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Introduction

The understanding of cancer pathogenesis and therapeutic strategies has shifted from a cancer cell-centric model to recognizing the critical role of the tumor microenvironment (TME). In the early days, tumor research primarily focused on the tumor cells themselves, aiming to elucidate how genetic mutations and alterations in signaling pathways contributed to tumor progression and poor clinical prognosis (1). Recent studies have revealed that the TME plays a pivotal role in tumorigenesis, progression and therapeutic resistance (2,3). The TME constitutes a dynamic ecosystem co-evolving with malignant cells and host components, comprising both cellular and non-cellular elements. Cellular components mainly include stromal cells (fibroblasts, endothelial cells and pericytes) and immune cells [T cells, macrophages and natural killer (NK) cells]. Non-cellular components include inflammatory cytokines, growth factors, chemokines, metabolites and the extracellular matrix (ECM) (2). ECM remodeling mediated by stromal cells and matrix metalloproteinases (MMPs) provides essential structural support for tumor invasion and metastasis. On the other hand, immune cells exhibit dual regulatory roles; while capable of tumoricidal activity through cytotoxic mechanisms, they may also adopt tumor-promoting phenotypes that create an immunosuppressive microenvironment (4). The hallmarks of cancer include sustaining proliferative signaling, inducing angiogenesis, resisting cell death, triggering tumor cell invasion and metastasis, activating tumor-promoting inflammation and evading immune surveillance. Acquisition and maintenance of these features depend not only on cancer cells but also on dynamic interactions with the TME (5). The interplay between tumor cells and the TME greatly affects tumor progression and clinical outcomes. These inter-cellular communications are driven by multiple coordinated pathways and complex protein networks, including cytokines, chemokines, growth factors and matrix-degrading enzymes, which collectively promote tumor cell proliferation, invasion and survival (3). Accurately identifying these dynamic interactions has represented a critical challenge in oncological research.

Antibody arrays, as one of the major affinity-based platforms, enable the parallel detection of hundreds to thousands of proteins. This powerful technology can uncover expression patterns of key factors across individual or multiple cell populations. Particularly suited for exploring overall secreted profiles in tumor tissues, it effectively analyzes protein expression signatures across different models (6). By mapping these dynamic molecular signals, antibody arrays reveal complex communication networks among diverse cellular components within tumor microenvironments, offering new insights into cell-to-cell interactions and coordinated regulatory mechanisms. The present review focused on current findings on tumor-TME crosstalk, identifying key secreted factors that promote malignancy and therapy resistance across different tumor types and demonstrating how high-throughput antibody arrays enable systematic profiling of TME signaling pathways.

Components and communication networks in TME

Cellular components of the TME

The term TME specifically refers to the microenvironment of solid tumors, which comprises not only malignant cells but also a large population of immune and stromal cells, along with non-cellular components (Fig. 1). The main cellular components include fibroblasts, endothelial cells, adipocytes, innate immune cells and adaptive immune cells. These cells collectively modulate the local TME through dynamic interactions, exhibiting dual roles that either promote or antagonize tumor progression.

Figure 1 Overview of the (TME) structure. The complex TME is composed of various cellular and non-cellular components. Tumor cells, stromal cells, and immune cells interact through direct contact or secreted factors, collectively influencing tumor progression. TME, tumor microenvironment; CAF, cancer-associated fibroblast; DC, dendritic cell; ECM, extracellular matrix; NK cell, natural killer cell; Treg, regulatory T cell.

Cancer-associated fibroblasts (CAFs), the most abundant stromal cell population within the TME, play pivotal roles in cancer progression. Their identified functions include ECM remodeling to facilitate tumor invasion (7), promotion of cancer cell stemness (8), enhancement of chemoresistance to targeted therapies (9) and reprogramming of the immune environment within tumors (10,11). Studies have revealed that CAFs constitute a heterogeneous population originating from diverse precursor cells through either local differentiation or recruitment to tumor sites (2,12,13). While activation of local tissue-resident fibroblasts and stellate cells is recognized as the primary source of CAFs (12), alternative origins include adipocytes, bone marrow-derived mesenchymal stem cells, epithelial cells undergoing epithelial-mesenchymal transition (EMT) and endothelial cells undergoing endothelial-mesenchymal transition (13,14). In malignant tumors, neoplastic cells drive the transformation of normal fibroblasts into CAFs by activating inflammatory pathways through secretion of cytokines, growth factors and functional DNAs or non-coding RNAs (15,16). Furthermore, other non-malignant cells in the TME can induce CAF conversion, as evidenced by findings showing that M2-polarized macrophages promote the transformation of intra-tumoral normal fibroblasts into CAFs via paracrine signaling pathways (17). The heterogeneity of CAFs markedly influences subsequent tumor progression. Studies have identified specific CAF subtypes associated with characteristics such as tumorigenesis, chemotherapy resistance and immunosuppression (11,18,19). However, the extent of CAF heterogeneity and the potential tumor-modulating effects exerted by distinct CAF subtypes remain under investigation.

Tumor-associated macrophages (TAMs) constitute nearly half of the cellular components within solid tumors, playing pivotal roles in tumor progression (20). These cells, which are derived from peripheral blood mononuclear cells, differentiate into macrophages when stimulated by various factors secreted by tumor and stromal cells, such as chemokines, cytokines and growth factors (21). Within the TME, TAMs are markedly involved in tumor proliferation, invasion, metastasis and angiogenesis, with TAM-derived cytokines acting as key regulators in these processes (22,23). TAMs are traditionally classified into M1 and M2 subtypes, with M1-like macrophages involved in pathogen clearance, inflammatory response and anti-tumor immune functions (24), while M2-polarized macrophages exhibit anti-inflammatory properties and promote tumor cell proliferation, metastasis and immune evasion (25,26). Recent evidence suggests that TAM phenotypic diversity in vivo exceeds this binary classification due to tumor heterogeneity (27). As a highly diverse immune cell population with various phenotypes and functions, TAMs undergo differentiation influenced by multiple factors, leading to heterogeneous pro-tumorigenic capabilities. For instance, Wang et al (28) demonstrated that hepatocellular carcinoma-derived C-C motif chemokine ligand 2 (CCL2) and CCL5 attract TAMs and induce their polarization towards pro-tumorigenic M2-like macrophages. Similarly, Su et al (29) found that breast cancer cells activate macrophages to a TAM-like phenotype by secreting granulocyte-macrophage colony-stimulating factor (GM-CSF). In prostate cancer, CAFs recruit and activate monocytes through C-X-C motif chemokine ligand 12 (CXCL12) and CXCL14 chemokines to generate M2-polarized macrophages (30,31). This heterogeneity leads to complex regulatory interactions between TAMs and tumor tissues.

As well as macrophages, other immune cells also play critical roles in tumor progression or anti-tumor surveillance. Cancer patients exhibit diminished immune surveillance capacity and immune dysregulation, including imbalances in CD4+ T cells, CD8+ T cells and associated cytokines (32,33). Thymus-derived naive CD4+ T cells differentiate into distinct subsets upon antigenic stimulation in the periphery: T-helper 1 cells (Th1) are characterized by secretion of interferon-γ (IFN-γ), while Th2 is defined by interleukin-4 (IL-4) production (34). Another subset comprises regulatory T cells (Tregs), which play a pivotal role in attenuating anti-tumor immune responses (35). Tregs express the transcription factor Foxp3 and surface markers CD25/CD127, suppressing the function of effector T cells and antigen-presenting cells through direct cell-cell contact and secretion of inhibitory cytokines like transforming growth factor-β (TGF-β) and IL-10 (35,36). Myeloid-derived suppressor cells (MDSCs), originating from aberrant myeloid differentiation of hematopoietic stem cells, exhibit immunosuppressive properties (37,38). Accumulating evidence highlights MDSCs as central components of the malignant TME, critically driving tumor progression and chemoresistance through secretion of inflammatory factors and chemokines such as IL-6 and CXCL family members (39,40). Neutrophils, the most abundant leukocytes in human blood, represent a significant proportion of the TME in multiple malignancies (41,42). These cells demonstrate functional plasticity and are recruited to tumor tissues via specific cytokines and chemokines during solid tumor progression, where they adopt context-dependent roles (42,43). Neutrophils can be classified into anti-tumor N1 and pro-tumor N2 subtypes (44). A subset of neutrophils may exert anti-cancer effects through reactive oxygen species (ROS) and neutrophil elastase release (45,46); however, the majority are polarized into pro-tumorigenic phenotypes by factors within the TME (47). N2 neutrophils promote tumor cell proliferation, angiogenesis and metastasis via secretion of pro-tumorigenic molecules, while also fostering an immunosuppressive TME through interactions with macrophages, NK cells and T cells (43,48,49).

Signal transduction mediated by cytokines

Cytokines are a class of secreted proteins that mediate intercellular communication. Broadly defined, cytokines encompass a diverse group of secreted molecules including chemokines, growth factors, angiogenic factors, soluble receptors and extracellular proteases (50). These proteins not only exhibit cancer-suppressive functions but also participate in regulating physiological processes closely associated with tumor initiation, progression and metastasis, such as inflammation, apoptosis, migration and angiogenesis (2,4).

Multiple factors directly regulate tumor progression by modulating tumor cell proliferation and intra-tumoral vascular formation. For instance, vascular endothelial growth factors (VEGF) and its downstream signaling pathways are overexpressed in most malignancies, demonstrating dual functions in promoting angiogenesis and enhancing vascular permeability through specific induction of endothelial cell division, proliferation and migration (51,52). Similarly, insulin-like growth factor-1 (IGF-1) binds to its receptor IGF-1R to activate PI3K/AKT and MEK/ERK signaling pathways, thereby regulating tumor cell proliferation, invasion and metastasis (53). Notably, IGF-1R has been found widely expressed across various cell types in the TME, including epithelial cancer cells, CAFs and myeloid cells (54). TGF-β, a pleiotropic growth factor, exhibits dual functionality by suppressing tumor cell growth while promoting cancer progression through EMT induction under specific conditions, conferring stem-like properties to cancer cells (55). Additionally, aberrant expression of factors such as fibroblast growth factor (FGF), epidermal growth factor (EGF) and hepatocyte growth factor (HGF) markedly promotes tumor and stromal cell proliferation along with angiogenesis (56,57).

Furthermore, cellular components within the TME secrete inflammatory cytokines and chemokines that exert complex effects through autocrine and paracrine mechanisms. IL-6 demonstrates pleiotropic pro-inflammatory functions, promoting B and T cell differentiation while directly stimulating tumor cell proliferation and chemoresistance (58,59). The pro-inflammatory cytokine IL-8 enhances tumor cell proliferation, survival and exhibits potent pro-angiogenic activity, while also participating in the recruitment of lymphocytes, monocytes and neutrophils (59). Conversely, the anti-inflammatory cytokine IL-10 protects cancer cells from immune attack through potent immunosuppressive mechanisms (40,60).

Extensive research has focused on soluble factor-mediated molecular interactions between tumor cells and their microenvironment, collectively revealing the tip of the iceberg in intra-tumoral regulatory networks (3,61,62). Although the current understanding of the involvement of cytokine networks in TME communication remains incomplete, cytokines represent valuable therapeutic targets and biomarkers. This necessitates a deeper exploration of their signal transduction and regulatory functions in the TME.

Antibody arrays for secretome studies

As major components of the cellular secretome, cytokines play pivotal roles in both physiological conditions and disease pathogenesis. Their dynamic balance reflects the pathological progression of cancer and reveals key disease mechanisms. Mass spectrometry (MS) technology analyzes proteins through the mass-to-charge ratio of ionized fragments and has developed into a routine detection tool for proteome research (63,64). However, MS still faces technical challenges in secretome analysis due to limitations such as the requirement for complex sample preprocessing (including protein enrichment and separation), insufficient sensitivity for detecting low-abundance proteins (such as inflammatory factors and growth factors) and poor reproducibility (65).

Affinity-based proteomic approaches offer superior sensitivity and specificity for detecting low-abundance proteins, allowing high-throughput analysis of multiple targets with minimal sample input. Critically, these techniques eliminate the need for complex sample preprocessing and have broad dynamic ranges, enabling the simultaneous detection of proteins with high and low abundance without extensive sample preparation (66). This makes affinity-based methods particularly well suited for analyzing clinical samples.

Antibody arrays are a type of protein microarray and represent a cornerstone technology in affinity-based proteomics. Derived from DNA microarrays, protein microarrays aim to detect proteins with high-throughput and sensitivity. In these systems, proteins such as antibodies, are immobilized on solid-phase carriers such as glass or nitrocellulose membranes to capture targets in samples (66,67). This technology is primarily categorized into forward-phase protein arrays (FPPA) and reverse-phase protein arrays (RPPA) based on the type of coated proteins (Fig. 2) (68). The antibody array is the most common form of FPPA, in which specific antibodies are spotted onto a solid surface to detect protein concentrations in liquid samples. There are two main types of antibody arrays: Labeled-based arrays and sandwich-based arrays, each with distinct advantages and limitations.

Figure 2 Overview of main protein array platforms. Key features of two types of antibody arrays (left: sandwich-based; middle: label-based) alongside a reverse-phase protein array (right).

Sandwich-based antibody arrays employ pairs of antibodies for target detection: a capture antibody immobilized on the substrate and a detection antibody (often biotinylated) coupled to a signal reporter (such as a fluorescent label), allowing semi-quantitative or quantitative analysis (66,69). Due to the predefined antibody layout, fluorescence signals can be precisely mapped to specific target proteins. This dual-antibody setup, similar to other immunoassays, ensures high specificity and sensitivity. In contrast, label-based platforms pre-label sample proteins (for instance with biotin or fluorescent dyes), eliminating the need for detection antibodies. This bypasses challenges related to antibody pair availability and cross-reactivity, enabling the development of high-density arrays capable of detecting thousands of proteins simultaneously (70). Additionally, label-based arrays can be combined with specific antibodies or chemical reagents to detect post-translational protein modifications.

In RPPA systems, instead of immobilizing antibodies, protein samples themselves are directly spotted onto array surfaces. This sample-centric format allows analysis of a large number of samples in parallel on a single array. In cancer research, RPPAs have been widely used to measure protein expression and signaling pathway activation in tumor tissues and cells. They are also applied to detect autoantibodies against tumor-associated antigens in patient sera by immobilizing the relevant antigens on the array. However, RPPAs typically require experiment-specific customization, which limits their general applicability.

Recent findings on TME using antibody arrays

The TME is highly heterogeneous, highlighting the complexity of the secreted protein signaling network within it, which necessitates a more comprehensive and systematic analysis to deconstruct. High-density antibody arrays have been successfully applied to secretome analysis across diverse sample types, including cell culture supernatants, biofluids and tissue lysates. These platforms reveal dysregulated protein patterns critical for interpreting the mechanisms underlying major diseases. In cancer research, precise detection of key secreted signaling proteins has advanced our understanding of the interactions between the TME and tumor cells and has enabled the discovery of novel therapeutic targets, demonstrating significant value from both research and clinical perspectives. In this section, we discuss key recent discoveries on the crosstalk between tumor cells and the TME utilizing antibody array technology, categorized into three major themes (Tables I and SI).

Table I Summary of findings in tumor microenvironment research using antibody arrays.

Table I

Summary of findings in tumor microenvironment research using antibody arrays.

First author/s, year	Tumor type	Non-tumor cells	Array results	(Refs.)
Chen, 2014	NSCLC	CAFs	IGF-2, HGF, and CXCL12 were identified as CAF-secreted factors.	(8)
Bellei, 2021	NMSC	CAFs	Upregulation of IL-6, IL-8, and other pro-inflammatory cytokines secretion in Onco-P20-treated CAFs was shown.	(9)
Hu, 2021	NSCLC	CAFs	Elevated differential expression of HGF and FGF-7 in three CAF subtypes was revealed.	(11)
Su and Liu, 2014	Breast cancer	TAMs	High GM-CSF secretion in breast cancer cell secretome was shown.	(29)
Zhou et al, 2024	Breast cancer	MDSCs	CXCL8 was identified as the most dysregulated chemokine in C-E-cad-knockdown cells.	(40)
Wei et al, 2023	CSCC	CAFs	PAI-1 was identified as the most upregulated secretory cytokine in CAFs compared with normal fibroblasts.	(71)
Wei & Cheng, 2021	CSCC	CAFs	Periostin, RGMB, Trappin-2, and PD-L1 were indicated as upregulated in CAFs.	(72)
Sun, 2020	Ovarian cancer	Omental adipocytes	CCL2 was identified as a key adipocyte-secreted factor.	(73)
Ryan, 2019	TNBC	CAFs, TAMs	Upregulated CCL2 secretion in geminin-overexpressing HMEs CM was identified.	(74)
Han, 2022	Cervical cancer	Hypoxic fibroblasts	Upregulated LTBR secretion in hypoxia fibroblasts was revealed.	(75)
Huang et al, 2024	HCC	TAMs	CCL7 was identified as the key cytokine reduced after TREM1 knockdown in TAMs.	(76)
Zheng & Wang, 2023	Breast cancer	TAMs	CXCL1 was identified as the most upregulated chemokine in TAMs under mild psychological stress.	(77)
Tatsuno, 2024	Osteosarcoma	TAMs	IL-8 levels were indicated to markedly increase in co-culture CM compared with osteosarcoma cells or macrophage monocultures.	(78)
Kim, 2024	CRC	TAMs	Upregulated secretion of IL-6, TNF-α, and CXCL1 in M2-like TAMs was detected.	(79)
Licarete, 2019	Melanoma	TAMs	Reduced production of angiogenic proteins after PLP treatment was confirmed.	(80)
Wang et al, 2019	Breast cancer	Neutrophils	Elevated TIMP-1, GM-CSF, and angiogenin in tumor-infiltrating neutrophils compared with peripheral neutrophils were identified.	(81)
Lee & Chen, 2023	HNSCC	Eosinophils	Upregulated levels of CCL2, IL-1β, IL-12, and other cytokines in activated eosinophils were identified.	(82)
Benzing, 2019	PDAC	THP-1 monocyte-like cells	TIMP-1 and TIMP-2 in THP-1 CM were detected.	(83)
Li et al, 2024	LUAD	Multiple immune cells	TGF-β1 was identified as the most upregulated cytokine in UPP-1 highly expressed tumor cells.	(84)
Zhu et al, 2019	HCC	Multiple immune cells	TGF-β3, DcR3, and TGF-βRI were detected as upregulated in HCC tissues.	(85)
Xie et al, 2018	Breast cancer, colon cancer	Multiple immune cells	Increased immune-activating and immuno-suppressive cytokines in AngII signaling blockage tumor cells were revealed.	(86)
Jiang et al, 2022	Osteosarcoma	Multiple immune cells	Increased secretion of CCL5, CXCL9, and CXCL10 in c-Myc-inhibited K7M2 cell line was demonstrated.	(87)
Wang and Wang, 2020	Ovarian cancer	CD8+ T cells	CXCL16 and CX3CL1 were confirmed as upregulated in CM of STK3-overexpressing ovarian cancer cells.	(88)
Zhang and Cascio, 2024	HGSOC	MSCs	Elevated CXCL1, CXCL5, CCL7, and MIP-3 in macrophages treated with CM from MSC and tumor cell co-cultures were identified.	(89)
Sheng, 2024	PDAC	CAFs	Reduced IL-6, IL-8, and CXCL12 in TAK1-knockdown CAFs were identified.	(90)
Nakamura, 2021	Melanoma	CAFs	RAS-mediated CCL5 production in fibroblasts was identified.	(91)
SenGupta, 2021	TNBC	Neutrophils	Elevated CXCL1, CXCL2, CXCL3, and TGF-β levels in TNBC-derived CM compared with ER+ cells-derived CM were detected.	(92)
Ogawa and Yamamoto, 2019	CRC	Neutrophils	Elevated CXCL1/8 in SMAD4− SW837 cells and increased CXCL8 in neutrophils exposed to SMAD4− cell CM were identified.	(93)
Germann, 2020	CRC	Neutrophils	MMP-9 and RELMB were identified as differentially secreted in T cell-neutrophil co-cultures.	(94)
Chan, 2023	PAAD	Neutrophils	Upregulated CXCL2, CTACK, and P-Selectin in melatonin-treated tumor cell-derived CM were found.	(95)
Su et al, 2018	Breast cancer	CAFs	Elevated IL-6/IL-8 secretion in CD10+GPR77+ CAFs was shown.	(96)
Zeng et al, 2023	Breast cancer	TAMs, CAFs	CCL18, IL-6, and IL-8 were found upregulated in in-vitro-cultured chemoresistant tumor tissues.	(97)
Papaccio et al, 2021	Melanoma	CAFs	Elevated IL-6, IL-8, IL-7, CCL11, and TIMP-2 in CAFs compared with normal fibroblasts were indicated.	(98)
Zhou, 2016	Ovarian cancer	CAFs	A spectrum of secreted proteins derived from cisplatin-treated CAFs including CCL5 were detected.	(99)
Che, 2018	ESCC	CAFs	Increased PAI-1 secretion by cisplatin-pretreated CAFs was found.	(100)
Chrisochoidou, 2023	Mesothelioma	CAFs	Upregulated cytokines (e.g., IL-6, CXCL12) and growth factors (e.g., PDGFs, FGFs) in activated fibroblasts were identified.	(101)
Wang et al, 2021	ccRCC	TAMs	CCL5 was identified as a key factor in the SOX17-modulated interaction between ccRCC cells and TAMs.	(102)
Liu et al, 2021	CRC	TAMs	GM-CSF, G-CSF, and IL-7 secretion upregulated in KRAS mutant cells were identified.	(103)
Lo Re and Mazza, 2020	HCC	Tregs	Downregulation of 94 cytokines (including IL-6 and IL-8) in macroH2A1-knockdown cell secretome was detected.	(104)
Guo et al, 2023	Breast cancer	CAFs	IL-6, FGF-9, MIF, and TNFRSF11 were identified as highly secreted factors by CAFs.	(105)
Chu, 2014	Cervical cancer	CAFs	High levels of growth factors (e.g., IGF-2, EGF, and GM-CSF) secreted by CAFs, and PDGF-AA and PDGF-BB secreted by HeLa cells were revealed.	(106)
Cao and Yin, 2021	HCC	Non-parenchymal liver cells	Upregulated MMP-8 in irradiated NPCs was identified.	(107)
Arshad, 2015	Lung carcinoma	Fibroblasts	Upregulated IL-6, bFGF, CXCL16, sTNF-R1, MMP2/3, and Pro-MMP9 in TC-1 secretome when co-cultured with fibroblasts were identified	(108)

[i] AngII, angiotensin II; CAF, cancer-associated fibroblast; CCL, C-C motif chemokine ligand; ccRCC, clear cell renal cell carcinoma; CM, conditioned medium; CRC, colorectal cancer; CSCC, cutaneous squamous cell carcinoma; CX3CL1, C-X3-C motif chemokine ligand 1; CXCL, C-X-C motif chemokine ligand; DcR3, decoy receptor 3; EGF, epidermal growth factor; ER, estrogen receptor; ESCC, esophageal squamous cell carcinoma; FGF, fibroblast growth factor; G-CSF, granulocyte colony-stimulating factor; GM-CSF, granulocyte-macrophage colony-stimulating factor; GPR77, G-protein-coupled receptor 77; HCC, hepatocellular carcinoma; HGF, hepatocyte growth factor; HGSOC, high-grade serous ovarian cancer; HMEs, human mammary epithelial cells; HNSCC, head and neck squamous cell carcinoma; IGF-2, insulin-like growth factor 2; IL, interleukin; LTBR, lymphotoxin-beta receptor; LUAD, lung adenocarcinoma; MDSCs, myeloid-derived suppressor cells; MIF, macrophage migration inhibitory factor; MIP-3, macrophage inflammatory protein-3; MMP, matrix metalloproteinase; MSCs, mesenchymal stem cells; NSCLC, non-small cell lung cancer; NMSC, non-melanoma skin cancer; PAAD, pancreatic adenocarcinoma; PAI-1, plasminogen activator inhibitor-1; PDAC, pancreatic ductal adenocarcinoma; PDGF, platelet-derived growth factor; PD-L1, programmed Death-Ligand 1; PLP, peptide-loaded particles; RGMB, repulsive guidance molecule B; RELMB, resistin-like molecule beta; SOX17, SRY-box transcription factor 17; sTNF-R1, soluble tumor necrosis factor receptor 1; TAK1, TGF-beta-activated kinase 1; TAMs, tumor-associated macrophages; TGF-β, transforming growth factor-beta; TGFβRI, transforming growth factor-beta receptor I; TIMP, tissue inhibitor of metalloproteinases; TNBC, triple-negative breast cancer; TNF-α, tumor necrosis factor-alpha; TNFRSF11, tumor necrosis factor receptor superfamily member 11; Tregs, regulatory T cells; TREM1, triggering receptor expressed on myeloid cells 1; UPP-1, uridine phosphorylase-1.

TME on tumor progression and metastasis

The TME is a dynamic ecosystem where stromal and immune cells interact with cancer cells through a complex network of secreted proteins, such as inflammatory mediators, chemokines and growth factors, to drive the development of tumorigenic phenotypes. Studies have made breakthroughs in exploring the molecular mechanisms underlying this process.

In a series of studies on cervical squamous cell carcinoma, Wei et al (71) investigated the tumor-promoting mechanisms of CAFs through two distinct signaling axes. First, CAF-derived plasminogen activator inhibitor-1 (PAI-1) was identified as promoting lymphatic metastasis by triggering EndoMT in lymphatic endothelial cells (LECs). This process was mediated through LDL receptor-related protein 1 (LRP1)-dependent activation of the AKT/ERK signaling pathway, ultimately facilitating tumor dissemination. This identifies the PAI-1/LRP1 axis as a potential therapeutic target for inhibiting metastasis. Parallel to this discovery, the team further identified periostin+ CAFs as another critical mediator of lymphatic metastasis. Periostin secreted by these CAFs activated the integrin-FAK/Src signaling cascade in LECs, leading to phosphorylation and subsequent degradation of VE-cadherin. This disrupted LEC barrier integrity, facilitating tumor cell intravasation and dissemination (72). In addition to fibroblasts, Sun et al (73) demonstrated how omental adipocytes facilitate tumor peritoneal metastasis in ovarian cancer in a murine model. Adipocyte-secreted CCL2 bound to CCR2 on cancer cells, activating the PI3K/AKT/mTOR pathway, which upregulated hypoxia inducible factor-1α (HIF-1α) and VEGF-A secretion. This stimulated intra-tumor angiogenesis and tumor metastasis to the omentum. Blocking the CCL2/CCR2 axis represents a potential therapeutic strategy to prevent peritoneal metastasis in ovarian cancer. A complex bi-directional crosstalk between geminin-overexpressing (GemOE) triple-negative breast cancer (TNBC) cells and stromal cells was investigated as a driver of metastasis. GemOE-cells secreted acetylated HMGB1, which activated receptor for advanced glycation end-products (RAGE) and CXCR4 expression on mesenchymal stem cells (MSCs) and recruited them into tumors. These MSCs differentiated into CAFs and secreted S100A4, which reciprocally stimulated GemOE-cells to release CCL2, attracting and polarizing macrophages into M2-TAMs. Gas6 secreted by the TAMs combined with AXL on GemOE cells and synergized with RAGE signaling to amplify cancer stemness, EMT and CD151/α3β1-integrin-mediated interactions, enhancing invasiveness (74). While CAFs typically promote tumor growth, Han et al (75) revealed that hypoxic fibroblasts demonstrated opposing effects. Conditioned medium (CM) from hypoxic dermal fibroblasts (H-CM) suppressed cervical cancer cell (HeLa) viability by enhancing apoptosis via caspase-3/7 activation, mitochondrial dysfunction and G0/G1 arrest. Proteomics analysis identified lymphotoxin-beta receptor (LTBR), a member of the TNF receptor family, as a key factor upregulated in H-CM, which suppressed HeLa cell proliferation. These findings suggest microenvironment-dependent fibroblast plasticity in regulating malignant progression (75).

TAMs are pivotal drivers of tumor progression, primarily through their secretion of cytokines and chemokines that promote immunosuppressive microenvironments and metastatic cascades. Huang et al (76) demonstrated that TAMs expressing high levels of triggering receptor expressed on myeloid cells 1 (TREM1) are key contributors to EMT and metastasis in hepatocellular carcinoma (HCC). The authors identified CCL7 as a key downstream effector secreted by TREM1+ TAMs that drives these effects. These findings suggest TREM1 or CCL7 as potential targets to disrupt EMT and metastasis in HCC. Furthermore, TREM1 expression positively associated with elevated levels of programmed death ligand 1 (PD-L1) and cytotoxic T lymphocyte associated protein 4 (CTLA-4), linking CCL7-driven EMT to immunosuppressive TME remodeling (76). In breast cancer models, Zheng et al (77) demonstrated that chronic unpredictable mild psychological stress activates glucocorticoid receptor (GR) signaling in TAMs, leading to increased secretion of CXCL1. This chemokine facilitated MDSC recruitment via CXCR2, enhancing their immunosuppressive capacity to inhibit CD8+ T cell cytotoxicity and promote pre-metastatic niche formation. Therefore, targeting GR signaling in TAMs or the CXCL1-CXCR2 axis represents a potential therapeutic strategy to counteract stress-induced metastasis and immunosuppression in breast cancer. In osteosarcoma, a group of Iba1+/CD163+ TAMs were found to enhance tumor progression and lung metastasis. Using antibody arrays, the authors confirmed the CM of tumor cells and TAMs co-cultures enriched with IL-8, which markedly enhanced osteosarcoma cell proliferation, migration and invasion through the FAK pathway (78). Macrophage polarization and cytokine release are influenced by bi-directional crosstalk with tumor-derived factors, which further enhance tumor progression. Kim et al (79) highlighted that colorectal cancer (CRC)-derived CD133+ microvesicles (MVs) differentiate macrophages into an M2-like phenotype within the TME. CD133+ MVs triggered IL-6 secretion from TAMs, which subsequently activated the STAT3 pathway in CRC cells, thereby enhancing their EMT and invasion. IL-6/STAT3 activation further established a feedback loop, reinforcing CD133 expression in cancer cells and establishing an immunosuppressive TME conducive to CRC progression. Targeting TAMs is a promising strategy to enhance anti-tumor effects. As demonstrated by Licarete et al (80), prednisolone phosphate (PLP) enhances doxorubicin (DOX) efficacy against B16.F10 melanoma cells by targeting TAMs. PLP suppresses TAM-mediated angiogenesis by downregulating pro-angiogenic factors (FGF-2, VEGF, G-CSF, IL-1β and TNF-α), thereby disrupting the TME supporting melanoma growth. Combined PLP with DOX synergistically inhibits melanoma proliferation and induces apoptosis, linked to reduced oxidative stress and potentiated anti-angiogenic effects. However, the M2 immunosuppressive phenotype and IL-10 and arginase-1 (Arg-1) secretion remains unaffected, highlighting TAM angiogenic pathways as the primary mechanism.

Other immune cells within the TME also play a role in regulating tumor progression through distinct secretory programs. In TNBC, tumor-infiltrating neutrophils are engaged in a self-reinforcing loop with cancer cells via tissue inhibitor of metalloproteinase-1 (TIMP-1) and CD90 interactions (81). Antibody array screening demonstrated that these neutrophils secrete elevated TIMP-1, which drove twist-mediated EMT in tumor cells. Reciprocally, EMT-transformed CD90+ tumor cells enhanced TIMP-1 production in neutrophils through CD90-Mac-1 interactions, amplifying metastatic potential of the tumor (81). Lee et al (82) demonstrated that eosinophils drove metastasis through cytokine-mediated immunosuppression and vascular remodeling in head and neck squamous cell carcinoma (HNSCC). Tumor-associated tissue eosinophilia (TATE) was found to be associated with aggressive tumor features such as angiogenesis and lymph node metastasis in HNSCC, with TATE-rich tumors exhibiting increased CD4+Foxp3+ Tregs, exhausted CD8+PD1+ T cells and reduced cytotoxic lymphocytes, collectively fostering an immunosuppressive niche that accelerates tumor progression. CCL2 was identified as a key eosinophil-derived factor promoting tumor cell migration and EMT (82). While typically associated with immunosuppression, Benzing et al (83) reported that undifferentiated monocyte-like suppresses pancreatic ductal adenocarcinoma (PDAC) invasiveness. Co-culturing PDAC cells with monocyte-like THP-1 markedly suppressed invadopodia formation and matrix degradation. Proteomic analysis of THP-1 CM identified high levels of TIMP-2, which selectively inhibits MT1-MMP (MMP-14), an enzyme critical for invadopodia function.

Immune microenvironment remodeling through secreted proteins

The balance between tumor-promoting and tumor-suppressing immune responses is critical for tumor survival and involves multiple signaling pathways modulated by secreted factors derived from tumor cells, immune cells and non-neoplastic stromal cells within the TME. However, due to the complexity of this dynamic process, the underlying mechanisms remain incompletely understood. Li et al (84) identified the critical role of uridine phosphorylase 1 (UPP1), highly expressed in lung adenocarcinoma (LUAD) cells, in shaping an immunosuppressive TME. UPP1 upregulation in tumor cells elevated TGF-β1 secretion, which promoted Treg differentiation, CD8+ T cell exhaustion and macrophage polarization toward an M2 phenotype via paracrine signaling. Additionally, UPP1 activated the PI3K/AKT/mTOR pathway to enhance PD-L1 expression on LUAD cells, further impairing T cell cytotoxic function. Similarly, TGF-β3 overexpression in HCC cells was shown to activate the SMAD2/3-Sp1 axis, thereby upregulating decoy receptor 3 (DcR3), which inhibits pro-inflammatory signaling by binding to the LIGHT ligand on activated CD4+ T cells. DcR3 further promoted the differentiation of CD4+ T cells into Th2 and Treg cells while suppressing Th1 polarization, thereby impairing anti-tumor immunity in HCC (85). Xie et al (86) demonstrated that hypoxic tumor cells in mouse breast and colon cancer models produce angiotensin II (AngII) via a hypoxia-lactate-chymase-dependent axis, fostering an immunosuppressive TME characterized by increased infiltration of Tregs, TAMs, CAFs and MDSCs, alongside reduced recruitment of CD8+ effector T cells. Blocking AngII signaling reversed this immunosuppressive TME and markedly upregulated immune-activating cytokines (such as IL-7, IL-20 and CXCL11) while downregulating immunosuppressive cytokines (such as IL-10, GM-CSF and CCL28) in 4T1 breast cancer cells. Since the immunosuppressive microenvironment is essential for tumor persistence, its modulation represents a promising therapeutic strategy. Jiang et al (87) revealed c-Myc targeting as a strategy to overcome immune suppression in osteosarcoma. c-Myc inhibition via JQ-1 upregulated T cell-recruiting chemokines (CCL5, CXCL9 and CXCL10), thereby enhancing cytotoxic T cell infiltration. Simultaneously, c-Myc inhibition upregulated CD40 on dendritic cells (DCs) and activated CD40/CD40L costimulatory signaling to promote DC-T cell interaction and cytotoxic T cell activation. These immune-enhancing effects, rather than direct anti-proliferative actions, drove tumor regression and prolonged overall survival in murine models. Wang et al (88) found that STK3 expression is epigenetically suppressed via promoter hypermethylation in ovarian cancer, associated with poor patient prognosis. Overexpression of STK3 in ovarian cancer cells activated the NF-κB signaling pathway, leading to upregulated CXCL16 and CX3CL1. These chemokines facilitated the recruitment of CD8+ T cells and markedly inhibited tumor cell proliferation, migration and invasion while promoting apoptosis. These findings highlighted the role of STK3 in counteracting immune suppression.

High-stromal tumors, characterized by elevated fibroblast and MSC content, exhibit a distinct immune landscape marked by reduced infiltration of effector cells. In high-grade serous ovarian carcinoma, CXCL12 overexpression was found in epithelial-like tumor cells and cancer-associated MSCs and was shown to drive immune exclusion by binding to CXCR4 receptors on cytotoxic CD8+ T cells and NK cells, sequestering these effector cells in stromal compartments (89). This CXCL12-CXCR4 axis impaired anti-tumor immunity by suppressing granzyme B and IFN-γ production in CD8+ T cells while upregulating immunosuppressive cytokines such as CXCL1, CXCL5 and CXCL13 in myeloid cells (89). Similarly, Sheng et al (90) identified TAK1+ CAFs as playing a critical role in shaping the immunosuppressive microenvironment of PDAC. TAK1+ CAFs predominantly exhibit an inflammatory phenotype and serve as major sources of CXCL12 and IL-6, which recruit MDSCs and polarize macrophages toward M2 phenotypes, while also excluding CD8+ T cells from tumor nests. Notably, TAK1 inhibition shifted CAFs toward a tumor-suppressive myo-fibroblastic phenotype, which suppressed EMT and tumor cell invasion via downregulation of MAPK and NF-κB pathways. Furthermore, angiotensin-stimulated human and mouse fibroblasts were found to secrete CCL5 as a key immunosuppressive mediator in melanoma, which simultaneously reduced intra-tumoral CD8+ T cell infiltration and promoted Treg recruitment via CCR5 (91).

A growing number of studies have reported that neutrophils can be recruited to tumor tissues by specific cytokines and chemokines, undergo functional polarization and exert dual regulatory effects in TME immunomodulation. SenGupta et al (92) demonstrated that TNBC recruited neutrophils via tumor-derived factors. Antibody array screening identified a distinct secreted profile in aggressive TNBC cells compared with ER+ cells, characterized by elevated production of TGF-β and CXCR2 ligands (e.g., CXCL1, CXCL2 and CXCL3). These factors synergistically promoted neutrophil chemotaxis and polarization via TGF-β/SMAD3 signaling and the CXCR2-Gi pathway. Complementing this, Ogawa et al (93) revealed that loss of SMAD4, a tumor suppressor, in CRC cells triggered massive production of CXCL1 and CXCL8 via IκB kinase 2 and GSK-3β pathways, leading to accumulation of CXCR2+ neutrophils in tumors. These neutrophils further amplified CXCL1 and CXCL8 production in the TME, while polarizing neutrophils toward pro-angiogenic and immunosuppressive phenotypes marked by MMP-2 and 9, Arg-1 and indoleamine 2,3-dioxygenase expression. Germann et al (94) further identified that tumor-infiltrating neutrophils play a pivotal role in forming an immunosuppressive microenvironment in CRC. These neutrophils release MMP-9 to proteolytically convert latent TGF-β into its bio-active form. Activated TGF-β subsequently exerts dual immunosuppressive effects by suppressing anti-tumor effector T cells and promoting Treg expansion. Neutrophil depletion or MMP inhibition in mice reduces TGF-β signaling, enhances CD8+ T cell infiltration and diminishes the tumor burden. Conversely, Chan et al (95) reported that neutrophils acquire an anti-tumor phenotype in pancreatic adenocarcinoma (PAAD) with melatonin supplementation. Melatonin stimulates CXCL2 secretion from tumor cells, promoting neutrophil infiltration and polarization toward an N1-like phenotype in PAAD, characterized by CD11b+Ly6G+. These neutrophils exhibit elevated ROS and form neutrophil extracellular traps, inducing tumor cell death through cell-contact-dependent NETosis.

TME in promoting therapeutic resistance

Therapeutic resistance constitutes a fundamental issue limiting the efficacy of chemotherapy in cancer patients. Besides neoplastic cells, the TME mediates therapy resistance through direct intercellular contact within TME components or dynamic alterations in local secreted factors. CAFs exhibit functional heterogeneity in shaping chemoresistance through distinct secreted profiles that sustain cancer stemness and activate survival pathways. Su et al (96) identified a pivotal CD10+GPR77+ CAF subset in breast cancers that establishes a chemoresistance niche by driving persistent NF-κB activation through GPR77-mediated complement signaling. This signaling axis promotes IL-6 and IL-8 secretion in CAFs, which enhances cancer stemness and upregulates ABCG2-mediated drug efflux in cancer cells. Moreover, the recruitment and activation of this CAF subset were found to be dynamically regulated by crosstalk with macrophages (97). M2-polarized TAMs secrete CCL18, which binds to the PITPNM3 receptor on normal breast fibroblasts, inducing their differentiation into CD10+GPR77+ CAFs. In clinical cohorts, a strong association between CCL18+ TAM infiltration and CD10+GPR77+ CAF density across breast cancer subtypes was confirmed, highlighting the role of immune-stromal cell crosstalk in amplifying cancer chemoresistance (97). Further demonstrating the role of CAF secretory heterogeneity in chemoresistance, Hu et al (11) established a living biobank of patient-derived CAFs in non-small cell lung cancer and used high-throughput antibody arrays to functionally classify CAFs into three subtypes based on their secreted profiles and resistance mechanisms. Subtype I CAFs secreted high levels of HGF and FGF-7, activating MET/FGFR bypass signaling to confer robust resistance to EGFR/ALK inhibitors. Subtype II CAFs predominantly secreted FGF-7, mediating moderate FGFR-dependent resistance, while subtype III CAFs showed minimal protective capacity. This classification enabled subtype-specific therapeutic strategies: Dual MET/FGFR inhibition overcame subtype I-mediated resistance, whereas subtype II required only FGFR blockade. In melanoma, Papaccio et al (98) revealed a pivotal role of CAFs in suppressing chemotherapy-induced cytotoxicity via secretion of IL-6 and IL-8. CAF-derived supernatants enriched in pro-tumorigenic growth factors (HGF, FGF-2 and VEGF) accelerated melanoma cell migration and induced sustained activation of FAK signaling in treated tumor cells. In ovarian cancer, cisplatin-treated CAFs secrete elevated CCL5, which activates STAT3 and PI3K/AKT pathways in tumor cells, attenuating cisplatin cytotoxicity both in vitro and in vivo by enhancing Bcl-2 expression and suppressing pro-apoptotic markers (99). Additionally, Che et al (100) identified cisplatin-induced PAI-1 secretion by esophageal CAFs as a key mediator of AKT and ERK1/2 activation in esophageal squamous cell carcinoma (ESCC). PAI-1 suppresses caspase-3 activity and ROS accumulation, attenuating apoptosis in a paracrine manner, with clinical data linking high stromal PAI-1 expression to poor progression-free survival in ESCC patients. Chemoresistance development also involves remodeling of ECM. Chrisochoidou et al (101) showed that mesothelioma-activated fibroblasts deposit collagen- and tenascin-enriched ECM, which sequesters TGF-β and activates PI3K/mTOR/SRC pathways to promote resistance to cisplatin and pemetrexed. This fibrotic niche recruits naïve fibroblasts through chemo-attractants such as CHI3L1 and angiopoietin, establishing a self-sustaining resistance loop.

Tumor-associated immune cells and neoplastic cells cooperatively establish chemoresistance through secreted protein networks that reprogram the TME. In clear cell renal cell carcinoma, Wang et al (102) identified a self-reinforcing loop where SOX17 loss activated YAP/TEAD1 signaling, driving CCL5 secretion to recruit and polarize M2-like TAMs. These TAMs, in turn, suppressed SOX17 via CCR5/STAT3 signaling, amplifying CCL5 production and fostering resistance to tyrosine kinase inhibitors. Similarly, in KRAS-mutant CRC, Liu et al (103) demonstrated that mutant KRAS stabilizes HIF-1α via ROS-mediated prolyl hydroxylase inhibition, leading to overexpression of colony GM-CSF and lactate. These factors synergistically polarized macrophages toward an immunosuppressive CD206high/HLA-DRlow phenotype characterized by anti-inflammatory cytokine secretion, which shielded tumor cells from cetuximab-induced apoptosis. The metabolic reprogramming of CRC cells further reinforced TAM-mediated resistance, as lactate enhanced GM-CSF-driven immunosuppression. In HCC, macroH2A1 loss was demonstrated to enhance cancer cell stemness marked by reduced IL-6 and IL-8 secretion. This cytokine-depleted secretome reprogramed neighboring tumor cells to adopt chemoresistance and expanded immunosuppressive CD4+CD25+FoxP3+ Tregs, linking epigenetic dysregulation to paracrine-mediated immune evasion (104).

Resistance to radiotherapy also involves stromal secreted factors that alter tumor cell behavior. Guo et al (105) demonstrated that CAFs in breast cancer secrete IL-6 to promote radioresistance. CAF-derived IL-6 enhanced post-irradiation survival and proliferation of cancer cells in vitro, while murine co-injection models revealed that IL-6/STAT3 signaling diminished radiation efficacy. Chu et al (106) found that CAFs enhanced cervical cancer growth and resistance to radiation. CM from CAFs alone or from CAF-HeLa co-cultures suppressed DNA damage response genes (GADD45α, BTG2) and promoted p38 phosphorylation in HeLa cells following irradiation. Using antibody arrays, the authors identified distinct cytokine profiles in CAFs and HeLa cells: CAFs secreted high levels of IGF-2, EGF and FGF-4, whereas HeLa cells produced platelet-derived growth factor (PDGF)-AA and PDGF-BB, suggesting that this bidirectional crosstalk establishes a growth factor-enriched, radioprotective microenvironment through integrated growth factor signaling. Beyond fibroblasts, Cao et al (107) showed that sub-lethally irradiated non-parenchymal liver cells in HCC elevated MMP-8 production, which suppressed AMPK phosphorylation and activated mTOR signaling in HCC cells, driving TME remodeling and metastatic progression. Arshad et al (108) reported that simultaneous irradiation of stromal and tumor cells further modulates secretory dynamics. Irradiated wild-type fibroblasts secreted TGF-β1 to induce EMT in lung carcinoma cells, enhancing migration via Vimentin and Snail upregulation, while RhoB-deficient fibroblasts promoted pro-metastatic MMP secretion. Notably, co-irradiation of tumor and stromal cells suppressed TGF-β1 and MMP release, in contrast to stromal-only irradiation. RhoB-deficient fibroblasts additionally upregulated IL-6 and FGF-2 in tumor cells, suggesting genotype-specific shifts in paracrine signaling that may influence immune modulation.

Summary

The TME constitutes a complex ecosystem composed of cellular components (such as stromal cells, infiltrating immune cells and vascular and lymphatic networks) and non-cellular elements, including ECM components and soluble factors such as inflammatory factors, growth factors, chemokines and MMPs. Dynamic remodeling of the TME during tumor progression is regulated by intricate inter-cellular communication networks. Crosstalk among TME cell populations occurs via both direct cell-cell contact and signaling mediated by secreted factors. These complex communication networks form positive feedback loops that sustain tumor cell proliferation, facilitate invasion and immune evasion and enable resistance to therapy, ultimately influencing disease progression. An in-depth understanding of this cytokine-driven signaling is crucial to deciphering tumor-TME interactions.

Antibody arrays have emerged as powerful tools for exploring the regulatory mechanisms within the TME. Unlike traditional protein detection methods, antibody arrays enable high-throughput, multiplex detection of low-abundance secreted proteins using minimal sample input. By rapidly and accurately analyzing the secretion profiles, these platforms reveal dynamic alterations in TME signaling networks, elucidating cytokine-mediated mechanisms underlying tumor progression, drug resistance and immune escape. These findings not only deepen our understanding of TME heterogeneity but also inform the development of combination therapies targeting both tumor cells and their supportive microenvironment.

By mapping complex signaling cascades between tumor cells and stromal components to identify key cytokines, researchers can more effectively prioritize therapeutic targets. In addition, understanding the heterogeneity of the TME is essential for personalizing anti-tumor strategies and advancing precision oncology. This includes using antibody arrays to screen for predictive biomarkers in drug-resistant patients or to guide drug selection based on patient-specific TME secreted profiles. These applications hold promise for transforming cancer treatment paradigms.

Supplementary Data

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Authors' contributions

YW wrote the manuscript and constructed figures and tables. SL wrote the manuscript. HD and RPH revised the manuscript. SL and RPH conceived the study. Data authentication is not applicable. All authors have read and approved the final manuscript.

Ethics approval and consent to participate

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Patient consent for publication

Not applicable.

Competing interests

The authors declare that they have no competing interests.

Acknowledgements

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Funding

The present study was supported by RayBiotech innovative research fund, National Key R&D Program of China (grant nos. 2024YFA1307601, 2024YFA1307602 and 2024YFA1307603). Guangzhou 2024 Annual Special Project on Agricultural and Social Development Science and Technology (grant nos. 2024B03J1332 and 2024B03J1249).

References