Neutrophils are integral to the frontline defense against pathogenic bacterial and fungal invasions. Beyond their traditional roles, these cells are increasingly recognized for their dualistic contributions to the pathology of autoimmune and inflammatory diseases, as well as their complex involvement in cancer progression. Neutrophils interact with different disease states, highlighting their potential as therapeutic targets. Within tumor microenvironment (TME), tumor-associated neutrophils (TANs) exhibit a functional dichotomy, capable of either fostering or impeding tumor growth and metastasis. This binary functional potential of TANs, under certain conditions, suggests a reversible state that could transition from tumor-promoting to tumor-eradicating phenotypes. Despite the critical implications of such functional plasticity, systematic studies of TAN behavioral shifts in the context of cancer immunotherapy remain scarce. Herein, we review recent advancements in the understanding of TANs within the TME, highlighting their binary regulatory effects on solid tumors. Leveraging the latest insights from experimental and clinical research, this review elucidates the complex roles of TANs in tumor development and explores their molecular interactions as potential therapeutic targets. The elucidation of these mechanisms holds promise for novel cancer treatment strategies, aiming to improve patient outcomes by manipulating the tumor-promoting or -suppressing functions of TANs.

Esophageal squamous cell carcinoma (ESCC) remains a major cause of cancer-related mortality worldwide, with immunotherapy resistance representing a critical therapeutic challenge. This resistance is driven by profound tumor heterogeneity that fosters diverse immune evasion mechanisms, limiting the efficacy of current treatments. Thus, an urgent need exists to identify the key drivers within heterogeneous tumors that potentiate immunotherapeutic resistance. Single-cell, spatial, and bulk transcriptomic data from multiple ESCC cohorts were integrated to map the cellular ecosystems and decipher the heterogeneity of immunotherapy-resistant tumors. Multi-cohort analyses were conducted to assess clinical relevance, and key findings were validated through targeted functional assays in vitro and in vivo. We discovered a previously unrecognized secreted phosphoprotein 1 (SPP1)-expressing malignant epithelial subpopulation that is markedly enriched in non-responders, representing a critical component of tumor heterogeneity. Elevated SPP1 expression correlated with immunotherapy resistance and poorer survival across independent cohorts. Mechanistically, SPP1 promoted tumor proliferation and migration while upregulating PD-L1 via the mTORC1-STAT3 pathway, as revealed by our RNA-sequencing data. Furthermore, SPP1 induced CD8⁺ T cell exhaustion through SPP1-CD44 signaling, accompanied by CD44-dependent MAPK activation, and polarized macrophages toward an immunosuppressive phenotype via α5β1 integrin-dependent FAK-AKT-mTOR activation. Spatial transcriptomics validation demonstrated that SPP1⁺ cells shape immunosuppressive niches within the tumor microenvironment. Therapeutically, SPP1 blockade enhanced the antitumor effect of anti-PD-1 therapy to suppress tumor growth in vivo. To directly address tumor heterogeneity, we employed drug sensitivity profiling that nominated microtubule and kinase inhibitors as potent agents against SPP1⁺ cells, providing a strategy to target resistant subpopulations. This study establishes SPP1 as both a key driver of immunotherapy resistance in heterogeneous ESCC tumors and a promising therapeutic target for overcoming treatment failure.

Emerging proof shows that abnormal lipometabolism affects invasion, metastasis, stemness and tumor microenvironment in carcinoma cells. However, molecular markers related to lipometabolism have not been further established in breast cancer. In addition, numerous studies have been conducted to screen for prognostic features of breast cancer only with RNA sequencing profiles. Currently, there is no comprehensive analysis of multiomics data to extract better biomarkers. Therefore, we have downloaded the transcriptome, single nucleotide mutation and copy number variation dataset for breast cancer from the TCGA database, and constructed a riskScore of twelve genes by LASSO regression analysis. Patients with breast cancer were categorized into high and low risk groups based on the median riskScore. The high-risk group had a worse prognosis than the low-risk group. Next, we have observed the mutated frequencies and the copy number variation frequencies of twelve lipid metabolism related genes LMRGs and analyzed the association of copy number variation and riskScore with OS. Meanwhile, the ESTIMATE and CIBERSORT algorithms assessed tumor immune fraction and degree of immune cell infiltration. In immunotherapy, it is found that high-risk patients have better efficacy in TCIA analysis and the TIDE algorithm. Furthermore, the effectiveness of six common chemotherapy drugs was estimated. At last, high-risk patients were estimated to be sensitive to six chemotherapeutic agents and six small molecule drug candidates. Together, LMRGs could be utilized as a de novo tumor biomarker to anticipate better the prognosis of breast cancer patients and the therapeutic efficacy of immunotherapy and chemotherapy.