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Immune infiltration of tumors is closely associated with clinical outcome in renal cell carcinoma (RCC). Tumor‐infiltrating immune cells (TIICs) regulate cancer progression and are appealing therapeutic targets. The purpose of this study was to determine the composition of TIICs in RCC and further reveal the independent prognostic values of TIICs. CIBERSORT, an established algorithm, was applied to estimate the proportions of 22 immune cell types based on gene expression profiles of 891 tumors. Cox regression was used to evaluate the association of TIICs and immune checkpoint modulators with overall survival (OS). We found that CD8+ T cells were associated with prolonged OS (hazard ratio [HR] = 0.09, 95% confidence interval [CI].01‐.53; P = 0.03) in chromophobe carcinoma (KICH). A higher proportion of regulatory T cells was associated with a worse outcome (HR = 1.59, 95% CI 1.23‐.06; P < 0.01) in renal clear cell carcinoma (KIRC). In renal papillary cell carcinoma (KIRP), M1 macrophages were associated with a favorable outcome (HR = .43, 95% CI .25‐.72; P < 0.01), while M2 macrophages indicated a worse outcome (HR = 2.55, 95% CI 1.45‐4.47; P < 0.01). Moreover, the immunomodulator molecules CTLA4 and LAG3 were associated with a poor prognosis in KIRC, and IDO1 and PD‐L2 were associated with a poor prognosis in KIRP. This study indicates TIICs are important determinants of prognosis in RCC meanwhile reveals potential targets and biomarkers for immunotherapy development. We described the immune landscape in detail, revealing the distinct immune infiltration patterns of different subtypes and stages of RCC. We further revealed relationships between TIIC and molecular subtypes, tumor stages, recurrent genomic alterations and survival in RCC. Our work advances the understanding of immune response meanwhile reveals potential targets and biomarkers for immunotherapy development.

Tumor‐infiltrating immune cells play a crucial role in tumor progression and response to treatment. However, the limited studies on infiltrating immune cells have shown inconsistent and even controversial results for osteosarcoma (OS). In addition, the dynamic changes of infiltrating immune cells after neoadjuvant chemotherapy are largely unknown. We downloaded the RNA expression matrix and clinical information of 80 OS patients from the TARGET database. CIBERSORT was used to evaluate the proportion of 22 immune cell types in patients based on gene expression data. M2 macrophages were found to be the most abundant immune cell type and were associated with improved survival in OS. Another cohort of pretreated OS samples was evaluated by immunohistochemistry to validate the results from CIBERSORT analysis. Matched biopsy and surgical samples from 27 patients were collected to investigate the dynamic change of immune cells and factors before and after neoadjuvant chemotherapy. Neoadjuvant chemotherapy was associated with increased densities of CD3+ T cells, CD8+ T cells, Ki67 + CD8+ T cells and PD‐L1+ immune cells. Moreover, HLA‐DR‐CD33+ myeloid‐derived suppressive cells (MDSC) were decreased after treatment. We determined that the application of chemotherapy may activate the local immune status and convert OS into an immune “hot” tumor. These findings provide rationale for investigating the schedule of immunotherapy treatment in OS patients in future clinical trials. Host anti–tumor immune response boosted by neoadjuvant chemotherapy. Following neoadjuvant chemotherapy, CD3+ T cells increased significantly and there was a trend of increased cytotoxic T cells. CD8+ T cells in both tumor center and stroma also increased remarkably. Importantly, activated CD8+ T cells, defined as Ki67 + CD8+ T cells, were more abundant in post–chemotherapy samples, and were negatively correlated with the proliferation ability of tumor cells.

Small cell lung cancer (SCLC) has recently been subcategorized into neuroendocrine (NE)‐high and NE‐low subtypes showing ‘immune desert’ and ‘immune oasis’ phenotypes, respectively. Here, we aimed to characterize the tumor microenvironment according to immune checkpoints and NE subtypes in human SCLC tissue samples at the protein level. In this cross‐sectional study, we included 32 primary tumors and matched lymph node (LN) metastases of resected early‐stage, histologically confirmed SCLC patients, which were previously clustered into NE subtypes using NE‐associated key RNA genes. Immunohistochemistry (IHC) was performed on formalin‐fixed paraffin‐embedded TMAs with antibodies against CD45, CD3, CD8, MHCII, TIM3, immune checkpoint poliovirus receptor (PVR), and indoleamine 2,3‐dioxygenase (IDO). The stroma was significantly more infiltrated by immune cells both in primary tumors and in LN metastases compared to tumor nests. Immune cell (CD45+ cell) density was significantly higher in tumor nests (P = 0.019), with increased CD8+ effector T‐cell infiltration (P = 0.003) in NE‐low vs NE‐high tumors. The expression of IDO was confirmed on stromal and endothelial cells and was positively correlated with higher immune cell density both in primary tumors and in LN metastases, regardless of the NE pattern. Expression of IDO and PVR in tumor nests was significantly higher in NE‐low primary tumors (vs NE‐high, P < 0.05). We also found significantly higher MHC II expression by malignant cells in NE‐low (vs NE‐high, P = 0.004) tumors. TIM3 expression was significantly increased in NE‐low (vs NE‐high, P < 0.05) tumors and in LN metastases (vs primary tumors, P < 0.05). To our knowledge, this is the first human study that demonstrates in situ that NE‐low SCLCs are associated with increased immune cell infiltration compared to NE‐high tumors. PVR, IDO, MHCII, and TIM3 are emerging checkpoints in SCLC, with increased expression in the NE‐low subtype, providing key insight for further prospective studies on potential biomarkers and targets for SCLC immunotherapies. Small cell lung cancer (SCLC) has recently been subcategorized into neuroendocrine (NE)‐high and NE‐low subtypes. This study demonstrates that NE‐low SCLCs are associated with increased immune‐cell infiltration compared to NE‐high tumours. We found expression of PVR, indoleamine 2,3‐dioxygenase, MHCII, and TIM3 to be increased in the NE‐low subtype, highlighting these molecules as potential biomarkers and targets for SCLC immunotherapies.

Thymic carcinoma is a rare malignant disease with no standard systemic chemotherapy. The purpose of the present study was to investigate tumor‐infiltrating immune cells (TIIC) in the tumor microenvironment (TME), focusing on the impact of TIIC and program death‐ligand 1 (PD‐L1) expression on clinical outcomes in thymic cancer. Patients with thymic carcinoma resected between 1973 and 2017 were investigated. The tissue specimens were analyzed through immunohistochemical staining to elucidate the prognostic effects of TIIC, their ratios and PD‐L1 in a preliminary cohort (n = 10). The density of TIIC as well as PD‐L1 expression was evaluated in intraepithelial and tumor‐stromal areas on the representative whole section of tumors. The immune factors showing significant association with disease‐free survival (DFS) were evaluated in the total cohort (n = 42). TIIC in the preliminary population showed no significant difference between the two groups. However, CD8, CD20, CD204, FOXP3 and CD20/CD204 ratio demonstrated a tendency to act as predictive markers for recurrence. In the total cohort, significant differences were observed for CD8+, CD20+ and CD204+ cells in tumor islets, and for CD8+, CD20+ and FOXP3+ cells as well as the CD8/CD204 and CD20/CD204 ratios in the stroma, indicating their prognostic effect. The prognostic effect of the PD‐L1 expression in tumor cells could not be established, possibly because of intratumoral heterogeneity. CD8, CD20 and CD204 positive TIIC in stroma were identified as possible better prognostic biomarkers, considering the heterogeneity of other biomarkers. The present study paves the way for exploring strategies of combination immunotherapy targeting B cell immunity in thymic carcinoma. The present study revealed that CD8+, CD20+ and CD204+ tumor‐infiltrating immune cells in cancer stroma might be prognostic biomarkers, considering the heterogeneity of other biomarkers, including PD‐L1 expression on tumor cells in thymic carcinoma.

Intrahepatic cholangiocarcinoma (ICC) remains a highly heterogeneous disease with poor prognosis. Tumor‐infiltrating lymphocytes were predictive in various cancers, but their prognostic value in ICC is less clear. A total of 168 ICC patients who had received liver resection were enrolled and assigned to the derivation cohort. Sixteen immune markers in tumor and peritumor regions were examined by immunohistochemistry. A least absolute shrinkage and selection operator model was used to identify prognostic markers and to establish an immune signature for ICC (ISICC). An ISICC‐applied prediction model was built and validated in another independent dataset. Five immune features, including CD3peritumor (P), CD57P, CD45RAP, CD66bintratumoral (T) and PD‐L1P, were identified and integrated into an individualized ISICC for each patient. Seven prognostic predictors, including total bilirubin, tumor numbers, CEA, CA19‐9, GGT, HBsAg and ISICC, were integrated into the final model. The C‐index of the ISICC‐applied prediction model was 0.719 (95% CI, 0.660‐0.777) in the derivation cohort and 0.667 (95% CI, 0.581‐0.732) in the validation cohort. Compared with the conventional staging systems, the new model presented better homogeneity and a lower Akaike information criteria value in ICC. The ISICC‐applied prediction model may provide a better prediction performance for the overall survival of patients with resectable ICC in clinical practice. Using tissue microarray, we examined the density of 16 immune biomarkers in 280 ICC patients who underwent hepatectomy, and established a novel ISICC‐based prediction model (IPM) to predict patients’ overall survival with bilirubin, tumor numbers, CEA, CA19‐9, γ‐glutamyl transferase (GGT), HBsAg and ISICC. The new model may provide a better prediction performance for the overall survival of patients with resectable ICC in clinical practice.

The mortality rate of oesophageal squamous cell carcinoma (ESCC) remains high, and conventional TNM systems cannot accurately predict its prognosis, thus necessitating a predictive model. In this study, a 17‐gene prognosis‐related gene signature (PRS) predictive model was constructed using the random survival forest algorithm as the optimal algorithm among 99 machine‐learning algorithm combinations based on data from 260 patients obtained from TCGA and GEO. The PRS model consistently outperformed other clinicopathological features and previously published signatures with superior prognostic accuracy, as evidenced by the receiver operating characteristic curve, C‐index and decision curve analysis in both training and validation cohorts. In the Cox regression analysis, PRS score was an independent adverse prognostic factor. The 17 genes of PRS were predominantly expressed in malignant cells by single‐cell RNA‐seq analysis via the TISCH2 database. They were involved in immunological and metabolic pathways according to GSEA and GSVA. The high‐risk group exhibited increased immune cell infiltration based on seven immunological algorithms, accompanied by a complex immune function status and elevated immune factor expression. Overall, the PRS model can serve as an excellent tool for overall survival prediction in ESCC and may facilitate individualized treatment strategies and predction of immunotherapy for patients with ESCC.

Background Research on the impact of tumor-infiltrating immune cells(TIICs) combined with systemic inflammatory response (SIR) factors on cervical lesions and the prognosis of squamous cell cervical cancer (SCC) is limited. Therefore, this study aimed to evaluate the predictive and prognostic significance of TIICs and SIR factors in cervical epithelial lesions, specifically non-cervical epithelial lesions (NC), high-grade squamous intraepithelial lesions (HSIL), and SCC. Methods This retrospective study analyzed 163 patients in three cohorts: NC ( n  = 59), HSIL ( n  = 52), and SCC ( n  = 52). Tumor-infiltrating immune cells (TIICs) in the tumor/lesion center and adjacent stroma were assessed via immunohistochemistry and multiplex immunofluorescence, while systemic inflammatory response (SIR) factors were derived from preoperative blood counts. The primary outcome was relapse-free survival (RFS) in the SCC cohort, analyzed using Cox proportional hazards regression. Results TIICs were significantly elevated in the HSIL group compared with those in the NC group, accompanied by reduced platelet counts (PLT). The tumor stroma (TS) exhibited a greater degree of TIICs than the tumor/lesion center (TC) in both the HSIL and SCC groups. The presence of CD163+, CD11b+, and FOXP3 + TIICs, along with PLT levels, emerged as key indicators associated with the advanced histological stage. Compared to tTIICs, sTIICs demonstrated superior diagnostic performance in differentiating between HSIL and SCC groups. Lower levels of PLT (hazard ratio [HR] = 5.047, 95% confidence interval [CI]:1.373–18.540, P  = 0.015), higher CD4 + T cells (HR = 0.211, 95%CI:0.062–0.722, P  = 0.008), and FOXP3 + regulatory T cells (Tregs) (HR = 0.245, 95%CI:0.073–0.820, P  = 0.010) were identified as poor prognostic indicators for recurrence-free survival (RFS) in SCC. A combination of FOXP3 + Tregs and PLT provided a more robust prediction of SCC recurrence. An increase in exhausted CD4 + T cells likely explains the observation that higher CD4 + T-cell infiltration correlated with lower RFS in SCC. Conclusion The spatial distribution of TIICs, particularly the density in the tumor stroma, increases across the histological spectrum of cervical lesion severity. A signature combining FOXP3 + Treg cells and preoperative platelet counts provides a robust model for predicting SCC recurrence. Furthermore, the accumulation of exhausted CD4 + T cells appears to be a hallmark of disease advancement and poor prognosis, offering potential targets for personalized immunotherapy.

Aims Different types of tumor‐infiltrating immune cells not only augment but also dampen antitumor immunity in the microenvironment of melanoma. Therefore, it is critical to provide an overview of tumor‐infiltrating immune cells in melanoma and explore a novel strategy for immunotherapies. Methods We analyzed the immune states of different stages in melanoma patients by the immune, stromal, and estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) scores. Immune cell types were identified by the estimating relative subsets of RNA transcripts (CIBERSORTx) algorithm in 471 melanoma and 324 healthy tissues. Moreover, we performed a gene set variation analysis (GSVA) to determine the differentially regulated pathways in the tumor microenvironment. Results In melanoma cohorts, we found that ESTIMATE and immune scores were involved in survival or tumor clinical stage. Among the 22 immune cells, CD8+ T cells, M2 macrophages, and regulatory T cells (Tregs) showed significant differences using the CIBERSORTx algorithm. Furthermore, GSVA identified the immune cell‐related pathways; the primary immunodeficiency pathway, intestinal immune network for IgA, and TGF‐β pathways were identified as participants of the crosstalk in CD8+ T cells, Tregs, and M2 macrophages in the melanoma microenvironment. Conclusion These results reveal the cellular and molecular characteristics of immune cells in melanoma, providing a method for selecting targets of immunotherapies and promoting the efficacy of therapies for the treatment of melanoma. Immunotherapy has shown excellent responses in melanoma, while the reaction is low. However, the molecular mechanisms of tumor infiltrated immune cells have not been explored. In this study, we found that higher ESTIMATE and immune scores were associated with a clinical‐stage in melanoma patients and also filtered the crosstalks between cells that immune cells. Our work revealed the immune cellular and molecular characteristics of melanoma, providing a method for selecting targets promoting immunotherapy efficacy.

Background Immunotherapeutic approaches have recently emerged as effective treatment regimens against various types of cancer. However, the immune‐mediated mechanisms surrounding papillary renal cell carcinoma (pRCC) remain unclear. This study aimed to investigate the tumor microenvironment (TME) and identify the potential immune‐related biomarkers for pRCC. Methods The CIBERSORT algorithm was used to calculate the abundance ratio of immune cells in each pRCC samples. Univariate Cox analysis was used to select the prognostic‐related tumor‐infiltrating immune cells (TIICs). Multivariate Cox regression analysis was performed to develop a signature based on the selected prognostic‐related TIICs. Then, these pRCC samples were divided into low‐ and high‐risk groups according to the obtained signature. Analyses using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA) were performed to investigate the biological function of the DEGs (differentially expressed genes) between the high‐ and low‐risk groups. The hub genes were identified using a weighted gene co‐expression network analysis (WGCNA) and a protein‐protein interaction (PPI) analysis. The hub genes were subsequently validated by multiple clinical traits and databases. Results According to our analyses, nine immune cells play a vital role in the TME of pRCC. Our analyses also obtained nine potential immune‐related biomarkers for pRCC, including TOP2A, BUB1B, BUB1, TPX2, PBK, CEP55, ASPM, RRM2, and CENPF. Conclusion In this study, our data revealed the crucial TIICs and potential immune‐related biomarkers for pRCC and provided compelling insights into the pathogenesis and potential therapeutic targets for pRCC. The infiltration levels of 22 immune cells in the 291 pRCC samples obtained from patients are shown in Figures A and B. Additionally, we screened the nine immune cells associated with OS via univariate Cox analysis, and the results were shown in Figure D. The immune cells associated with OS were follicular helper T cells, Macrophages M1, activated dendritic cells activated, regulatory T cells (Tregs), B‐cell memory, CD8 T cells8, macrophages M2, naïve B cells, and CD4 memory‐activated T cells. The 291patients were divided into low‐ and high‐risk groups based on the selected prognostic‐related immune cells via Multivariate Cox regression analysis.

Objective At present, there is still a lack of reliable biomarkers for ovarian cancer (OC) to guide prognosis prediction and accurately evaluate the dominant population of immunotherapy. In recent years, the relationship between peripheral blood markers and tumor‐infiltrating immune cells (TICs) with cancer has attracted much attention. However, the relationship between the survival of OC patients and intratumoral‐ or extratumoral‐associated immune cells remains controversial. Methods In this study, four machine‐learning algorithms were used to predict overall survival in OC patients based on peripheral blood indicators. To further screen out immune‐related gene and molecular targets, we systematically explored the correlation between TICs and OC patient survival based on The Cancer Genome Atlas database. Using the TICs score method, patients were divided into a low immune infiltrating cell group and a high immune infiltrating cell group. Results The results showed that there was a significant statistical significance between the peripheral blood indicators and the survival prognosis of OC patients. Survival analysis showed that TICs play a crucial role in the survival of OC patients. Four core genes, CXCL9, CD79A, MS4A1, and MZB1, were identified by cross‐PPI and COX regression analysis. Further analysis found that these genes were significantly associated with both TICs and survival in OC patients. Conclusions These results suggest that both peripheral blood markers and TICs can be used as prognostic predictors in patients with OC, and CXCL9, CD79A, MS4A1, and MZB1 may be potential therapeutic targets for OC immunotherapy. This study combines several machine‐learning and bioinformatic analysis methods to explore the correlation of peripheral and tumor‐infiltrating immune cells with patient outcomes in ovarian cancer. The results suggest that both peripheral blood markers and TICs can be used as prognostic predictors in patients with ovarian cancer, and CXCL9, CD79A, MS4A1, and MZB1 may be potential therapeutic targets for ovarian cancer immunotherapy.