user information is up-to-date. If you have any billing related questions, please contact our Author Billing department directly at authorbilling@plos.org.

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Kind regards,

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Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

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Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

Reviewer #1: Yes

Reviewer #2: Yes

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Reviewer #1: The authors well addressed my questions. I have no more questions and recommend to accept and publish.

Reviewer #2: This revision demonstrates a significant improvement; the authors have addressed all of my previous comments and concerns. I don’t have any further questions.

**********

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Reviewer #1: No

Reviewer #2: No

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PLoS One. doi: 10.1371/journal.pone.0299827.r004

Acceptance letter

Chen Li

20 Mar 2024

PONE-D-23-42477R1

PLOS ONE

Dear Dr. Yamashita,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

At this stage, our production department will prepare your paper for publication. This includes ensuring the following:

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If revisions are needed, the production department will contact you directly to resolve them. If no revisions are needed, you will receive an email when the publication date has been set. At this time, we do not offer pre-publication proofs to authors during production of the accepted work. Please keep in mind that we are working through a large volume of accepted articles, so please give us a few weeks to review your paper and let you know the next and final steps.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

S1 Fig. Genes commonly associated with ARG1 and FoxP3 in low S/E group.

(TIF)

pone.0299827.s001.tif^{(330KB, tif)}

S1 Table. List of CAFG.

(XLSX)

pone.0299827.s002.xlsx^{(1.3MB, xlsx)}

S2 Table. List of TAEG.

(XLSX)

pone.0299827.s003.xlsx^{(192.6KB, xlsx)}

S3 Table. List of CTLG.

(XLSX)

pone.0299827.s004.xlsx^{(170.4KB, xlsx)}

S4 Table. List of TAMCG.

(XLSX)

pone.0299827.s005.xlsx^{(331.7KB, xlsx)}

S5 Table. List of CD3 TIL-associated genes.

(XLSX)

pone.0299827.s006.xlsx^{(241.7KB, xlsx)}

S6 Table. List of genes in TAEG in order of highest correlation with PECAM1.

(XLSX)

pone.0299827.s007.xlsx^{(197.4KB, xlsx)}

S7 Table. List of genes in CTLG in order of highest correlation with CD8A.

(XLSX)

pone.0299827.s008.xlsx^{(175KB, xlsx)}

S8 Table. List of ARG1G.

(XLSX)

pone.0299827.s009.xlsx^{(2.7MB, xlsx)}

S9 Table. List of iMDSC-associated genes.

(XLSX)

pone.0299827.s010.xlsx^{(384.8KB, xlsx)}

S10 Table. List of TregG.

(XLSX)

pone.0299827.s011.xlsx^{(3.8MB, xlsx)}

S11 Table. List of BTIL-associated genes.

(XLSX)

pone.0299827.s012.xlsx^{(21.8KB, xlsx)}

S12 Table. List of TAN-associated genes.

(XLSX)

pone.0299827.s013.xlsx^{(23.8KB, xlsx)}

Attachment

Submitted filename: Review for PO.docx

pone.0299827.s014.docx^{(14.6KB, docx)}

Attachment

Submitted filename: Response to Reviewers.docx

pone.0299827.s015.docx^{(22.8KB, docx)}

Data Availability Statement

GSE35602: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE35602 GSE17538: https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE17538.

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PERMALINK

CAF-associated genes putatively representing distinct prognosis by in silico landscape of stromal components of colon cancer

Kota Okuno

Kyonosuke Ikemura

Riku Okamoto

Keiko Oki

Akiko Watanabe

Yu Kuroda

Mikiko Kidachi

Shiori Fujino

Yusuke Nie

Tadashi Higuchi

Motohiro Chuman

Marie Washio

Mikiko Sakuraya

Masahiro Niihara

Koshi Kumagai

Takafumi Sangai

Yusuke Kumamoto

Takeshi Naitoh

Naoki Hiki

Keishi Yamashita

Roles

Abstract

Introduction

Material and methods

Expression profiles of the microdissection tissues of the 13 CRC tumors (GSE35602)

Prognostic analysis from the bulk tissues of the 232 colon cancer tumors (GSE17538)

Statistical analysis

Ethics statement

Results

CAF-associated genes (CAFG) and other TME-associated genes in cancer str of the CRC tumors by in silico analysis

Fig 1. Relationship between CAFG and other TME-associated genes in cancer str of the CRC tumors (GSE35602).

Fig 2. TME markers and their associated group genes in cancer str of the CRC tumors (GSE35602).

CAFG are partially shared with TAE-associated genes (TAEG) representing tumor angiogenesis in colon cancer

Fig 3. TAE-associated genes (TAEG) in cancer str of the CRC tumors (GSE35602).

CAFG was prognostically independent of CTL-associated genes (CTLG) in colon cancer

Fig 4. Prognostic relevance of CTL marker, CD8A, in combination with CAFG.

Fig 5. CTL-associated genes (CTLG) in cancer str of the CRC tumors (GSE35602).

CTLG affecting good prognosis in colon cancer

TAMC-associated genes (TAMCG) were partially overlapped with CAFG

Fig 6. Tumor-associated myeloid cells (TAMC) and other TME markers in cancer str of the CRC tumors (GSE35602).

ARG1 and CD33 putatively representing differential myeloid derived suppressor cells (MDSC) subpopulations unexpectedly exhibited good prognosis in colon cancer

T cell subpopulations (Treg), B-TIL, and TAN and prognosis in colon cancer

Discussion

Supporting information

Data Availability

Funding Statement

References

Decision Letter 0

Chen Li

Roles

Author response to Decision Letter 0

Decision Letter 1

Chen Li

Roles

Acceptance letter

Chen Li

Roles

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

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