Profiles of immune infiltration in colorectal cancer and their clinical significant: A gene expression-based study

CSER: a gene regulatory network construction method based on causal strength and ensemble regression

Introduction

Gene regulatory networks (GRNs) reveal the intricate interactions between and among genes, and understanding these interactions is essential for revealing the molecular mechanisms of cancer. However, existing algorithms for constructing GRNs may confuse regulatory relationships and complicate the determination of network directionality.

Methods

We propose a new method to construct GRNs based on causal strength and ensemble regression (CSER) to overcome these issues. CSER uses conditional mutual inclusive information to quantify the causal associations between genes, eliminating indirect regulation and marginal genes. It considers linear and nonlinear features and uses ensemble regression to infer the direction and interaction (activation or regression) from regulatory to target genes.

Results

Compared to traditional algorithms, CSER can construct directed networks and infer the type of regulation, thus demonstrating higher accuracy on simulated datasets. Here, using real gene expression data, we applied CSER to construct a colorectal cancer GRN and successfully identified several key regulatory genes closely related to colorectal cancer (CRC), including ADAMDEC1, CLDN8, and GNA11.

Discussion

Importantly, by integrating immune cell and microbial data, we revealed the complex interactions between the CRC gene regulatory network and the tumor microenvironment, providing additional new biomarkers and therapeutic targets for the early diagnosis and prognosis of CRC.

Pan-cancer analysis reveals CCL5/CSF2 as potential predictive biomarkers for immune checkpoint inhibitors

BACKGROUND

Currently, there are no optimal biomarkers available for distinguishing patients who will respond to immune checkpoint inhibitors (ICIs) therapies. Consequently, the exploration of novel biomarkers that can predict responsiveness to ICIs is crucial in the field of immunotherapy.

METHODS

We estimated the proportions of 22 immune cell components in 10 cancer types (6,128 tumors) using the CIBERSORT algorithm, and further classified patients based on their tumor immune cell proportions in a pan-cancer setting using k-means clustering. Differentially expressed immune genes between the patient subgroups were identified, and potential predictive biomarkers for ICIs were explored. Finally, the predictive value of the identified biomarkers was verified in patients with urothelial carcinoma (UC) and esophageal squamous cell carcinoma (ESCC) who received ICIs.

RESULTS

Our study identified two subgroups of patients with distinct immune infiltrating phenotypes and differing clinical outcomes. The patient subgroup with improved outcomes displayed tumors enriched with genes related to immune response regulation and pathway activation. Furthermore, CCL5 and CSF2 were identified as immune-related hub-genes and were found to be prognostic in a pan-cancer setting. Importantly, UC and ESCC patients with high expression of CCL5 and low expression of CSF2 responded better to ICIs.

CONCLUSION

We demonstrated CCL5 and CSF2 as potential novel biomarkers for predicting the response to ICIs in patients with UC and ESCC. The predictive value of these biomarkers in other cancer types warrants further evaluation in future studies.

Prognostic value of preoperative combined neutrophil, monocyte, and lymphocyte scores in patients with renal cell carcinoma undergoing laparoscopic nephrectomy: A retrospective study

BACKGROUND

In a multi-institutional clinical study, we assessed the prognostic significance of a novel indicator preoperative peripheral blood immune (PBIS) scores that combined ratios of preoperative lymphocyte, monocyte, and neutrophil of renal cell carcinoma (RCC) patients undergoing laparoscopic nephrectomy.

METHODS

Between January 2014 and December 2019, 438 patients with RCC were retrospectively analyzed in three centers. We used X-tile software to obtain the optimum cut-off values for neutrophils, monocytes, and lymphocytes to classify the patients. To assess the relationship between PBIS score and overall survival (OS), and cancer-specific survival (CSS) in patients with RCC by Kaplan-Meier survival curves and Cox regression analyses. In addition, predictive OS and CSS nomograms were constructed. The discriminative ability of nomogram and predictive performance accuracy were verified with consistency index (C-index), calibration curves, receiver operating curve (ROC) curves, decision curve analysis (DCA) curves, and time-dependent ROC curves.

RESULTS

The optimum cutoff values for monocytes, lymphocytes, and neutrophils were 0.46, 1.01, and 4.50, respectively. We divided patients into four subgroups according to PBIS scores, which were significantly associated with M-stage (p = 0.008), T-stage (p < 0.001), N-stage (p = 0.006), and AJCC stage (p < 0.001). Multivariate Cox regression analysis revealed that RCC patients with lower PBIS scores showed a worse postoperative prognosis and served as an independent predictor of OS (p = 0.002) and CSS (p < 0.001). Ultimately, the nomograms based on PBIS scores demonstrated excellent predictive performance for OS (C-index: 0.770) and CSS (C-index: 0.828) through the analysis of calibration curves, ROC curves, DCA curves, and time-dependent ROC curves.

CONCLUSION

PBIS score served as novel and effective predictor to accurately predict OS and CSS in patients with RCC receiving laparoscopic nephrectomy.

Applying image features of proximal paracancerous tissues in predicting prognosis of patients with hepatocellular carcinoma

BACKGROUND

Most of the methods using digital pathological image for predicting Hepatocellular carcinoma (HCC) prognosis have not considered paracancerous tissue microenvironment (PTME), which are potentially important for tumour initiation and metastasis. This study aimed to identify roles of image features of PTME in predicting prognosis and tumour recurrence of HCC patients.

METHODS

We collected whole slide images (WSIs) of 146 HCC patients from Sun Yat-sen Memorial Hospital (SYSM dataset). For each WSI, five types of regions of interests (ROIs) in PTME and tumours were manually annotated. These ROIs were used to construct a Lasso Cox survival model for predicting the prognosis of HCC patients. To make the model broadly useful, we established a deep learning method to automatically segment WSIs, and further used it to construct a prognosis prediction model. This model was tested by the samples of 225 HCC patients from the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC).

RESULTS

In predicting prognosis of the HCC patients, using the image features of manually annotated ROIs in PTME achieved C-index 0.668 in the SYSM testing dataset, which is higher than the C-index 0.648 reached by the model only using image features of tumours. Integrating ROIs of PTME and tumours achieved C-index 0.693 in the SYSM testing dataset. The model using automatically segmented ROIs of PTME and tumours achieved C-index of 0.665 (95% CI: 0.556-0.774) in the TCGA-LIHC samples, which is better than the widely used methods, WSISA (0.567), DeepGraphSurv (0.593), and SeTranSurv (0.642). Finally, we found the Texture SumAverage Skew HV on immune cell infiltration and Texture related features on desmoplastic reaction are the most important features of PTME in predicting HCC prognosis. We additionally used the model in prediction HCC recurrence for patients from SYSM-training, SYSM-testing, and TCGA-LIHC datasets, indicating the important roles of PTME in the prediction.

CONCLUSIONS

Our results indicate image features of PTME is critical for improving the prognosis prediction of HCC. Moreover, the image features related with immune cell infiltration and desmoplastic reaction of PTME are the most important factors associated with prognosis of HCC.

Predictive value of procollagen c-protease enhancer protein on the prognosis of glioma patients

Procollagen c-protease enhancer protein (PCOLCE) performs an essential action in improving the recreation of procollagen c-protease and promoting the reconstruction of extracellular matrix. High PCOLCE expression was associated with a negative prognosis of stomach cancer, ovarian cancer, and osteosarcoma. The goal of this work is to investigate the function of PCOLCE in glioma. Multiple bioinformatics techniques have been employed to investigate the roles of PCOLCE in glioma, consisting of the correlation between PCOLCE and prognosis, immune checkpoints, immune cell infiltrates, and tumor microenvironment (TME). The gene ontology (GO) annotations and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses were used to assess the potential function of PCOLCE in glioma. PCOLCE was found to be increased in glioma. We revealed that PCOLCE was a potential prognostic factor and related to tumor grade. Up-regulated PCOLCE was related to poor prognosis in lower-grade glioma (LGG), glioblastoma multiforme (GBM), and recurrent glioma. PCOLCE was correlated with immune cell infiltration, particularly B cells, CD4+ T cells, macrophages, neutrophils, and dendritic cells (DCs) in LGG, and DCs infiltration in GBM. PCOLCE was co-expressed with many genes related to the immune and the immune checkpoint. In addition, glioma patients with low expression of PCOLCE had a higher response to the immunological checkpoint blockade (ICB). Additionally, PCOLCE may exert its roles via several immune-related biological processes or pathways, such as leukocyte migration, activation of T cells, adaptive immune response, neutrophil-mediated immunity, NF-κB, and TNF signaling pathways. In conclusion, PCOLCE may be a new immune-related gene and regulate tumor development through immunological pathways.

Hypoxia-derived molecular subtype and gene signature characterize prognoses and therapeutic responses in head and neck squamous cell carcinoma

Intratumoral hypoxia is widely associated with the development of malignancy, treatment resistance, and worse prognoses. This study aims to investigate the role of hypoxia-related genes (HRG) in the immune landscape, treatment response, and prognosis of head and neck squamous cell carcinoma (HNSCC). The transcriptome and clinical data of HNSCC were downloaded from TCGA and GEO databases, and HNSCC molecular subtypes were identified using non-negative matrix factorization (NMF) clustering. Prognostic models were constructed using univariate, Lasso, and multivariate Cox regression analyses. The relationship between HRGs and immune cell infiltration, immune therapy response, and drug sensitivity was evaluated, and a nomogram was constructed. 47 HRGs were differentially expressed in HNSCC, among which 10 genes were significantly associated with HNSCC prognosis. Based on these 10 genes, 2 HNSCC molecular subtypes were identified, which showed significant heterogeneity in terms of prognosis, immune infiltration, and treatment response. A total of 3280 differentially expressed genes were identified between the subtypes. After univariate, Lasso, and multivariate Cox regression analysis, 18 genes were selected to construct a novel prognostic model, which showed a significant correlation with B cells, T cells, and macrophages. Using this model, HNSCC was classified into high-risk and low-risk groups, which exhibited significant differences in terms of prognosis, immune cell infiltration, immune therapy response, and drug sensitivity. Finally, a nomogram based on this model and radiotherapy was constructed, which showed good performance in predicting HNSCC prognosis and guiding personalized treatment strategies. The decision curve analysis demonstrated its better clinical applicability compared to other strategies. HRGs can identify 2 HNSCC molecular subtypes with significant heterogeneity, and the HRG-derived risk model has the potential for prognostic prediction and guiding personalized treatment strategies.

The Mechanistic Target of Rapamycin Complex 1 Pathway Contributes to the Anti-Tumor Effect of Granulocyte-Macrophage-Colony-Stimulating Factor-Producing T Helper Cells in Mouse Colorectal Cancer

INTRODUCTION

The role of granulocyte-macrophage-colony-stimulating factor-producing T helper (ThGM) cells in colorectal cancer (CRC) development remains unclear. This study characterizes the function of ThGM cells in mouse CRC.

METHODS

Mouse CRC was induced by administrating azoxymethane and dextran sulfate sodium. The presence of ThGM cells in CRC tissues and the mechanistic target of rapamycin complex 1 (mTORC1) signaling in ThGM cells was detected by flow cytometry. The impact of mTORC1 signaling on ThGM cell function was determined by in vitro culture. The effect of ThGM cells on CRC development was evaluated by adoptive transfer assays.

RESULTS

ThGM cells, which expressed granulocyte-macrophage-colony-stimulating factor (GM-CSF), accumulated in CRC tissues. mTORC1 signaling is activated in CRC ThGM cells. mTORC1 inhibition by rapamycin suppressed ThGM cell differentiation and proliferation and resulted in the death of differentiating ThGM cells. mTORC1 inhibition in already differentiated ThGM cells did not induce significant cell death but decreased the expression of GM-CSF, interleukin-2, and tumor necrosis factor-alpha while impeding cell proliferation. Furthermore, mTORC1 inhibition diminished the effect of ThGM cells on driving macrophage polarization toward the M1 type, as evidenced by lower expression of pro-inflammatory cytokines, major histocompatibility complex class II molecule, and CD80 in macrophages after co-culture with rapamycin-treated ThGM cells. Lentivirus-mediated knockdown/overexpression of regulatory-associated protein of mTOR (Raptor) confirmed the essential role of mTORC1 in ThGM cell differentiation and function. Adoptively transferred ThGM cells suppressed CRC growth whereas mTORC1 inhibition abolished this effect.

CONCLUSION

mTORC1 is essential for the anti-CRC activity of ThGM cells.

FSTL3 is associated with prognosis and immune cell infiltration in lung adenocarcinoma

PURPOSE

FSTL3 expression is altered in various types of cancer. However, the role and mechanism of action of FSTL3 in lung adenocarcinoma development and tumor immunity are unknown. We investigated the association between FSTL3 expression and clinical characteristics and immune cell infiltration in lung adenocarcinoma samples from The Cancer Genome Atlas (TCGA) and a separate validation set from our hospital.

METHODS

Data on immune system infiltration, gene expression, and relevant clinical information were obtained by analyzing lung adenocarcinoma sample data from TCGA database. Using online tools like GEPIA, the correlations between FSTL3 expression and prognosis, clinical stage, survival status, and tumor-infiltrating immune cells were examined. In a validation dataset, immunohistochemistry was performed to analyze FSTL3 expression and its related clinical characteristics.

RESULTS

FSTL3 expression was markedly reduced in patients with lung adenocarcinoma. N stage, pathological stage, and overall survival were significantly correlated with FSTL3 expression. According to GSEA, FSTL3 is strongly linked to signaling pathways such as DNA replication and those involved in cell cycle regulation. Examination of TCGA database and TIMER online revealed a correlation between FSTL3 and B cell, T cell, NK cell, and neutrophil levels. The prognosis of patients with lung adenocarcinoma was significantly affected by six genes (KRT6A, VEGFC, KRT14, KRT17, SNORA12, and KRT81) related to FSTL3.

CONCLUSION

FSTL3 is significantly associated with the prognosis and progression of lung adenocarcinoma and the infiltration of immune cells. Thus, targeting FSTL3 and its associated genes in immunotherapy could be potentially beneficial for the treatment of lung adenocarcinoma.

Association between immune-related hub genes CD36, CXCL13, FGFR4, GABBR1, LAMP3, MMP12, and PPM1H and colorectal cancer prognosis

The present study aims to identify immune-related prognostic genes in colorectal cancer (CRC), and to explore potential mechanisms through which these genes regulate CRC progression. We first constructed a prognostic risk model based on seven gene signatures [cluster of differentiation-36 (CD36), chemokine (C-X-C-motif) ligand 13 (CXCL13), fibroblast growth factor receptor 4 (FGFR4), gamma-amino-butyric acid type B receptor 1 (GABBR1), lysosome-associated membrane glycoprotein 3 (LAMP3), recombinant matrix metalloproteinase 12 (MMP12), and protein phosphatase 1H (PPM1H)] using integrated bioinformatic analyses. FGFR4, GABBR1, and LAMP3 were highly expressed in CRC cell lines (in comparison with a normal colonic epithelial cell line), while CD36, CXCL13, MMP12, and PPM1H were weakly expressed. These in vitro expression results were largely consistent with our bioinformatic analysis. A prognostic model was generated to identify a high-risk group with worse survival outcome based on Kaplan-Meier analysis. Our prognostic model showed superior accuracy in both the training and test cohorts. In addition, we found that the low-risk subgroup exhibited greater infiltration by M1 macrophages, CD8+ T cells, CD4+ T cells, and activated NK cells. In conclusion, our findings provide evidence that seven immune-related hub genes can be considered as gene signatures to predict CRC prognosis and to differentiate CRC patient benefit, ultimately serving as a guide for individualized immunotherapy.

MRPL48 is a novel prognostic and predictive biomarker of hepatocellular carcinoma

BACKGROUND

Hepatocellular carcinoma (HCC) is one of the most prevalent forms of cancer and poses a threat to the health and survival of humans. Mitochondrial ribosomal protein L48 (MRPL48) belongs to the mitochondrial ribosomal protein family, which participates in energy production. Studies have shown that MRPL48 can predict osteosarcoma incidence and prognosis, as well as promotes colorectal cancer progression. However, the role of MRPL48 in HCC remains unknown.

METHODS

TCGA, GEO, HCCDB, CPTAC, SMART, UALCAN, Kaplan-Meier plotter, cBioPortal, and MethSurv were performed for bioinformatics purposes. Quantitative RT-PCR, immunoblotting, and functional studies were conducted to validate the methodology in vitro.

RESULTS

MRPL48 was greatly overexpressed in HCC tissues, compared with healthy tissue, which was subsequently demonstrated in vitro as well. The survival and regression analyses showed that MRPL48 expression is of significant clinical prognostic value in HCC. The ROC curve and nomogram analysis indicated that MRPL48 is a powerful predictor of HCC. MRPL48 methylation was adversely associated with the expression of MRPL48, and patients with a low level of methylation had poorer overall survival than those with a high level of methylation. GSEA showed that the expression of the MRPL48 was correlated with Resolution of Sister Chromatid Cohesion, Mitotic Prometaphase, Retinoblastoma Gene in Cancer, RHO Gtpases Activate Formins, Mitotic Metaphase and Anaphase, and Cell Cycle Checkpoints. An analysis of immune cell infiltration showed a significant association between MRPL48 and immune cell infiltration subsets, which impacted the survival of HCC patients. Additionally, MRPL48 knockdown reduced HCC cell proliferation, migration, and invasion in vitro.

CONCLUSIONS

We demonstrated that MRPL48 expression may be associated with HCC development and prognosis. These findings may open up new research directions and opportunities for the development of HCC treatments.

A novel immune-related risk-scoring system associated with the prognosis and response of cervical cancer patients treated with radiation therapy

Objective: The tumor microenvironment plays a critical role in the radiotherapy and immunotherapy response of cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC). Radioresistance is a key factor in treatment failure among patients who receive radical radiotherapy. Thus, new immune-related biomarkers associated with radiotherapy response in CESC are needed. Methods: In this study, the CIBERSORT and ESTIMATE methods were applied to determine the percentage of tumor-infiltrating cells and the number of immune components in 103 CESCs treated with radiotherapy from The Cancer Genome Atlas (TCGA) database. The main dysregulated genes were subjected to multivariate and univariate analyses. The prognostic value of this system was studied via receiver operating characteristic curve and survival analysis. For further confirmation, the biomarkers' expression levels and predictive value were validated by immunohistochemistry (IHC) and qRT-PCR. The CIBERSORT algorithm was used to calculate the compositional patterns of 22 types of immune cells in cervical cancer patients treated with radiation therapy. Results: Data for 17 radioresistant and 86 radiosensitive tumors were obtained from the The Cancer Genome Atlas database. 53 immune-related DEGs were identified. GO and KEGG analyses revealed that the DEGs were enriched in protein kinase B signaling, growth factors in cytokines, the MAPK pathway and the PI3K-Akt pathway. Then, 14 key immune-related genes built a risk scoring model were deemed prognostic in CESC with radiotherapy. The area under the curve (AUC) of the model was 0.723, and the high-risk group presented worse outcomes than the low-risk group. In addition, the high-risk group tended to have persistent tumors (p = 0.001). The high expression of WT1 and SPOUYT4 were associated with relapse, the high expression of Angiotensinogen and MIEN1 were associated with nonrelapse. Analysis of the immune microenvironment indicated that M0 macrophages, M2 macrophages, activated mast cells and resting memory CD4+ T cells were positively correlated with the risk score (p < 0.05). Conclusion: The novel immune-related risk scoring system has some advantages in predicting the prognosis and treatment response of cervical cancer patients treated with radiotherapy. Moreover, it might provide novel clues for providing targeted immune therapy to these patients.

Identification and validation of immune-related hub genes based on machine learning in prostate cancer and AOX1 is an oxidative stress-related biomarker

To investigate potential diagnostic and prognostic biomarkers associated with prostate cancer (PCa), we obtained gene expression data from six datasets in the Gene Expression Omnibus (GEO) database. The datasets included 127 PCa cases and 52 normal controls. We filtered for differentially expressed genes (DEGs) and identified candidate PCa biomarkers using a least absolute shrinkage and selector operation (LASSO) regression model and support vector machine recursive feature elimination (SVM-RFE) analyses. A difference analysis was conducted on these genes in the test group. The discriminating ability of the train group was determined using the area under the receiver operating characteristic curve (AUC) value, with hub genes defined as those having an AUC greater than 85%. The expression levels and diagnostic utility of the biomarkers in PCa were further confirmed in the GSE69223 and GSE71016 datasets. Finally, the invasion of cells per sample was assessed using the CIBERSORT algorithm and the ESTIMATE technique. The possible prostate cancer (PCa) diagnostic biomarkers AOX1, APOC1, ARMCX1, FLRT3, GSTM2, and HPN were identified and validated using the GSE69223 and GSE71016 datasets. Among these biomarkers, AOX1 was found to be associated with oxidative stress and could potentially serve as a prognostic biomarker. Experimental validations showed that AOX1 expression was low in PCa cell lines. Overexpression of AOX1 significantly reduced the proliferation and migration of PCa cells, suggesting that the anti-tumor effect of AOX1 may be attributed to its impact on oxidative stress. Our study employed a comprehensive approach to identify PCa biomarkers and investigate the role of cell infiltration in PCa.

Identification of a Twelve-microRNA Signature with Prognostic Value in Stage II Microsatellite Stable Colon Cancer

We aimed to identify and validate a set of miRNAs that could serve as a prognostic signature useful to determine the recurrence risk for patients with COAD. Small RNAs from tumors of 100 stage II, untreated, MSS colon cancer patients were sequenced for the discovery step. For this purpose, we built an miRNA score using an elastic net Cox regression model based on the disease-free survival status. Patients were grouped into high or low recurrence risk categories based on the median value of the score. We then validated these results in an independent sample of stage II microsatellite stable tumor tissues, with a hazard ratio of 3.24, (CI_95% = 1.05-10.0) and a 10-year area under the receiver operating characteristic curve of 0.67. Functional analysis of the miRNAs present in the signature identified key pathways in cancer progression. In conclusion, the proposed signature of 12 miRNAs can contribute to improving the prediction of disease relapse in patients with stage II MSS colorectal cancer, and might be useful in deciding which patients may benefit from adjuvant chemotherapy.

Network pharmacological analysis of corosolic acid reveals P4HA2 inhibits hepatocellular carcinoma progression

BACKGROUND

Corosolic acid is a pentacyclic triterpene acid with hypoglycemic, anti-inflammatory, and anti-cancer effects. However, its potential targets in hepatocellular carcinoma (HCC) are unknown, hindering clinical utilization.

METHODS

Differentially expressed proteins of the Bel-7404 cell line were identified with tandem mass tag analysis and differentially expressed genes (DEGs) of an HCC TCGA dataset using bioinformatics. Gene functions and pathways were inferred using the DAVID database. Online databases were used to establish P4HA2 expression in HCC (GEPIA2) and its relationship with patient survival (UALCAN and The Human Protein Atlas), the association between P4HA2 expression and immune cell infiltration (TIMER2), and DNA methylation of the P4HA2 gene (MethSurv). Cell proliferation, cell cycle, and cell death were assessed with PI and SYTOX-Green staining, CCK-8, and colony formation assays. Protein expression levels were detected by Western blotting.

RESULTS

A total of 44 differentially expressed proteins and 4498 DEGs were identified. Four genes whose proteins were also found in the differential protein profile but with opposing expressions were selected as candidate targets. The candidate gene prolyl 4-hydroxylase subunit alpha 2 (P4HA2) was recognized as the only potential target due to its high expression in public datasets, association with poor patient survival, and relation to immune cell infiltration in HCC tissues. Moreover, the DNA methylation status in 4 CpG islands of the P4HA2 gene correlated with a poor prognosis. Furthermore, corosolic acid treatment inhibited the proliferation of HCC cell lines Bel-7404 and HepG2 in a dose-dependent manner, caused G2/M phase cell cycle arrest, and promoted cell death. In addition, the treatment reduced P4HA2 protein levels.

CONCLUSION

Our results indicate that P4HA2 is a potential target of corosolic acid. Thus, they contribute to understanding molecular changes in HCC after corosolic acid treatment and facilitate finding new treatment regimens.

Epigenetic and Tumor Microenvironment for Prognosis of Patients with Gastric Cancer

BACKGROUND

Epigenetics studies heritable or inheritable mechanisms that regulate gene expression rather than altering the DNA sequence. However, no research has investigated the link between TME-related genes (TRGs) and epigenetic-related genes (ERGs) in GC.

METHODS

A complete review of genomic data was performed to investigate the relationship between the epigenesis tumor microenvironment (TME) and machine learning algorithms in GC.

RESULTS

Firstly, TME-related differential expression of genes (DEGs) performed non-negative matrix factorization (NMF) clustering analysis and determined two clusters (C1 and C2). Then, Kaplan-Meier curves for overall survival (OS) and progression-free survival (PFS) rates suggested that cluster C1 predicted a poorer prognosis. The Cox-LASSO regression analysis identified eight hub genes (SRMS, MET, OLFML2B, KIF24, CLDN9, RNF43, NETO2, and PRSS21) to build the TRG prognostic model and nine hub genes (TMPO, SLC25A15, SCRG1, ISL1, SOD3, GAD1, LOXL4, AKR1C2, and MAGEA3) to build the ERG prognostic model. Additionally, the signature's area under curve (AUC) values, survival rates, C-index scores, and mean squared error (RMS) curves were evaluated against those of previously published signatures, which revealed that the signature identified in this study performed comparably. Meanwhile, based on the IMvigor210 cohort, a statistically significant difference in OS between immunotherapy and risk scores was observed. It was followed by LASSO regression analysis which identified 17 key DEGs and a support vector machine (SVM) model identified 40 significant DEGs, and based on the Venn diagram, eight co-expression genes (ENPP6, VMP1, LY6E, SHISA6, TMEM158, SYT4, IL11, and KLK8) were discovered.

CONCLUSION

The study identified some hub genes that could be useful in predicting prognosis and management in GC.

Identification of a novel Immune-Related prognostic model for patients with colorectal cancer based on 3 subtypes

BACKGROUND

The mechanism of immunity in the development of colorectal cancer (CRC) has been studied in-depth, but knowledge of its role in the treatment of CRC is limited.

OBJECTIVE

This study aimed to classify CRC based on immunology and construct an immune-related prognostic model.

METHODS

Nine expression profile datasets of CRC, comprising 1640 samples, were downloaded from the NCBI GEO database. Immune infiltration of CRC was estimated using 5 algorithms. Based on the relative infiltration level of immune cells, immune score, and stromal score, immunosubtype analysis of tumors was conducted. Differentially expressed genes (DEGs) between the two subtypes were screened, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis were performed. Hematoxylin eosin (HE) staining, immunohistochemical (IHC) staining and qPCR were used to verify the correlation between DEGs and differentiation degree of cancer and the expression of Ki67. Subsequently, a risk signature was constructed based on the least absolute shrinkage and selection operator (LASSO) model.

RESULTS

Based on the infiltration level, immune score, and stromal score of each immune cell, CRC was divided into three immune cell subtypes. Most immune checkpoint genes showed highly significant differences among the three cell subtypes, and most of the co-stimulatory and co-inhibitory molecules were lower in cluster 1 and the highest in cluster 3. Next, 50 common DEGs were determined from the intersections of the different subtypes. Among these common DEGs, 25 were identified to be relevant to the prognosis of CRC patients. The mRNA expressions of C5orf46, CYP1B1, MIR100HG, SFRP2 and CXCL13 was related to clinical prognostic indicators. Finally, these 5 DEGs were included in a prognostic risk signature model, which effectively identified high-risk groups among CRC patients in both the training and validation sets.

CONCLUSION

In this study, CRCs were divided into three subtypes based on immunology, and the different subtypes led to different prognosis. Additionally, a prognostic model was constructed based on five immune-related DEGs to distinguish the three subtypes.

Exploration of prognostic biomarkers in head and neck squamous cell carcinoma microenvironment from TCGA database

Background

Immune checkpoint blockade (ICB) therapies have redefined human cancer treatment, including for head and neck squamous cell carcinoma (HNSCC). However, clinical responses to various immune checkpoint inhibitors are often accompanied by immune-related adverse events (irAEs). Therefore, it is crucial to obtain a comprehensive understanding of the association between different immune tumor microenvironments (TMEs) and the immunotherapeutic response.

Methods

The research data were obtained from The Cancer Genome Atlas (TCGA) database. We applied RNA-seq genomic data from tumor biopsies to assess the immune TME in HNSCC. As the TME is a heterogeneous system that is highly associated with HNSCC progression and clinical outcome, we relied on the Estimation of Stromal and Immune cells in Malignant Tumor tissues using Expression data (ESTIMATE) algorithm to calculate immune and stromal scores that were evaluated based on the immune or stromal components in the TME. Then, the Tumor Immune Dysfunction and Exclusion algorithm (TIDE) was used to predict the benefits of ICB to each patient. Finally, we identified specific prognostic tumor-infiltrating immune cells (TIICs) by quantifying the cellular composition of the immune response in HNSCC and its association to survival outcome, using the CIBERSORT algorithm.

Results

Utilizing the HNSCC cohort of the TCGA database and TIDE and ESTIMATE algorithm-derived immune scores, we obtained a list of microenvironment-associated lncRNAs that predicted different clinical outcomes in HNSCC patients. We validated these correlations in a different HNSCC cohort available from the TCGA database and provided insight into the prediction of response to ICB therapies in HNSCC.

Conclusions

This study confirmed that CD8⁺ T cells were significantly associated with better survival in HNSCC and verified that the top five significantly mutated genes (SMGs) in the TCGA HNSCC cohort were TP53, TTN, FAT1, CDKN2A, and MUC16. A high level of CD8⁺ T cells and high immune and stroma scores corresponded to a better survival probability in HNSCC.

Invasion characteristics and clinical significance of tumor-associated macrophages in gastrointestinal Krukenberg tumors

Background

Tumor-associated macrophages (TAMs) have been used as potential drug targets in preclinical research and clinical trials of various cancers. However, their distribution in Krukenberg tumors (KTs) remains unclear. We investigated the expression and prognostic value of TAMs in patients with gastrointestinal cancer with KTs.

Methods

The infiltration of various types of TAMs was detected in surgical tissues of 35 patients with KTs using immunohistochemical staining. The level of infiltration of TAMs in tumor nests (TN), tumor stroma (TS), and invasive margin (IM) areas was evaluated. The Kaplan-Meier method and univariate/multivariate Cox regression risk models were used to analyze the relationship between the degree of TAMs invasion and overall survival (OS) and progression-free survival (PFS).

Results

The distribution of TAMs exhibited spatial heterogeneity between TN, TS, and IM regions in primary tumor (PT) and KT tissues. TAMs infiltrated in the TN had greater prognostic value and were barely influenced by preoperative neoadjuvant therapy, despite similar grades of invasion in PT and KT tissues. Moreover, the number of CD68+ TAMs in TN of KT tissues was an independent risk factor affecting patient OS, whereas tumor resection scope might be an independent risk factor affecting patient PFS.

Conclusions

In view of the close relationship between TAMs, the tumor microenvironment and patient prognosis, targeting TAMs combined with chemotherapy is expected to become a new approach for the treatment of patients with KTs.

Identification of prognostic immune-related lncRNA signature predicting the overall survival for colorectal cancer

Long non-coding RNA (lncRNA) is an important regulator of gene expression and serves a fundamental role in immune regulation. The present study aimed to develop a novel immune-related lncRNA signature to assess the prognosis of patients with colorectal cancer (CRC). Transcriptome data and clinical information of patients with CRC were downloaded from The Cancer Genome Atlas (TCGA) and UCSC Xena platforms. Immune-related mRNAs were extracted from the Molecular Signatures Database (MSigDB), and the immune-related lncRNAs were identified based on correlation analysis. Then, univariate, Lasso and multivariate Cox regression were applied to construct an immune-related lncRNA signature, and CRC patients were divided into high- and low-risk groups according to the median risk score. Finally, we evaluated the signature from the perspectives of clinical outcome, clinicopathological parameters, tumor-infiltrating immune cells (TIICs), immune status, tumor mutation burden (TMB) and immunotherapy responsiveness. In total, 272 immune-related lncRNAs were identified, five of which were applied to construct an immune-related lncRNA signature. The signature divided patients with CRC into low- and high-risk groups, the prognosis of patients in the high-risk group were significantly poorer than those in low-risk group, and the results were further confirmed in external validation cohort. Furthermore, the high-risk group showed aggressive clinicopathological characteristics, specific TIIC and immune function status, and low sensitivity to immunotherapy. The immune-related lncRNA signature could be exploited as a promising biomarker for predicting the prognosis and immune status of patients with CRC.

Identification of the osteoarthritis signature gene PDK1 by machine learning and its regulatory mechanisms on chondrocyte autophagy and apoptosis

Background

Osteoarthritis (OA) is a degenerative joint disease frequently diagnosed in the elderly and middle-aged population. However, its specific pathogenesis has not been clarified. This study aimed to identify biomarkers for OA diagnosis and elucidate their potential mechanisms for restoring OA-dysregulated autophagy and inhibiting chondrocyte apoptosis in vitro.

Material and methods

Two publicly available transcriptomic mRNA OA-related datasets (GSE10575 and GSE51588) were explored for biomarker identification by least absolute shrinkage and selection operator (LASSO) regression, weighted gene co-expression network analysis (WGCNA), and support vector machine recursive feature elimination (SVM-RFE). We applied the GSE32317 and GSE55457 cohorts to validate the markers' efficacy for diagnosis. The connections of markers to chondrocyte autophagy and apoptosis in OA were also comprehensively explored in vitro using molecular biology approaches, including qRT-PCR and Western blot.

Results

We identified 286 differentially expressed genes (DEGs). These DEGs were enriched in the ECM-receptor interaction and PI3K/AKT signaling pathway. After external cohort validation and protein-protein interaction (PPI) network construction, PDK1 was finally identified as a diagnostic marker for OA. The pharmacological properties of BX795-downregulated PDK1 expression inhibited LPS-induced chondrocyte inflammation and apoptosis and rescued OA-dysregulated autophagy. Additionally, the phosphorylation of the mediators associated with the MAPK and PI3K/AKT pathways was significantly downregulated, indicating the regulatory function of PDK1 in apoptosis and autophagy via MAPK and PI3K/AKT-associated signaling pathways in chondrocytes. A significantly positive association between the PDK1 expression and Neutrophils, Eosinophils, Plasma cells, and activated CD4 memory T cells, as well as an evident negative correlation between T cells follicular helper and CD4 naive T cells, were detected in the immune cell infiltration analysis.

Conclusions

PDK1 can be used as a diagnostic marker for OA. Inhibition of its expression can rescue OA-dysregulated autophagy and inhibit apoptosis by reducing the phosphorylation of PI3K/AKT and MAPK signaling pathways.

An immune and epithelial-mesenchymal transition-related risk model and immunotherapy strategy for grade II and III gliomas

Grade II and III gliomas are heterogeneous and aggressive diseases. More efficient prognosis models and treatment methods are needed. This study aims to construct a new risk model and propose a new strategy for grade II and III gliomas. The data were downloaded from The Cancer Genome Atlas (TCGA), the Gene Expression Omnibus (GEO), gene set enrichment analysis (GSEA), and the EMTome website for analysis. The Human Cell Landscape website and the Genomics of Drug Sensitivity in Cancer website were used for single-cell analysis and drug susceptibility analysis. Gene set enrichment analysis, gene function enrichment analysis, univariate and multivariate Cox regression analyses, Pearson's correlation analysis, log-rank test, Kaplan-Meier survival analysis, and ROC analysis were performed. We constructed an immune-related prognostic model associated with the isocitrate dehydrogenase 1 (IDH1) mutation status. By analyzing the immune microenvironment of patients with different risk scores, we found that high-risk patients were more likely to have an inflammatory immune microenvironment and a higher programmed death ligand-1 (PD-L1) expression level. Epithelial-mesenchymal transition (EMT)-related gene sets were significantly enriched in the high-risk group, and the epithelial-mesenchymal transition phenotype was associated with a decrease in CD8+ T cells and an increase in M2 macrophages. Transforming growth factor-β (TGF-β) signaling was the most important signaling in inducing epithelial-mesenchymal transition, and TGFB1/TGFBR1 was correlated with an increase in CD8+ T cytopenia and M2 macrophages. Survival analysis showed that simultaneous low expression of TGFBR1 and PD-L1 had better survival results. Through single-cell analysis, we found that TGFB1 is closely related to microglia and macrophages, especially M2 macrophages. Finally, we discussed the sensitivity of TGFB1 inhibitors in gliomas using cell line susceptibility data. These results demonstrated a potential immunotherapy strategy in combination with the TGFB1/TGFBR1 inhibitor and PD-1/PD-L1 inhibitor for grade II and III gliomas.

Analysis of scRNA-seq and bulk RNA-seq demonstrates the effects of EVI2B or CD361 on CD8+ T cells in osteosarcoma

Osteosarcoma (OS) is a common primary malignant tumor of the bone in children and adolescents. The five-year survival rate is estimated to be ~70% based on the currently available treatment modalities. It is well known that tumor-infiltrating immune cells (TIICs) that are the most important components in the tumor microenvironment can exert a killing effect on tumor cells. Therefore, in the present study, 85 RNA-sequencing OS samples were categorized into high- and low-immune score groups with ESTIAMATE. Based on the immune score groups, 474 differentially expressed genes (DEGs) were acquired using the LIMMA package of R language. Subsequently, 86 DEGs were taken through univariate COX regression analysis, of which 14 were screened out by least absolute shrinkage and selection operator regression analysis. Furthermore, multivariate COX regression analysis was performed to obtain 4 DEGs. Finally, ecotropic virus integration site 2B (EVI2B) or CD361 gene was screened out via Kaplan-Meier analysis. In addition, CIBERSORT algorithm was used to evaluate the proportion of 22 kinds of TIICs in OS. Correlation analysis revealed that the high expression level of EVI2B can elevate the infiltrated proportion of CD8⁺ T cells. Moreover, analysis of single cell RNA-sequencing transcriptome datasets and immunohistochemical staining uncovered that EVI2B was mainly expressed on CD8⁺ T cells and that EVI2B could promote the expression of granzyme A and K of CD8⁺ T cells to exhibit a potent killing effect on tumor cells. Therefore, EVI2B was identified as a protective immune-related gene and contributed to good prognosis in OS patients.

Infiltration of LPAR5+ macrophages in osteosarcoma tumor microenvironment predicts better outcomes

Introduction

Tumor microenvironment (TME) has been shown to be extensively involved in tumor development. However, the dynamic change of TME components and their effects are still unclear. Here, we attempted to identify TME-related genes that could help predict survival and may be potential therapeutic targets.

Methods

Data was collected from UCSC Xena and GEO database. ESTIMATE and CIBERSORT algorithms were applied to estimate the components and the proportions of TIICs in TME. We analyzed the gene expression differences of immune components and stromal components, respectively, and finally got the overlapped DEGs. Through protein-protein interaction (PPI) network and univariate Cox regression analysis based on shared DEGs, we screened out and validated the TME-related genes. Focusing on this gene, we analyzed the expression and prognostic value of this gene, and investigated its relationship with immune cells by correlation analysis, single cell analysis, immunohistochemistry and immunofluorescence analysis.

Results

Through a series analysis, we found that the proportion of immune and stromal components was an important prognostic factor, and screened out a key gene, LPAR5, which was highly correlated with prognosis and metastasis. And the expression of LPAR5 was positively correlated with immune cells, especially macrophages, indicating LPAR5⁺ macrophages played an important role in tumor microenvironment of osteosarcoma. Meanwhile, the genes in LPAR5 high expression group were enriched in immune-related activities and pathways, and differentially expressed genes between LPAR5⁺ macrophages and LPAR5^- macrophages were enriched in the biological processes associated with phagocytosis and antigen presentation. What' more, we found that LPAR5 was mainly expressed in TME, and high LPAR5 expression predicting a better prognosis.

Conclusion

We identified a TME-related gene, LPAR5, which is a promising indicator for TME remodeling in osteosarcoma. Particularly, LPAR5⁺ macrophages might have great potential to be a prognostic factor and therapeutic target for osteosarcoma.

A Robust Prognostic Signature of Tumor Microenvironment in Colorectal Cancer

Background: Colorectal cancer (CRC) has been a major public health problem. Tumor microenvironment (TME) greatly contributes to the heterogeneity of CRC and is crucial for the regulation of CRC progression. The authors' study aimed to develop a robust prognostic signature for CRC patients based on TME-related genes. Materials and Methods: Gene expression data and clinicopathologic information of CRC patients were collected from Gene Expression Omnibus and The Cancer Genome Atlas databases. TME-related genes with prognostic value were identified by Cox regression and bootstrap method. The authors used the prognostic genes to construct a robust prognostic model using the least absolute shrinkage and selection operator (LASSO) regression method. The immune and stromal cell abundance of CRC samples were estimated by a microenvironment cell populations-counter method. Results: Based on a training set that comprised 893 CRC samples and 4775 TME-related genes, they established a prognostic model consisting of 25 TME-related genes. With specific risk score formulae, the prognostic model divided CRC patients into high-risk and low-risk subgroups with significantly different survival, which were further confirmed in validation cohorts consisting of other 473 CRC cases or subpopulation of specific stages. The result of time-dependent receiver operating characteristic analysis demonstrated strong predictive accuracy of the prognostic model both in training and validation cohorts. Multivariate Cox regression analysis showed that the 25-gene signature was an independent prognostic factor for overall survival, which was validated through clinical subgroups analysis. Further analysis revealed that CRC samples of high-risk group was abundant of stromal-relevant processes and had a significantly higher proportion of fibroblasts and endothelial cells infiltration. Conclusion: The authors established a robust prognostic signature of 25 TME-related genes which may be an effective tool for prognostic prediction and CRC patient stratification to assist in making treatment decisions.

System analysis based on the ER stress-related genes identifies WFS1 as a novel therapy target for colon cancer

BACKGROUND

Colon cancer (COAD) is the third-largest common malignant tumor and the fourth major cause of cancer death in the world. Endoplasmic reticulum (ER) stress has a great influence on cell growth, migration, proliferation, invasion, angiogenesis, and chemoresistance of massive tumors. Although ER stress is known to play an important role in various types of cancer, the prognostic model based on ER stress-related genes (ERSRGs) in colon cancer has not been constructed yet. In this study, we established an ERSRGs prognostic risk model to assess the survival of COAD patients.

METHODS

The COAD gene expression profile and clinical information data of the training set were obtained from the GEO database (GSE40967) and the test set COAD gene expression profile and clinical informative data were downloaded from the TCGA database. The endoplasmic reticulum stress-related genes (ERSRGs) were obtained from Gene Set Enrichment Analysis (GSEA) website. Differentially expressed ERSRGs between normal samples and COAD samples were identified by R "limma" package. Based on the univariate, lasso, and multivariate Cox regression analysis, we developed an ERSRGs prognostic risk model to predict survival in COAD patients. Finally, we verified the function of WFS1 in COAD through in vitro experiments.

RESULTS

We built a 9-gene prognostic risk model based on the univariate, lasso, and multivariate Cox regression analysis. Kaplan-Meier survival analysis and Receiver operating characteristic (ROC) curve revealed that the prognostic risk model has good predictive performance. Subsequently, we screened 60 compounds with significant differences in the estimated half-maximal inhibitory concentration (IC50) between high-risk and low-risk groups. In addition, we found that the ERSRGs prognostic risk model was related to immune cell infiltration and the expression of immune checkpoint molecules. Finally, we determined that knockdown of the expression of WFS1 inhibits the proliferation of colon cancer cells.

CONCLUSIONS

The prognostic risk model we built may help clinicians accurately predict the survival of patients with COAD. Our findings provide valuable insights into the role of ERSRGs in COAD and may provide new targets for COAD therapy.

HSPA8 Is a New Biomarker of Triple Negative Breast Cancer Related to Prognosis and Immune Infiltration

Objective

Triple negative breast cancer (TNBC) is a kind of cancer that endangers the lives of women all over the world in the 21st century. Heat shock protein member 8 (HSPA8) is the chaperone gene of the heat shock protein family. It is involved in many cellular functions. For example, it promotes the circulation between ATP and ADP, participates in protein folding, and can change the vitality of the cell and inhibit its growth. However, the abnormal expression of HSPA8 gene in TNBC and its diagnostic and prognostic significance still need to be further studied.

Methods

First, we used related databases (such as TCGA, GEO, GTEx, ONCOMINE, TIMER2.0, UALCAN, HPA, STRING, CCLE, and Kaplan-Meier plotter databases) to analyze the relationship between HSPA8 and TNBC by bioinformatics. Then, the analysis using only a small part of the experimental work is used to explain our findings. For example, HSPA8 protein expression was evaluated by immunohistochemical method in TNBC tissues. Western blotting experiments were carried out to verify the results. Then, the clinicopathological characteristics of patients with TNBC were analyzed by R software and Cox regression analysis. On the basis, a nomogram is constructed to estimate the 1-, 3-, and 5-year overall survival (OS). The prognostic nomogram performance was calibrated and evaluated by the calibration curve and receiver operating characteristic (ROC) curve.

Results

In the study, we analyzed the three GEO databases (including GSE86945, GSE106977, and GSE102088) and found that HSPA8 is one of the central genes of TNBC. Then, Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) researches indicated that HSPA8 was mainly involved in partner-mediated autophagy, mRNA catabolism, neutrophil activation, immune response, protein targeting, RNA splicing, RNA catabolism, and other biological processes. Next, we used bioinformatics technology to find that the expression level of HSPA8 in breast cancer (BC) and TNBC samples was significantly higher than that in normal breast tissues, which was determined by analyzing hospital patient samples and related experiments. In addition, the expression level of HSPA8 in BC and TNBC samples was significantly correlated with clinical indexes such as TNM stage. The Cox analysis revealed that the expression of HSPA8 in TNBC had significant clinical prognostic value. The results of nomogram and ROC test show that HSPA8 has significant predictive ability in TNBC. The results of immune infiltration of HSPA8 through the TIMER2.0 database showed that there was a significant correlation between HSPA8 and immune cell subsets.

Conclusions

Our results show that the expression of HSPA8 in TNBC has important clinical diagnostic significance and clarify the potential molecular mechanism that promotes the evolution of TNBC. The high expression of HSPA8 may be related with the poor clinical outcome of TNBC. This helps to provide us with a new direction of TNBC targeted therapy.

Tumor-Associated Neutrophils in Colorectal Cancer Development, Progression and Immunotherapy

The colorectal-cancer (CRC) incidence rate and mortality have remained high for several years. In recent years, immune-checkpoint-inhibitor (ICI) therapy has rapidly developed. However, it is only effective in a few CRC patients with microsatellite-instability-high (MSI-H) or mismatch-repair-deficient (dMMR) CRC. How to improve the efficiency of ICI therapy in CRC patients with microsatellite stability (MSS) remains a huge obstacle. Tumor-associated neutrophils (TANs), which are similar to macrophages, also have N1 and N2 phenotypes. They can be recruited and polarized through different cytokines or chemokines, and then play an antitumor or tumor-promoting role. In CRC, we find that the prognostic significance of TANs is still controversial. In this review, we describe the antitumor regulation of TANs, and their mechanism of promoting tumor progression by boosting the transformation of inflammation into tumors, facilitating tumor-cell proliferation, metastasis and angiogenesis. The targeting of TANs combined with ICIs may be a new treatment model for CRC. Relevant animal experiments have shown good responses, and clinical trials have also been carried out in succession. TANs, as “assistants” of ICI treatment, may become the key to the success of CRC immunotherapy, although no significant results have been obtained.

Spindle and Kinetochore-associated Family Genes are Prognostic and Predictive Biomarkers in Hepatocellular Carcinoma

BACKGROUND AND AIMS

Hepatocellular carcinoma (HCC) is one of the most frequent malignant tumors. Spindle and kinetochore-associated (SKA) family genes are essential for the maintenance of the metaphase plate and spindle checkpoint silencing during mitosis. Recent studies have indicated that dysregulation of SKA family genes induces tumorigenesis, tumor progression, and chemoresistance via modulation of cell cycle and DNA replication. However, the differential transcription of SKAs in the context of HCC and its prognostic significance has not been demonstrated.

METHODS

Bioinformatics analyses were performed using TCGA, ONCOMINE, HCCDB, Kaplan-Meier plotter, STRING, GEPIA databases. qRT-PCR, western blot, and functional assays were utilized for in vitro experiments.

RESULTS

We found remarkable upregulation of transcripts of SKA family genes in HCC samples compared with normal liver samples on bioinformatics analyses and in vitro validation. Interaction analysis and enrichment analysis showed that SKA family members were mainly related to microtubule motor activity, mitosis, and cell cycle. Immuno-infiltration analysis showed a correlation of all SKA family genes with various immune cell subsets, especially T helper 2 (Th2) cells. Transcriptional levels of SKA family members were positively associated with histologic grade, T stage, and α-fetoprotein in HCC patients. Receiver operating characteristic curve analysis demonstrated a strong predictive ability of SKA1/2/3 for HCC. Increased expression of these SKAs was associated with unfavorable overall survival, progression-free survival, and disease-specific survival. On Cox proportional hazards regression analyses, SKA1 upregulation and pathological staging were independent predictors of overall survival and disease-specific survival of HCC patients. Finally, clinical tissue microarray validation and in vitro functional assays revealed SKA1 acts an important regulatory role in tumor malignant behavior.

CONCLUSIONS

SKA family members may potentially serve as diagnostic and prognostic markers in the context of HCC. The correlation between SKAs and immune cell infiltration provides a promising research direction for SKA-targeted immunotherapeutics for HCC.

The Role of CD28 and CD8+ T Cells in Keloid Development

Background: A keloid is a benign skin tumor that extends beyond the initial injury area, and its pathologic mechanism remains unclear. Method: High-throughput sequencing data were obtained from normal skin tissue of patients with keloids (Group N) and healthy controls (Group C). Important genes were mined by bioinformatics analysis and identified by RT−qPCR, Western blotting, immunohistochemistry and immunofluorescence assays. The CIBERSORT algorithm was used to convert gene expression information into immune cell information. Flow cytometry was used to verify the key immune cells. Fluorescence-activated cell sorting coculture and CCK8 experiments were used to explore the effect of CD8+ T cells on keloid-associated fibroblasts. Neural network models were used to construct associations among CD28, CD8+ T cells and the severity of keloids and to identify high-risk values. Result: The expression levels of costimulatory molecules (CD28, CD80, CD86 and CD40L) in the skin tissue of patients with keloids were higher than the levels in healthy people (p < 0.05). The number of CD8+ T cells was significantly higher in Group N than in Group C (p < 0.05). The fluorescence intensities of CD28 and CD8+ T cells in Group N were significantly higher than those in Group C (p = 0.0051). The number and viability of fibroblasts cocultured with CD8+ T cells were significantly reduced compared with those of the control (p < 0.05). The expression of CD28 and CD8+ T cells as the input layer may be predictors of the severity of keloids with mVSS as the output layer. The high-risk early warning indicator for CD28 is 10−34, and the high-risk predictive indicator for CD8+ T cells is 13−28. Conclusions: The abnormal expression of costimulatory molecules may lead to the abnormal activation of CD8+ T cells. CD8+ T cells may drive keloid-associated immunosuppression. The expression of CD28 and CD8+ T cells as an input layer may be a predictor of keloid severity. CD28 and CD8+ T cells play an important role in the development of keloids.

m6A Regulator-Based Methylation Modification Patterns Characterized by Distinct Tumor Microenvironment Immune Profiles in Rectal Cancer

Background

Previous studies reported the related role of RNA n6-methyladenosine (m6A) modification in tumorigenesis and development. However, it is not clear whether m6A modification also plays a potential role in the immune regulation of rectal cancer (RC) and the formation of tumor microenvironment.

Methods

In this study, we screened 23 m6A regulatory factors from 369 rectal cancer specimens, further determined the modification patterns of m6A in RC, and systematically linked these modification patterns with the characteristics of TME cell infiltration. The principal component analysis (PCA) algorithm was used to evaluate the m6A modification pattern of a single tumor related to immune response.

Results

Three different m6A modification patterns were found in the measurement results, which are related to different clinical results and biological pathways. TME identification results show that the identified m6A pattern is closely related to immune characteristics. According to the m6Ascore extracted from m6A-related signature genes, RC patients were divided into high and low score subgroups combined with tumor mutation burden. Patients with high tumor mutation burden and higher m6Ascore have a significant survival advantage and enhanced immune infiltration. Further analysis showed that patients with higher m6Ascore had higher PD-L1 expression levels and showed better immune response and lasting clinical benefits.

Conclusions

M6A modification plays a crucial role in the formation of TME diversity and complexity. The evaluation of the m6A modification mode will help us to enhance our understanding of the characteristics of TME infiltration and provide new insights for more effective immunotherapy strategies.

Identification and Validation of a Four-Gene Ferroptosis Signature for Predicting Overall Survival of Lung Squamous Cell Carcinoma

Background

Lung squamous cell carcinoma (LUSC) represents 30% of all non-small cell lung carcinoma. Targeted therapy is not sufficient for LUSC patients because of the low frequency of targeted-effective mutation in LUSC whereas immunotherapy offers more options for patients with LUSC. We explored a ferroptosis-related prognostic signature that can potentially assess the prognosis and immunotherapy efficacy of LUSC patients.

Methods

A total of 502 LUSC patients were downloaded from The Cancer Genome Atlas (TCGA). The external validation data were obtained from the Gene Expression Omnibus (GEO): GSE73403. Then, we identified the candidate genes and constructed the prognostic signature through the Cox survival regression analyses and least absolute shrinkage and selection operator (LASSO). Risk score plot, Kaplan–Meier curve, and ROC curve were used to assess the prognostic power and performance of the model. The CIBERSORT algorithm estimated the fraction of immune cell types. TIDE was utilized to predict the response to immunotherapy. IMvigor210 was used to explore the association between the risk scores and immunotherapy outcomes. A nomogram combined selected clinical characteristics, and the risk scores were constructed.

Results

We screened 132 differentially expressed ferroptosis-related genes. According to KEGG and GO pathway analyses, these genes were mainly engaged in the positive regulation of cytokine production, cytokine metabolic process, and oxidoreductase activity. We then constructed a prognostic model via LASSO regression. The proportions of CD8+ T cells, CD4+ activated T cells, and follicular helper T cells were significantly different between low-risk and high-risk groups. TIDE algorithm indicated that low-risk LUSC patients might profit more from immune checkpoint inhibitors. The predictive value of the ferroptosis gene model in immunotherapy response was further confirmed in IMvigor210. Finally, we combined the clinical characteristics with a LASSO regression model to construct a nomogram that could be easily applied in clinical practice.

Conclusion

We identified a prognostic model that provides an accurate and objective basis for guiding individualized treatment decisions for LUSC.

Comprehensive Analyses of MELK-Associated ceRNA Networks Reveal a Potential Biomarker for Predicting Poor Prognosis and Immunotherapy Efficacy in Hepatocellular Carcinoma

Background: Hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world with high morbidity and mortality. Identifying specific molecular markers that can predict HCC prognosis is extremely important. MELK has been reported to play key roles in several types of human cancers and predict poor prognosis. This study was aimed to explore the impact of MELK on HCC.

Methods: A pan-cancer analysis of MELK was conducted by The Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) data. The prognosis of MELK in various cancers was analyzed in GEPIA. Then, a ceRNA network of MELK was constructed based on the comprehensive consideration of the expression analysis, the correlation analysis, and the survival analysis by R software. The correlation of MELK and immune cell infiltration was analyzed by TIMER and CIBERSORT. Then, the overall survival of differentially expressed immune cells was conducted. The correlation of MELK and immune checkpoints expression was analyzed by GEPIA.

Results: MELK was overexpressed in 14 types of human cancers, and its expression was significantly higher than that in both unmatched and paired normal samples in HCC. Higher MELK expression was correlated with poorer survival and advanced clinical stage, topography (T) stage, and histological grade. The univariate and multivariate Cox regression analyses showed that MELK was an independent risk factor for poor prognosis in HCC. Then, we constructed a ceRNA network consisting of MELK, miR-101-3p, and two lncRNAs (SNHG1 and SNHG6) after evaluating the expression and impact on prognosis in HCC of these RNAs. TIMER and CIBERSORT databases indicated that MELK was correlated with various immune cells including B cells, CD8+ T cells, CD4+ T cells, macrophage, neutrophil, and dendritic cells in HCC. Of them, B cells, CD4+ T cells, macrophage, and neutrophil were related to the prognosis of HCC. In addition, MELK was significantly positively correlated with the immune checkpoint genes.

Conclusions: MELK may be a novel potential biomarker for predicting prognosis and immunotherapy efficacy in patients with HCC. Our study may provide new molecular and therapeutic strategies for the treatment of HCC patients.

Associations of Complete Blood Count Parameters with Disease-Free Survival in Right- and Left-Sided Colorectal Cancer Patients

Colorectal cancer (CRC) is a leading cause of cancer-related deaths worldwide. Some complete blood count (CBC) parameters are found to be associated with CRC prognosis. In this study, ninety-seven pretreated CRC patients were included, and the patients were divided into two groups: left-sided and right-sided, depending on the anatomical location of the tumor. Based on clinicopathologic features including tumor budding, disease stages, and tumor anatomical location, levels of CBC parameters were compared, and disease-free survivals (DFS) were determined. There were differences between patients with different tumor budding scores for only three parameters, including red cell distribution width (RDW), numbers of platelets, and mean platelet volume (MPV). Furthermore, numbers of WBCs, monocytes, and MPV in CRC patients with early disease stages were higher than those with advanced stages. However, levels of eosinophil in CRC patients with advanced stages were higher than those with early stages. Depending on the tumor anatomical location, we observed that numbers of red blood cells (RBCs), hemoglobin (Hgb), and hematocrit (Hct) in CRC patients with left-sided tumors were higher than those with right-sided tumors. We found that low levels of MPV were associated with shorter DFS. However, high levels of eosinophils were associated with shorter DFS in all CRC patients. When patients were divided based on the tumor anatomical location, higher levels of MPV, MCHC, and Hgb were associated with better DFS in the left-sided but not right-sided CRC patients. However, left-sided, but not right-sided, CRC patients with high levels of eosinophil and RDW had shorter DFS. Furthermore, right-sided, but not left-sided, CRC patients with high levels of platelets tended to have a shorter DFS. Our data show that MPV and eosinophils could serve as potential prognostic biomarkers in pre-treatment CRC patients, regardless of the tumor anatomical location. Additionally, lower levels of MPV, MCHC, and Hgb, and high levels of eosinophils and RDW could be negative predictive biomarkers in left-sided CRC patients.

The Landscape of the Tumor-Infiltrating Immune Cell and Prognostic Nomogram in Colorectal Cancer

Tumor-infiltrating immune cells are associated with prognosis and immunotherapy targets in colorectal cancer (CRC). The recently developed CIBERSORT method allows immune cell analysis by deconvolution of high-throughput data onto gene expression. In this study, we analyzed the relative proportions of immune cells in GEO (94 samples) and TCGA (522 samples) CRC data based on the CIBERSORT method. A total of 22 types of tumor-infiltrating immune cells were evaluated. Combined with GEO and TCGA data, it was found that naive B cells, M2 macrophages, and resting mast cells were highly expressed in normal tissues, while M0 macrophages, M1 macrophages, activated mast cells, and neutrophils were highly expressed in tumors. Moreover, we constructed a prognostic model by infiltrating immune cells that showed high specificity and sensitivity in both the training (AUC of 5-year survival = 0.699) and validation (AUC of 5-year survival = 0.844) sets. This provides another basis for clinical prognosis. The results of multiple immunofluorescence detection showed that there were differences in the results of bioinformatics analysis. Neutrophils were highly expressed in normal tissues, and M2 macrophages were highly expressed in tumor tissues. Collectively, our data suggested that infiltrating immune cells in CRC may be an important determinant of prognosis and immunotherapy.

Association of the tissue infiltrated and peripheral blood immune cell subsets with response to radiotherapy for rectal cancer

Background

Tumor microenvironment plays pivotal roles in carcinogenesis, cancer development and metastasis. Composition of cancer immune cell subsets can be inferred by deconvolution of gene expression profile accurately. Compositions of the cell types in cancer microenvironment including cancer infiltrating immune and stromal cells have been reported to be associated with the cancer outcomes markers for cancer prognosis. However, rare studies have been reported on their association with the response to preoperative radiotherapy for rectal cancer.

Methods

In this paper, we deconvoluted the immune/stromal cell composition from the gene expression profiles. We compared the composition of immune/stromal cell types in the RT responsive versus nonresponsive for rectal cancer. We also compared the peripheral blood immune cell subset composition in the stable diseases versus progressive diseases of rectal cancer patients with fluorescence-activated cell sorting from our institution.

Results

Compared with the non-responsive group, the responsive group showed higher proportions of CD4⁺ T cell (0.1378 ± 0.0368 vs. 0.1071 ± 0.0373, p = 0.0215), adipocytes, T cells CD4 memory resting, and lower proportions of CD8⁺ T cell (0.1798 ± 0.0217 vs. 0.2104 ± 0.0415, p = 0.0239), macrophages M2, and preadipocytes in their cancer tissue. The responsive patients showed a higher ratio of CD4⁺/CD8⁺ T cell proportions (mean 0.7869 vs. 0.5564, p = 0.0210). Consistently, the peripheral blood dataset showed higher proportion of CD4⁺ T cells and higher ratio of CD4⁺/CD8⁺ T cells, and lower proportion of CD8⁺ T cells for favorable prognosis. We validated these results with a pooled dataset of GSE3493 and GSE35452, and more peripheral blood data, respectively. Finally, we imported these eight cell features including eosinophils and macrophage M1 to Support Vector Machines and could predict the pre-radiotherapy responsive versus non-responsive with an accuracy of 76%, ROC AUC 0.77, 95% confidential interval of 0.632–0.857, better than the gene signatures.

Conclusions

Our results showed that the proportions of tumor-infiltrating subsets and peripheral blood immune cell subsets can be important immune cell markers and treatment targets for outcomes of radiotherapy for rectal cancer.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01252-6.

High-Grade Endometrial Stromal Sarcoma: Molecular Alterations and Potential Immunotherapeutic Strategies

Endometrial stromal tumor (EST) is an uncommon and unusual mesenchymal tumor of the uterus characterized by multicolored histopathological, immunohistochemical, and molecular features. The morphology of ESTs is similar to normal endometrial stromal cells during the proliferative phase of the menstrual cycle. ESTs were first classified into benign and malignant based on the number of mitotic cells. However, recently WHO has divided ESTs into four categories: endometrial stromal nodules (ESN), undifferentiated uterine sarcoma (UUS), low-grade endometrial stromal sarcoma (LG-ESS), and high-grade endometrial stromal sarcoma (HG-ESS). HG-ESS is the most malignant of these categories, with poor clinical outcomes compared to other types. With advances in molecular biology, ESTs have been further classified with morphological identification. ESTs, including HG-ESS, is a relatively rare type of cancer, and the therapeutics are not being developed compared to other cancers. However, considering the tumor microenvironment of usual stromal cancers, the advance of immunotherapy shows auspicious outcomes reported in many different stromal tumors and non-identified uterine cancers. These studies show the high possibility of successful immunotherapy in HG-ESS patients in the future. In this review, we are discussing the background of ESTs and the BCOR and the development of HG-ESS by mutations of BCOR or other related genes. Among the gene mutations of HG-ESSs, BCOR shows the most common mutations in different ways. In current tumor therapies, immunotherapy is one of the most effective therapeutic approaches. In order to connect immunotherapy with HG-ESS, the understanding of tumor microenvironment (TME) is required. The TME of HG-ESS shows the mixture of tumor cells, vessels, immune cells and non-malignant stromal cells. Macrophages, neutrophils, dendritic cells and natural killer cells lose their expected functions, but rather show pro-tumoral functions by the matricellular proteins, extracellular matrix and other complicated environment in TME. In order to overcome the current therapeutic limitations of HG-ESS, immunotherapies should be considered in addition to the current surgical strategies. Checkpoint inhibitors, cytokine-based immunotherapies, immune cell therapies are good candidates to be considered as they show promising results in other stromal cancers and uterine cancers, while less studied because of the rarity of ESTs. Based on the advance of knowledge of immune therapies in HG-ESS, the new strategies can also be applied to the current therapies and also in other ESTs.

A Pyroptosis-Related Gene Panel for Predicting the Prognosis and Immune Microenvironment of Cervical Cancer

Cervical cancer (CC) is one of the most common malignant tumors of the female reproductive system. And the immune system disorder in patients results in an increasing incidence rate and mortality rate. Pyroptosis is an immune system-related programmed cell death pathway that produces systemic inflammation by releasing pro-inflammatory intracellular components. However, the diagnostic significance of pyroptosis-related genes (PRGs) in CC is still unclear. Therefore, we identified 52 PRGs from the TCGA database and screened three Differentially Expressed Pyroptosis-Related Genes (DEPRGs) in the prognosis of cervical cancer: CHMP4C, GZMB, TNF. The least absolute shrinkage and selection operator (LASSO) regression analysis and multivariate COX regression analysis were then used to construct a gene panel based on the three prognostic DEPRGs. The patients were divided into high-and low-risk groups based on the median risk score of the panel. According to the Kaplan-Meier curve, there was a substantial difference in survival rates between the two groups, with the high-risk group’s survival rate being significantly lower than the low-risk group’s. The PCA and t-SNE analyses revealed that the panel was able to differentiate patients into high-and low-risk groups. The area under the ROC curve (AUC) shows that the prognostic panel has high sensitivity and specificity. The risk score could then be employed as an independent prognostic factor using univariate and multivariate COX regression analyses paired with clinical data. The analyses of GO and KEGG functional enrichment of differentially expressed genes (DEGs) in the high-and low-risk groups revealed that these genes were primarily engaged in immune response and inflammatory cell chemotaxis. To illustrate immune cell infiltration in CC patients further, we used ssGSEA to compare immune-related cells and immune pathway activation between the high-and low-risk groups. The link between three prognostic DEPRGs and immune-related cells was still being discussed after evaluating immune cell infiltration in the TCGA cohort with “CIBERSORT.” In addition, the GEPIA database and qRT-PCR analysis were used to verify the expression levels of prognostic DEPRGs. In conclusion, PRGs are critical in tumor immunity and can be utilized to predict the prognosis of CC.

Dissection of Immune Profiles in Microsatellite Stable and Low Microsatellite Instability Colon Adenocarcinoma by Multiomics Data Analysis

Background

Although microsatellite instability (MSI) is an indicator for active immunotherapy response, only 15% of colon adenocarcinoma (COAD) patients are with MSI. An investigation into the immune profiles in low MSI (MSI-L) and microsatellite stable (MSS) COAD remains lacking, whereas such exploration may provide new insights into COAD immunity.

Methods

We hierarchically clustered MSI-L/MSS COAD based on the enrichment levels of 28 immune signatures to identify its immune-specific subtypes. We also comprehensively compared molecular and clinicopathologic profiles among these subtypes.

Results

We identified three immune subtypes of MSI-L/MSS COAD (IM-H, IM-M, and IM-L), which had high, medium, and low immune signature scores, respectively. We demonstrated that this subtyping method was reproducible and predictable by analyzing five different datasets, including four bulk tumor datasets and one single-cell dataset. IM-H was characterized by high immunity, high stemness, strong potential of proliferation, invasion and metastasis, epithelial-mesenchymal transition, elevated expression of oncogenic pathways, low tumor purity, low intratumor heterogeneity (ITH), genomic instability, inferior response to chemotherapy, and unfavorable prognosis. IM-M was characterized by the highest ratio of immunostimulatory to immunosuppressive signatures, the best response to chemotherapy, and favorable prognosis. IM-L was characterized by low immunity, high tumor purity, high ITH, and genomic stability.

Conclusion

The immune-specific subtyping of MSI-L/MSS COAD may provide new insights into the tumor immunity as well as clinical implications for immunotherapy of the COAD patients who lack MSI.

Identification of diagnostic markers and immune cell infiltration characteristics in antineutrophil cytoplasmic antibody-associated vasculitis by weighted gene co-expression network analysis

Background

Antineutrophil cytoplasmic antibody-associated vasculitis (AAV) is a group of life-threatening systemic autoimmune diseases. The aim of this study was to determine the relationship between the AAV hub gene and immune cell infiltration, and its value for clinical disease treatment.

Methods

We downloaded the microarray information of 37 AAV patients and 27 controls from Gene Expression Omnibus (GEO). Genes were classified into totally different modules exploitation weighted gene co-expression network analysis (WGCNA). AAV diagnostic indicators were screened and then assessed immune cell infiltration by the least absolute shrinkage and selection operator (LASSO) and CIBERSORT. Finally, Connectivity Map analysis was applied to predict possible AAV glomerulus injury improvement therapies.

Results

WGCNA was developed and differentially expressed genes were classified into 6 modules, the black module was most tightly correlated to AAV. Among them, TIMP1 and FCER1G were most closely related to clinical features. Resting mast cells and monocytes emerged as having the foremost distinguished variations in AAV. C3AR1 and FCER1G were involved in AAV development by immune regulation. Connectivity Map analysis indicated the most significant compound was fisetin.

Conclusions

The present study is that the initial to spot immune cell infiltration with microarray data of glomeruli in AAV, which provides novel proof and clues for additional analysis of the molecular mechanisms.

Metabolomic Profiling Reveals That 5-Hydroxylysine and 1-Methylnicotinamide Are Metabolic Indicators of Keloid Severity

Background: Keloid is a skin fibroproliferative disease with unknown pathogenesis. Metabolomics provides a new perspective for revealing biomarkers related to metabolites and their metabolic mechanisms. Method: Metabolomics and transcriptomics were used for data analysis. Quality control of the data was performed to standardize the data. Principal component analysis (PCA), PLS-DA, OPLS-DA, univariate analysis, CIBERSORT, neural network model, and machine learning correlation analysis were used to calculate differential metabolites. The molecular mechanisms of characteristic metabolites and differentially expressed genes were identified through enrichment analysis and topological analysis. Result: Compared with normal tissue, lipids have a tendency to decrease in keloids, while peptides have a tendency to increase in keloids. Significantly different metabolites between the two groups were identified by random forest analysis, including 1-methylnicotinamide, 4-hydroxyproline, 5-hydroxylysine, and l-prolinamide. The metabolic pathways which play important roles in the pathogenesis of keloids included arachidonic acid metabolism and d-arginine and d-ornithine metabolism. Metabolomic profiling reveals that 5-hydroxylysine and 1-methylnicotinamide are metabolic indicators of keloid severity. The high-risk early warning index for 5-hydroxylysine is 4 × 10⁸-6.3×10⁸ (p = 0.0008), and the high-risk predictive index for 1-methylnicotinamide is 0.95 × 10⁷-1.6×10⁷ (p = 0.0022). Conclusion: This study was the first to reveal the metabolome profile and transcriptome of keloids. Differential metabolites and metabolic pathways were calculated by machine learning. Metabolomic profiling reveals that 5-hydroxylysine and 1-methylnicotinamide may be metabolic indicators of keloid severity.

Immune Infiltration in Gastric Cancer Microenvironment and Its Clinical Significance

Immunotherapy has developed rapidly and has gradually become one of the important methods for treatment of gastric cancer (GC). The research on tumor infiltrating immune cells (TIICs) and immune-related genes in the tumor microenvironment (TME) greatly encourages the development of immunotherapy. The devolution algorithm (CIBERSORT) was applied to infer the proportion of 22 TIICs based on gene expression profiles of GC tissues, which were downloaded from TCGA and GEO. TCGA was utilized to analyze the differential expression of immune-related genes, and explore the potential molecular functions of these genes. We have observed the enrichment of multiple TIICs in microenvironment of GC. Some of these cells were closely related to tumor mutational burden (TMB), microsatellite instability (MSI), Fuhrman grade, and TNM staging. Survival analysis showed that the infiltration level of CD8+ T cells, activated CD4+ memory T cells and M2 macrophages were significantly related to the prognosis of GC patients. The functional enrichment analysis of immune-related genes revealed that these genes were mainly associated with cytokine activation and response. Four significant modules were screened by PPI network and 20 key genes were screened from the modules. The expression levels of CALCR and PTH1R are strikingly related to the expression of immune checkpoint and the prognosis of GC patients. The type and number of TIICs in microenvironment of GC, as well as immune-related genes are closely related to tumor progression, and can be used as important indicators for patient prognosis assessment.

Tumour cell apoptosis modulates the colorectal cancer immune microenvironment via interleukin-8-dependent neutrophil recruitment

Sporadic apoptosis of tumour cells is a commonly observed feature of colorectal cancer (CRC) and strongly correlates with adverse patient prognosis. The uptake of apoptotic cell debris by neutrophils induces a non-inflammatory, pro-regenerative, and hence potentially pro-tumorigenic phenotype. In this study, we therefore sought to investigate the impact of apoptotic CRC cells on neutrophils and its consequence on other immune cells of the tumour microenvironment. Apoptosis induced by combined TNFα-treatment and UV-C irradiation, as well as various chemotherapeutic agents, led to a substantial release of neutrophil-attracting chemokines, most importantly interleukin-8 (IL-8), in both primary patient-derived and established CRC cells. Accordingly, conditioned media of apoptotic tumour cells selectively stimulated chemotaxis of neutrophils, but not T cells or monocytes. Notably, caspase-inhibition partially reduced IL-8 secretion, suggesting that caspase activity might be required for apoptosis-induced IL-8 release. Moreover, apoptotic tumour cell-conditioned media considerably prolonged neutrophil lifespan and induced an activated CD66b^highCD11b^highCD62L^low phenotype, comparable to that of tumour-associated neutrophils in CRC patients, as assessed by flow cytometry of dissociated CRC tissues. Immunohistochemical analyses of 35 CRC patients further revealed a preferential accumulation of neutrophils at sites of apoptotic tumour cells defined by the expression of epithelial cell-specific caspase-cleaved cytokeratin-18. The same areas were also highly infiltrated by macrophages, while T cells were virtually absent. Notably, neutrophils induced an M2-like CD86^lowCD163⁺CD206⁺ phenotype in co-cultured monocyte-derived macrophages and suppressed LPS-induced pro-inflammatory cytokine release. In an in vitro transwell model, IL-8 blockade efficiently prevented neutrophil-induced anti-inflammatory macrophage polarisation by inhibiting neutrophil migration towards IL-8 gradients generated by apoptotic CRC cells. To conclude, our data suggest that apoptotic cancer cells release chemotactic factors that attract neutrophils into the tumour, where their interaction with neighbouring macrophages might promote an immunologically unfavourable tumour microenvironment. This effect may contribute to tumour recurrence after chemotherapy-induced apoptosis.

SPARCL1 Is a Novel Prognostic Biomarker and Correlates with Tumor Microenvironment in Colorectal Cancer

Background

Secreted protein acidic and rich in cysteine-like 1 (SPARCL1) plays an important role in tumor pathogenesis. We aim to evaluate the clinical significance and potential biological roles of SPARCL1 in colorectal cancer (CRC).

Methods

Datasets from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases were downloaded to evaluate the expression levels of SPARCL1 in CRC. Receiver operating characteristic (ROC) curve was constructed to evaluate the diagnostic value of SPARCL1. Then, comprehensive database search was conducted for published clinical studies to explore clinical significance of SPARCL1. In addition, coexpression genes of SPARCL1 were identified through the cBioPortal database and enrichment analysis of SPARCL1 and its coexpression genes were performed by the “clusterProfiler” R package. Finally, the correlations between SPARCL1 and tumor microenvironment scores, tumor-infiltrating immune cells in CRC were determined by “ESTIMATE” and “GSVA” R packages.

Results

SPARCL1 was significantly downregulated in CRC tissues, and SPARCL1 showed high accuracy for diagnosis of primary CRC in both GEO and TCGA datasets. Pooled results from published clinical studies showed SPARCL1 expression was associated with differentiation (OR = 1.89, 95% CI: 1.38-2.59), tumor stage (OR = 0.47, 95% CI: 0.29-0.77), distant metastasis (OR = 0.53, 95% CI: 0.33-0.84), and overall survival (HR = 0.56, 95% CI: 0.43-0.74). SPARCL1 and its top 300 coexpression genes were involved in several KEGG pathways, such as focal adhesion, cell adhesion molecules, PI3K-Akt signaling pathway, cGMP-PKG signaling pathway, and ECM-receptor interaction. Besides, the SPARCL1 expression was significantly correlated with stromal score, immune score, ESTIMATE score, and diverse immune cells.

Conclusion

SPARCL1 significantly correlated with clinicopathological features and tumor microenvironment in CRC.

Identification of key biomarkers in steroid-induced osteonecrosis of the femoral head and their correlation with immune infiltration by bioinformatics analysis

Objective

This study aimed to identify key diagnostic markers and immune infiltration of (SONFH) by bioinformatics analysis.

Methods

Related SONFH datasets were downloaded from the Gene Expression Omnibus (GEO) database. First, we identified the differentially expressed genes (DEGs) and performed the functional enrichment analysis. Then weighted correlation network analysis (WGCNA) and the MCODE plug-in in Cytoscape were used to identify the diagnostic markers of SONFH. Finally, CIBERSORT was used to analyze the immune infiltration between SONFH and healthy controls, and the correlation between infiltrating immune cells and diagnostic markers was analyzed.

Results

TYROBP, TLR2, P2RY13, TLR8, HCK, MNDA, and NCF2 may be key diagnostic markers of SONFH. Immune cell infiltration analysis revealed that Memory B cells and activated dendritic cells may be related to the SONFH process. Moreover, HCK was negatively correlated with CD8 T cells, and neutrophils were positively correlated with those key diagnostic markers.

Conclusions

TYROBP, TLR2, P2RY13, TLR8, HCK, MNDA, and NCF2 may be used as diagnostic markers of SONFH, and immune-related mechanism of SONFH and the potential immunotherapy are worthy of further study.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12891-022-04994-7.

Analysis of the Mechanism of RAD18 in Glioma

INTRODUCTION

This study aimed to evaluate the regulatory mechanism of RAD18 in glioma development.

METHODS

RAD18 expression was compared in glioma tumors and normal samples. Furthermore, we investigated the association between gene transcription and clinical factors in glioma samples, followed by functional enrichment analysis, screening for key Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways, immune infiltration analysis of high and low RAD18 expression groups, and correlation analysis of quantified KEGG signaling pathways and immune cell types.

RESULTS

The expression of RAD18 was upregulated in gliomas. Moreover, RAD18 expression was significantly correlated with age, tumor grade, and histological subtype. Notably, patients with gliomas with high RAD18 expression levels had worse overall survival. Functional enrichment analysis showed that RAD18 was significantly related to biological processes, such as cell division, chemical synaptic transmission, and mitotic nuclear division, and KEGG pathways such as cell cycle, oxidative phosphorylation, and extracellular matrix (ECM)-receptor interaction. The infiltration of five immune cells (plasma B cells, naive B cells, resting CD4+ memory T cells, monocytes, and M1 macrophages) was significantly different between the high and low RAD18 expression groups, and this difference was significantly related to key KEGG pathways, such as neuroactive ligand-receptor interaction and ECM-receptor interaction.

CONCLUSION

RAD18 may serve as a target for glioma treatment and as a key regulator of glioma development.

CRYAB predicts clinical prognosis and is associated with immunocyte infiltration in colorectal cancer

Background

αB-Crystallin (CRYAB) is differentially expressed in various tumors. However, the correlation between CRYAB and immune cell infiltration in colorectal cancer (CRC) remains unclear.

Materials & Methods

Kaplan-Meier survival curves in The Cancer Genome Atlas (TCGA) were used to evaluate the relationship between CRYAB expression and both overall survival and progression-free survival. The relationships between CRYAB expression and infiltrating immune cells and their corresponding gene marker sets were examined using the TIMER database.

Results

The expression of CRYAB was lower in CRC tumor tissues than in normal tissues (P < 0.05). High CRYAB gene expression and high levels of CRYAB gene methylation were correlated with high-grade malignant tumors and more advanced tumor, nodes and metastasis (TNM) cancer stages. In addition, in colorectal cancer, there was a positive correlation between CRYAB expression and immune infiltrating cells including neutrophils, macrophages, CD8 + T cells, and CD4 + T cells, as well as immune-related genes including CD2, CD3D, and CD3E. Methylation sites such as cg13084335, cg15545878, cg13210534, and cg15318568 were positively correlated with low expression of CRYAB.

Conclusion

Because CRYAB likely plays an important role in immune cell infiltration, it may be a potential tumor-suppressor gene in CRC and a potential novel therapeutic target and predictive biomarker for colorectal cancer (CRC).

Proteomics in support of immunotherapy: contribution to model-based precision medicine

INTRODUCTION

Proteomics encompasses a wide and expanding range of methods to identify, characterize, and quantify thousands of proteins from a variety of biological samples, including blood samples, tumors, and tissues. Such methods are supportive of various forms of immunotherapy applied to chronic conditions such as allergies, autoimmune diseases, cancers, and infectious diseases.

AREAS COVERED

In support of immunotherapy, proteomics based on mass spectrometry has multiple specific applications related to (i) disease modeling and patient stratification, (ii) antigen/ autoantigen/neoantigen/ allergen identification, (iii) characterization of proteins and monoclonal antibodies used for immunotherapeutic or diagnostic purposes, (iv) identification of biomarkers and companion diagnostics and (v) monitoring by immunoproteomics of immune responses elicited in the course of the disease or following immunotherapy.

EXPERT OPINION

Proteomics contributes as an enabling technology to an evolution of immunotherapy toward a precision medicine approach aiming to better tailor treatments to patients' specificities in multiple disease areas. This trend is favored by a better understanding through multi-omics profiling of both the patient's characteristics, his/her immune status as well as of the features of the immunotherapeutic drug.

Exploration of genes and tumor infiltrating lymphocytes in female lung adenocarcinoma microenvironment that predicted prognosis

The tumor microenvironment has an important impact on tumor growth, invasion, metastasis, anti-tumor immune tolerance, and prognosis. The present study aimed to explore female lung adenocarcinoma microenvironment-associated tumor infiltrating lymphocytes (TILs) and genes that predict prognosis in The Cancer Genome Atlas (TCGA) database. Gene expression profiles of female patients with lung adenocarcinoma were downloaded from TCGA. Base on the CIBERSORT algorithm, we determined the fractions of TILs. By applying the ESTIMATE algorithm, immune scores and stromal scores were derived. According to the immune and stromal scores, we categorized the female patients with lung adenocarcinoma into high and low score groups. We also identified the fractions of TILs and differentially expressed genes (DEGs) that were significantly related with prognosis. The proportion of M1 macrophages was significantly negatively related to overall survival in female patients with lung adenocarcinoma. There were 269 upregulated genes and 35 downregulated genes both in immune scores and stromal scores. PTPRC (protein tyrosine phosphatase receptor type C) and GIMAP6 (GTPase, IMAP family member 6) were not only hub genes, but also were significantly related to overall survival in the Kaplan-Meier Plotter online and TCGA databases. In summary, our study provided new insight into the tumor microenvironment-related cellular and molecular mechanisms of women with lung adenocarcinoma. The results will be useful for future clinical studies.

PDGFRB is a potential prognostic biomarker and correlated with immune infiltrates in gastric cancer

Gastric cancer (GC) is a common cancer with high mortality and morbidity rates worldwide. Although medical and surgical treatments have improved, the mechanisms of the progression of GC remain unclear. Platelet-derived growth factor receptor-β (PDGFRB) plays a pivotal role in angiogenesis and tumor cell proliferation and has been suggested as a prognostic marker of cancer. This study aimed to explore the relationship of PDGFRB expression with clinicopathologic characteristics, immune cell infiltration status, and prognosis in GC. In this study, we visualized the expression and prognostic values of PDGFRB in GC using the Oncomine, UALCAN, GEPIA, and Kaplan-Meier Plotter databases. And then we explored the potential relationships between PDGFRB expression and the levels of immune cell infiltration using the TIMER, GEPIA databases and CIBERSORT algorithm. Furthermore, LinkedOmics analysis was performed to explore the functions for PDGFRB. The results showed close correlations between PDGFRB and immune cell infiltration especially M2 Macrophage infiltration in GC. High PDGFRB expression was related to poor outcomes in GC. High PDGFRB expression can negatively affect GC prognosis by promoting angiogenesis and modulating the tumor immune microenvironment. These results strongly suggest that PDGFRB can be used as a prognostic biomarker of GC and provide novel insights into possible immunotherapeutic targets.

CD40LG as a Prognostic Molecular Marker Regulates Tumor Microenvironment Through Immune Process in Breast Cancer

Purpose

Breast cancer (BRCA) is the second most common malignancy in the world and the most common in women. Here, we utilized publicly available BRCA dataset to investigate potential prognosis-related genes through integrated bioinformatics analysis.

Materials and Methods

BRCA dataset was obtained from the Cancer Genome Atlas (TCGA) database. The ESTIMATE algorithm was used to calculate the ImmuneScores and StromalScores of the samples and then divided them into high- and low-score groups based on the median score. Common differentially expressed genes (DEGs) were identified through differential expression analysis. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed. The core prognostic genes were the intersection of hub genes from PPI network and prognostic genes from univariate Cox proportional hazard regression analysis. Finally, the CIBERSORT algorithm was used to calculate proportions of 22 tumor-infiltrating immune cells (TICs) in BRCA samples.

Results

A total of 486 DEGs were identified. These genes were mainly enriched in immune-related pathways. Crossover genes between the hub genes and the prognostic genes were CD2 and CD40LG. CD40LG was further investigated in this study. CD40LG was downregulated in BRCA samples compared with normal samples, and a lower CD40LG expression was associated with advanced tumor stages and a poor prognosis. CD40LG was shown to be involved in immune-related pathways of BRCA by Gene Set Enrichment Analysis. Finally, 14 TICs were found to have a close relationship with CD40LG.

Conclusion

CD40LG was found to be a core prognostic gene related to tumor microenvironment and deeply involved in immune-related pathways in BRCA. Our findings may provide new insights for exploring the molecular mechanisms of BRCA and developing new immunotherapies for the disease.