Identification and validation of an immune-related gene signature predictive of overall survival in colon cancer

Action of m6A-related gene signatures on the prognosis and immune microenvironment of colonic adenocarcinoma

N6-methyladenosine (m6A) modification in human tumor cells exerts considerable influence on crucial processes like tumorigenesis, invasion, metastasis, and immune response. This study aims to comprehensively analyze the impact of m6A-related genes on the prognosis and immune microenvironment (IME) of colonic adenocarcinoma (COAD). Public data sources, predictive algorithms identified m6A-related genes and differential gene expression in COAD. Subtype analysis and assessment of immune cell infiltration patterns were performed using consensus clustering and the CIBERSORT algorithm. The Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis determined gene signatures. Independent prognostic factors were identified using univariate and multivariate Cox proportional hazards models. The findings indicate that 206 prognostic m6A-related DEGs contribute to the m6A regulatory network along with 8 m6A enzymes. Based on the expression levels of these genes, 438 COAD samples from The Cancer Genome Atlas (TCGA) were classified into 3 distinct subtypes, showing marked differences in survival prognosis, clinical characteristics, and immune cell infiltration profiles. Subtype 3 and 2 displayed reduced levels of infiltrating regulatory T cells and M0 macrophages, respectively. A six-gene signature, encompassing KLC3, SLC6A15, AQP7 JMJD7, HOXC6, and CLDN9, was identified and incorporated into a prognostic model. Validation across TCGA and GSE39582 datasets exhibited robust predictive specificity and sensitivity in determining the survival status of COAD patients. Additionally, independent prognostic factors were recognized, and a nomogram model was developed as a prognostic predictor for COAD. In conclusion, the six target genes governed by m6A mechanisms offer substantial potential in predicting COAD outcomes and provide insights into the unique IME profiles associated with various COAD subtypes.

Downstream STING pathways IRF3 and NF-κB differentially regulate CCL22 in response to cytosolic dsDNA

Double-stranded DNA (dsDNA) in the cytoplasm of eukaryotic cells is abnormal and typically indicates the presence of pathogens or mislocalized self-DNA. Multiple sensors detect cytosolic dsDNA and trigger robust immune responses via activation of type I interferons. Several cancer immunotherapy treatments also activate cytosolic nucleic acid sensing pathways, including oncolytic viruses, nucleic acid-based cancer vaccines, and pharmacological agonists. We report here that cytosolic dsDNA introduced into malignant cells can robustly upregulate expression of CCL22, a chemokine responsible for the recruitment of regulatory T cells (Tregs). Tregs in the tumor microenvironment are thought to repress anti-tumor immune responses and contribute to tumor immune evasion. Surprisingly, we found that CCL22 upregulation by dsDNA was mediated primarily by interferon regulatory factor 3 (IRF3), a key transcription factor that activates type I interferons. This finding was unexpected given previous reports that type I interferon alpha (IFN-α) inhibits CCL22 and that IRF3 is associated with strong anti-tumor immune responses, not Treg recruitment. We also found that CCL22 upregulation by dsDNA occurred concurrently with type I interferon beta (IFN-β) upregulation. IRF3 is one of two transcription factors downstream of the STimulator of INterferon Genes (STING), a hub adaptor protein through which multiple dsDNA sensors transmit their signals. The other transcription factor downstream of STING, NF-κB, has been reported to regulate CCL22 expression in other contexts, and NF-κB has also been associated with multiple pro-tumor functions, including Treg recruitment. However, we found that NF-κB in the context of activation by cytosolic dsDNA contributed minimally to CCL22 upregulation compared with IRF3. Lastly, we observed that two strains of the same cell line differed profoundly in their capacity to upregulate CCL22 and IFN-β in response to dsDNA, despite apparent STING activation in both cell lines. This finding suggests that during tumor evolution, cells can acquire, or lose, the ability to upregulate CCL22. This study adds to our understanding of factors that may modulate immune activation in response to cytosolic DNA and has implications for immunotherapy strategies that activate DNA sensing pathways in cancer cells.

Identification of novel T cell proliferation patterns, potential biomarkers and therapeutic drugs in colorectal cancer

Background: T cells are crucial components of antitumor immunity. A list of genes associated with T cell proliferation was recently identified; however, the impact of T cell proliferation-related genes (TRGs) on the prognosis and therapeutic responses of patients with colorectal cancer (CRC) remains unclear. Methods: 33 TRG expression information and clinical information of patients with CRC gathered from multiple datasets were subjected to bioinformatic analysis. Consensus clustering was used to determine the molecular subtypes associated with T cell proliferation. Utilizing the Lasso-Cox regression, a predictive signature was created and verified in external cohorts. A tumor immune environment analysis was conducted, and potential biomarkers and therapeutic drugs were identified and confirmed via in vitro and in vivo studies. Results: CRC patients were separated into two TRG clusters, and differentially expressed genes (DEGs) were identified. Patient information was divided into three different gene clusters, and the determined molecular subtypes were linked to patient survival, immune cells, and immune functions. Prognosis-associated DEGs in the three gene clusters were used to evaluate the risk score, and a predictive signature was developed. The ability of the risk score to predict patient survival and treatment response has been successfully validated using multiple datasets. To discover more possible biomarkers for CRC, the weighted gene co-expression network analysis algorithm was utilized to screen key TRG variations between groups with high- and low-risk. CDK1, BATF, IL1RN, and ITM2A were screened out as key TRGs, and the expression of key TRGs was confirmed using real-time reverse transcription polymerase chain reaction. According to the key TRGs, 7,8-benzoflavone was identified as the most significant drug molecule, and MTT, colony formation, wound healing, transwell assays, and in vivo experiments indicated that 7,8-benzoflavone significantly suppressed the proliferation and migration of CRC cells. Conclusion: T cell proliferation-based molecular subtypes and predictive signatures can be utilized to anticipate patient results, immunological landscape, and treatment response in CRC. Novel biomarker candidates and potential therapeutic drugs for CRC were identified and verified using in vitro and in vivo tests.

ZNF765 is a prognostic biomarker of hepatocellular carcinoma associated with cell cycle, immune infiltration, m6A modification, and drug susceptibility

Hepatocellular carcinoma (HCC) is an ongoing challenge worldwide. Zinc finger protein 765 (ZNF765) is an important zinc finger protein that is related to the permeability of the blood-tumor barrier. However, the role of ZNF765 in HCC is unclear. This study evaluated the expression of ZNF765 in hepatocellular carcinoma and the impact of its expression on patient prognosis based on The Cancer Genome Atlas (TCGA). Immunohistochemical assays (IHC) were used to examine protein expression. Besides, a colony formation assay was used to examine cell viability. We also explored the relationship between ZNF765 and chemokines in the HCCLM3 cells by qRT-PCR. Moreover, we examined the effect of ZNF765 on cell resistance by measurement of the maximum half-inhibitory concentration. Our research revealed that ZNF765 expression in HCC samples was higher than that in normal samples, whose upregulation was not conducive to the prognosis. The results of GO, KEGG, and GSEA showed that ZNF765 was associated with the cell cycle and immune infiltration. Furthermore, we confirmed that the expression of ZNF765 had a strong connection with the infiltration level of various immune cells, such as B cells, CD4+ T cells, macrophages, and neutrophils. In addition, we found that ZNF765 was associated with m6A modification, which may affect the progression of HCC. Finally, drug sensitivity testing found that patients with HCC were sensitive to 20 drugs when they expressed high levels of ZNF765. In conclusion, ZNF765 may be a prognostic biomarker related to cell cycle, immune infiltration, m6A modification, and drug sensitivity for hepatocellular carcinoma.

An N6-methyladenosine regulation- and mRNAsi-related prognostic index reveals the distinct immune microenvironment and immunotherapy responses in lower-grade glioma

BACKGROUND

N6-methyladenosine (m6A) modification is involved in tumorigenesis and progression as well as closely correlated with stem cell differentiation and pluripotency. Moreover, tumor progression includes the acquisition of stemness characteristics and accumulating loss of differentiation phenotype. Therefore, we integrated m6A modification and stemness indicator mRNAsi to classify patients and predict prognosis for LGG.

METHODS

We performed consensus clustering, weighted gene co-expression network analysis, and least absolute shrinkage and selection operator Cox regression analysis to identify an m6A regulation- and mRNAsi-related prognostic index (MRMRPI). Based on this prognostic index, we also explored the differences in immune microenvironments between high- and low-risk populations. Next, immunotherapy responses were also predicted. Moreover, single-cell RNA sequencing data was further used to verify the expression of these genes in MRMRPI. At last, the tumor-promoting and tumor-associated macrophage polarization roles of TIMP1 in LGG were validated by in vitro experiments.

RESULTS

Ten genes (DGCR10, CYP2E1, CSMD3, HOXB3, CABP4, AVIL, PTCRA, TIMP1, CLEC18A, and SAMD9) were identified to construct the MRMRPI, which was able to successfully classify patients into high- and low-risk group. Significant differences in prognosis, immune microenvironment, and immunotherapy responses were found between distinct groups. A nomogram integrating the MRMRPI and other prognostic factors were also developed to accurately predict prognosis. Moreover, in vitro experiments illustrated that inhibition of TIMP1 could inhibit the proliferation, migration, and invasion of LGG cells and also inhibit the polarization of tumor-associated macrophages.

CONCLUSION

These findings provide novel insights into understanding the interactions of m6A methylation regulation and tumor stemness on LGG development and contribute to guiding more precise immunotherapy strategies.

Peptide-based PET imaging agent of tumor TIGIT expression

BACKGROUND

Accumulating studies have demonstrated that elevated TIGIT expression in tumor microenvironment correlates with better therapeutic response to TIGIT-based immunotherapy in pre-clinical studies. Therefore, a non-invasive method to detect tumor TIGIT expression is crucial to predict the therapeutic effect.

METHODS

In this study, a peptide-based PET imaging agent, ⁶⁸Ga-DOTA-^DTBP-3, was developed to non-invasively detect TIGIT expression by micro-PET in tumor-bearing BALB/c mice. ^DTBP-3, a D-peptide comprising of 12 amino acids, was radiolabeled with ⁶⁸Ga through a DOTA chelator. In vitro studies were performed to evaluate the affinity of ⁶⁸Ga-DOTA-^DTBP-3 to TIGIT and its stability in fetal bovine serum. In vivo studies were assessed by micro-PET, biodistribution, and immunohistochemistry on tumor-bearing BALB/c mice.

RESULTS

The in vitro studies showed the equilibrium dissociation constant of ⁶⁸Ga-DOTA-^DTBP-3 for TIGIT was 84.21 nM and its radiochemistry purity was 89.24 ± 1.82% in FBS at 4 h in room temperature. The results of micro-PET, biodistribution and immunohistochemistry studies indicated that ⁶⁸Ga-DOTA-^DTBP-3 could be specifically targeted in 4T1 tumor-bearing mice, with a highest uptake at 0.5 h.

CONCLUSION

⁶⁸Ga-DOTA-^DTBP-3 holds potential for non-invasively detect tumor TIGIT expression and for timely assessment of the therapeutic effect of immune checkpoint blockade.

Cancer-Associated Fibroblast Risk Model for Prediction of Colorectal Carcinoma Prognosis and Therapeutic Responses

Colorectal carcinoma (CRC) is a malignant tumor of the digestive system. Cancer-associated fibroblasts (CAFs) are important cellular elements in the tumor microenvironment of CRC, which contribute to CRC progression and immune escape. To predict the survival outcome and therapeutic responses of CRC patients, we identified genes connected with stromal CAF and generated a risk model. In this study, we used multiple algorithms to reveal CAF-related genes in the Gene Expression Omnibus and The Cancer Genome Atlas datasets and construct a risk model composed by prognostic CAF-associated genes. Then, we evaluated whether the risk score could predict CAF infiltrations and immunotherapy in CRC and confirmed the expression of the risk model in CAFs. Our results showed that CRC patients with high CAF infiltrations and stromal score had worse prognosis than those with low-CAF infiltrations and stromal score. We obtained 88 stromal CAF-associated hub-genes and generated a CAF risk model consisting of ZNF532 and COLEC12. Compared with low-risk group, the overall survival in high-risk group was shorter. The relationship between risk score, ZNF532 and COLEC12, and stromal CAF infiltrations and CAF markers was positive. In addition, the effect of immunotherapy in the high-risk group was not as good as that in the low-risk group. Patients with the high-risk group were enriched in chemokine signaling pathway, cytokine-cytokine receptor interaction, and focal adhesion. Finally, we confirmed that the expressions of ZNF532 and COLEC12 in risk model were widely distributed in fibroblasts of CRC, and the expression levels were higher in fibroblasts than CRC cells. In conclusion, the prognostic CAF signature of ZNF532 and COLEC12 can be applied not only to predict the prognosis of CRC patients but also to evaluate the immunotherapy response in CRC patients, and these findings provide the possibility for further development of individualized treatment for CRC.

LAG3 genotype of the donor and clinical outcome after allogeneic transplantation from HLA-identical sibling donors

Introduction

The association of polymorphisms in molecules involved in the immune response (checkpoint inhibitors) with the clinical outcome after allogeneic transplantation (alloHSCT) has been described. Lymphocyte Activation 3 (LAG3) is a surface protein that plays a regulatory role in immunity as an inhibitory immune checkpoint molecule.

Methods

To determine its role in the alloHSCT setting, we analyzed 797 patients transplanted from HLA-identical sibling donors. The LAG3 rs870849 C>T polymorphism was genotyped in donors.

Results

We detected a higher incidence of severe acute GVHD in patients transplanted from donors with TT genotype (p: 0.047, HR 1.64; 95% CI 1.01 - 2.67). Overall survival (OS) was worse for patients transplanted from donors with the rs870849 CT/TT genotype (0.020; HR, 1.44; 95% CI 1.06 - 1.96), as well as disease-free survival (DFS) (p: 0.002; HR 1.58, 95%CI: 1.18 - 2.14) and transplant-related mortality (TRM) (p< 0.001; HR: 1.88, 95% CI 1.29 - 2.74). When combining the LAG3 rs870849 and the PDCD1 rs36084323 genotypes of the donor, three genetic groups were well defined, allowing a good stratification of the risk of acute GVHD, TRM, OS and DFS.

Discussion

We conclude that the LAG3 genotype of the donor may be considered in donors' selection. As this selection may be limited in the HLA-identical sibling donor scenario, further studies exploring the impact of LAG3 genotype of the donor in unrelated transplantation are warranted.

Construction and validation of a prognostic model for gastrointestinal stromal tumors based on copy number alterations and clinicopathological characteristics

Background

The increasing incidence of gastrointestinal stromal tumors (GISTs) has led to the discovery of more novel prognostic markers. We aim to establish an unsupervised prognostic model for the early prediction of the prognosis of future patients with GISTs and to guide clinical treatment.

Methods

We downloaded the GISTs dataset through the cBioPortal website. We extracted clinical information and pathological information, including the microsatellite instability (MSI) score, fraction genome altered (FGA) score, tumor mutational burden (TMB), and copy number alteration burden (CNAB), of patients with GISTs. For survival analysis, we used univariate Cox regression to analyze the contribution of each factor to prognosis and calculated a hazard ratio (HR) and 95% confidence interval (95% CI). For clustering groupings, we used the t-distributed stochastic neighbor embedding (t-SNE) method for data dimensionality reduction. Subsequently, the k-means method was used for clustering analysis.

Results

A total of 395 individuals were included in the study. After dimensionality reduction with t-SNE, all patients were divided into two subgroups. Cluster 1 had worse OS than cluster 2 (HR=3.45, 95% CI, 2.22-5.56, P<0.001). The median MSI score of cluster 1 was 1.09, and the median MSI score of cluster 2 was 0.24, which were significantly different (P<0.001). The FGA score of cluster 1 was 0.28, which was higher than that of cluster 2 (P<0.001). In addition, both the TMB and CNAB of cluster 1 were higher than those of cluster 2, and the P values were less than 0.001.

Conclusion

Based on the CNA of GISTs, patients can be divided into high-risk and low-risk groups. The high-risk group had a higher MSI score, FGA score, TMB and CNAB than the low-risk group. In addition, we established a prognostic nomogram based on the CNA and clinicopathological characteristics of patients with GISTs.

Identification of necroptosis-related subtypes, development of a novel signature, and characterization of immune infiltration in colorectal cancer

Introduction

Necroptosis, a type of programmed cell death, has recently been extensively studied as an important pathway regulating tumor development, metastasis, and immunity. However, the expression patterns of necroptosis-related genes (NRGs) in colorectal cancer (CRC) and their potential roles in the tumor microenvironment (TME) have not been elucidated.

Methods

We explored the expression patterns of NRGs in 1247 colorectal cancer samples from genetics and transcriptional perspective. Based on a consensus clustering algorithm, we identified NRG molecular subtypes and gene subtypes, respectively. Furthermore, we constructed a necroptosis-related signature for predicting overall survival time and verified the predictive ability of the model. Using the ESTIMATE, CIBERSORT, and ssGSEA algorithms, we assessed the association between the above subtypes, scores and immune infiltration.

Results

Most NRGs were differentially expressed between CRC tissues and normal tissues. We found that distinct subtypes exhibited different NRGs expression, patients' prognosis, immune checkpoint gene expression, and immune infiltration characteristics. The scores calculated from the necroptosis-related signature can be used to classify patients into high-risk and low-risk groups, with the high-risk group corresponding to reduced immune cell infiltration and immune function, and a greater risk of immune dysfunction and immune escape.

Discussion

Our comprehensive analysis of NRGs in CRC demonstrated their potential role in clinicopathological features, prognosis, and immune infiltration in the TME. These findings help us deepen our understanding of NRGs and the tumor microenvironment landscape, and lay a foundation for effectively assessing patient outcomes and promoting more effective immunotherapy.

TRIM27 is an adverse prognostic biomarker and associated with immune and molecular profiles in right-sided colon cancer

Right-sided colon cancer (RCC), as an independent tumor entity, shows a poor prognosis. It is imperative to detect immune microenvironment-related genes for predicting RCC patient prognosis and study their function in RCC. Tripartite motif-containing 27 (TRIM27) was identified as a risk signature from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) datasets by using weighted gene co-expression network analysis, differentially expressed analysis, and univariate Cox analysis. It predicted a poorer overall survival and increased lymph node metastasis, which were then validated in our 48 clinical samples. Using immunohistochemistry, TRIM27 was found to be highly expressed in both cancer cells and surrounding immunocytes, and its expression in tumor or immune cells both predicted a poorer prognosis. Thereafter, the functional mechanism, immune and molecular characteristics of TRIM27 were investigated using gene set enrichment analysis (GSEA), ESTIMATE, CIBERSORT, and gene set variation analysis (GSVA) at the single-cell, somatic mutation, and RNA-seq level. Patients with highly expressed TRIM27 presented lower CD4⁺ T cell infiltration and activation of the mTORC1/glycolysis pathway. In addition, patients with highly expressed TRIM27 were characterized by hypermetabolism, higher tumor purity, more BRAF mutation, and more chromosomal instability. Collectively, TRIM27 is an important immune-related prognostic biomarker in patients with RCC. It may function via activating the mTORC1/glycolysis pathway and suppressing CD4⁺ T cells. These results indicated that TRIM27 could be a promising therapeutic target in RCC.

Mining of immunological and prognostic-related biomarker for cervical cancer based on immune cell signatures

Background

Immunotherapy has changed the therapeutic landscape of cervical cancer (CC), but has durable anti-tumor activity only in a subset of patients. This study aims to comprehensively analyze the tumor immune microenvironment (TIME) of CC and to mine biomarkers related to immunotherapy and prognosis.

Methods

The Cancer Genome Atlas (TCGA) data was utilized to identify heterogeneous immune subtypes based on survival-related immune cell signatures (ICSs). ICSs prognostic model was constructed by Cox regression analyses, and immunohistochemistry was conducted to verify the gene with the largest weight coefficient in the model. Meanwhile, the tumor immune infiltration landscape was comprehensively characterized by ESTIMATE, CIBERSORT and MCPcounter algorithms. In addition, we also analyzed the differences in immunotherapy-related biomarkers between high and low-risk groups. IMvigor210 and two gynecologic tumor cohorts were used to validate the reliability and scalability of the Risk score.

Results

A total of 291 TCGA-CC samples were divided into two ICSs clusters with significant differences in immune infiltration landscape and prognosis. ICSs prognostic model was constructed based on eight immune-related genes (IRGs), which showed higher overall survival (OS) rate in the low-risk group (P< 0.001). In the total population, time-dependent receiver operating characteristic (ROC) curves displayed area under the curve (AUC) of 0.870, 0.785 and 0.774 at 1-, 3- and 5-years. Immunohistochemical results showed that the expression of the oncogene (FKBP10) was negatively correlated with the degree of differentiation and positively correlated with tumor stage, while the expression of tumor suppressor genes (S1PR4) was the opposite. In addition, the low-risk group had more favorable immune activation phenotype and higher enrichment of immunotherapy-related biomarkers. The Imvigor210 and two gynecologic tumor cohorts validated a better survival advantage and immune efficacy in the low-risk group.

Conclusion

This study comprehensively assessed the TIME of CC and constructed an ICSs prognostic model, which provides an effective tool for predicting patient’s prognosis and accurate immunotherapy.

Identification of microsatellite instability and immune-related prognostic biomarkers in colon adenocarcinoma

Background

Colon adenocarcinoma (COAD) is a prevalent malignancy that causes significant mortality. Microsatellite instability plays a pivotal function in COAD development and immunotherapy resistance. However, the detailed underlying mechanism requires further investigation. Consequently, identifying molecular biomarkers with prognostic significance and revealing the role of MSI in COAD is important for addressing key obstacles in the available treatments.

Methods

CIBERSORT and ESTIMATE analyses were performed to evaluate immune infiltration in COAD samples, followed by correlation analysis for MSI and immune infiltration. Then, differentially expressed genes (DEGs) in MSI and microsatellite stability (MSS) samples were identified and subjected to weighted gene co-expression network analysis (WGCNA). A prognostic model was established with univariate cox regression and LASSO analyses, then evaluated with Kaplan-Meier analysis. The correlation between the prognostic model and immune checkpoint inhibitor (ICI) response was also analyzed.

Results

In total, 701 significant DEGs related to MSI status were identified, and WGCNA revealed two modules associated with the immune score. Then, a seven-gene prognostic model was constructed using LASSO and univariate cox regression analyses to predict survival and ICI response. The high-risk score patients in TCGA and GEO cohorts presented a poor prognosis, as well as a high immune checkpoint expression, so they are more likely to benefit from ICI treatment.

Conclusion

The seven-gene prognostic model constructed could predict the survival of COAD and ICI response and serve as a reference for immunotherapy decisions.

A multifaceted and feasible prognostic model of amino acid metabolism-related genes in the immune response and tumor microenvironment of head and neck squamous cell carcinomas

We investigated the role of amino acid metabolism (AAM) in head and neck squamous cell carcinoma (HNSCC) tissues to explore its prognostic value and potential therapeutic strategies. A risk score based on four AAM-related genes (AMG) was constructed that could predict the prognosis of HNSCC. These four genes were up-regulated in HNSCC tissues and might act as oncogenes. Internal validation in The Cancer Genome Atlas (TCGA) by bootstrapping showed that patients with high-risk scores had a poorer prognosis than patients with low-risk scores, and this was confirmed in the Gene Expression Omnibus (GEO) cohort. There were also differences between the high-risk and low-risk groups in clinical information and different anatomical sites such as age, sex, TNM stage, grade stage, surgery or no surgery, chemotherapy, radiotherapy, no radiotherapy, neck lymph node dissection or not, and neck lymphovascular invasion, larynx, overlapping lesion of lip, and oral cavity and pharynx tonsil of overall survival (OS). Immune-related characteristics, tumor microenvironment (TME) characteristics, and immunotherapy response were significantly different between high- and low-risk groups. The four AMGs were also found to be associated with the expression of markers of various immune cell subpopulations. Therefore, our comprehensive approach revealed the characterization of AAM in HNSCC to predict prognosis and guide clinical therapy.

Focus on pattern recognition receptors to identify prognosis and immune microenvironment in colon cancer

In 2011, J. Hoffman, and B. Beutler won the Nobel Prize of medicine for the fact that they discovered the pattern recognition receptors (PRRs) and meanwhile described their effect on cell activation from the innate and adaptive immune systems. There are more and more evidences that have proved the obvious effect of PRRs on tumorigenesis progression. Nevertheless, the overall impact of PRR genes on prognosis, tumor microenvironmental characteristics and treatment response in patients with colon adenocarcinoma (COAD) remains unclear. In this research, we systematically assessed 20 PRR genes and comprehensively identified the prognostic value and enrichment degree of PRRs. The unsupervised clustering approach was employed for dividing COAD into 4 PRR subtypes, namely cluster A, cluster B, cluster C and cluster D, which were significantly different in terms of the clinical features, the immune infiltrations, and the functions. Among them, cluster B has better immune activities and functions. Cox and LASSO regression analysis was further applied to identify a prognostic five-PRR-based risk signature. Such signature can well predict patients’ overall survival (OS), together with a good robustness. Confounding parameters were controlled, with results indicating the ability of risk score to independently predict COAD patients’ OS. Besides, a nomogram with a strong reliability was created for enhancing the viability exhibited by the risk score in clinical practice. Also, patients who were classified based on the risk score owned distinguishable immune status and tumor mutation status, response to immunotherapy, as well as sensitivity to chemotherapy. A low risk score, featuring increased tumor stemness index (TSI), human leukocyte antigen (HLA), immune checkpoints, and immune activation, demonstrated a superior immunotherapeutic response. According to the study results, the prognostic PRR-based risk signature could serve as a robust biomarker for predicting the clinical outcomes as well as evaluating therapeutic response for COAD patients.

N7-methylguanosine-related lncRNAs: Distinction between hot and cold tumors and construction of predictive models in colon adenocarcinoma

Colon adenocarcinoma (COAD) is a prevalent malignant tumor that severely threatens human health across the globe. Immunotherapy is an essential need for patients with COAD. N7-methylguanosine (m7G) has been associated with human diseases, and non-coding RNAs (lncRNAs) regulate various tumor-related biological processes. Nonetheless, the m7G-related lncRNAs involved in COAD regulation are limited. This study aims to construct the clustering features and prognostic model of m7G-related lncRNAs in COAD. First, The Cancer Genome Atlas (TCGA) database was used to identify m7G-related differentially expressed lncRNAs (DELs), based on which COAD cases could be classified into two subtypes. Subsequently, univariate Cox analysis was used to identify 9 prognostic m7G-related lncRNAs. Further, Five candidates were screened by LASSO-Cox regression to develop new models. The patients were divided into high-risk and low-risk groups based on the median risk score. Consequently, the Kaplan-Meier survival curve demonstrated a statistically significant overall survival (OS) between the high- and low-risk groups (P&lt;0.001). Multivariate Cox regression analysis revealed that risk score is an independent prognostic factor in COAD patients (P&lt;0.001). This confirms the clinical applicability of the model. Additionally, we performed Gene Set Enrichment Analysis (GSEA), which uncovered the biological and functional differences between risk subgroups, i.e., enrichment of immune-related diseases in the high-risk group and enrichment of metabolic-related pathways in the low-risk group. In a drug sensitivity analysis, high-risk group were more sensitive to some chemotherapeutics and targeted drugs than low-risk group. Eventually, the stability of the model was confirmed by qRT-PCR. Our study unraveled the features of different immune states of COAD and established a prognostic model, including five m7G-related lncRNAs for COAD patients. These results will bolster clinical treatment and survival prediction of COAD.

Identification of a Prognostic Model Based on Immune Cell Signatures in Clear Cell Renal Cell Carcinoma

Background. Accumulating evidence substantiated that the immune cells were intricately intertwined with the prognosis and therapy of clear cell renal cell carcinoma (ccRCC). We aimed to construct an immune cell signatures (ICS) score model to predict the prognosis of ccRCC patients and furnish guidance for finding appropriate treatment strategies. Methods. Based on The Cancer Genome Atlas (TCGA) database, the normalized enrichment score (NES) of 184 ICSf was calculated using single-sample gene set enrichment analysis (ssGSEA). An ICS score model was generated in light of univariate Cox regression and Least absolute shrinkage and selection operator (Lasso)-Cox regression, which was independently validated in ArrayExpress database. In addition, we appraised the predictive power of the model via Kaplan-Meier (K-M) curves and time-dependent receiver operating characteristic (ROC) curves. Eventually, immune infiltration, genomic alterations and immunotherapy were analyzed between high and low ICS score groups. Results. Initially, we screened 11 ICS with prognostic impact based on 515 ccRCC patients. K-M curves presented that the high ICS score group experienced a poorer prognosis ( $P<0.001$ ). In parallel, ROC curves revealed a satisfactory reliability of model to predict individual survival at 1, 3, and 5 years, with area under the curves (AUCs) of 0.744, 0.713, and 0.742, respectively. In addition, we revealed that the high ICS score group was characterized by increased infiltration of immune cells, strengthened BAP1 mutation frequency, and enhanced expression of immune checkpoint genes. Conclusion. The ICS score model has higher predictive power for patients’ prognosis and can instruct ccRCC patients in seeking suitable treatment.

Identification of a novel cellular senescence-related signature for the prediction of prognosis and immunotherapy response in colon cancer

The study was conducted to construct a cellular senescence-related risk score signature to predict prognosis and immunotherapy response in colon cancer. Colon cancer data were acquired from the Gene Expression Omnibus and The Cancer Genome Atlas databases. And cellular senescence-related genes were obtained from the CellAge database. The colon cancer data were classified into different clusters based on cellular senescence-related gene expression. Next, prognostic differential genes among clusters were identified with survival analysis. A cellular senescence-related risk score signature was developed by performing the LASSO regression analysis. Finally, PCA analysis, t-SNE analysis, Kaplan-Meier survival analysis, ROC analysis, univariate Cox regression analysis, multivariate Cox regression analysis, C-index analysis, meta-analysis, immune infiltration analysis, and IPS score analysis were used to evaluate the significance of the risk signature for predicting prognosis and immunotherapy response in colon cancer. The colon cancer data were classified into three clusters. The patients in cluster A and cluster B had longer survival. A cellular senescence-related risk score signature was developed. Patients in the low-risk score group showed a better prognosis. The risk score signature could predict colon cancer patients’ prognosis independently of other clinical characteristics. The risk score signature predicted the prognosis of colon cancer patients more accurately than other signatures. Patients in the low-risk score group showed a better response to immunotherapy. The opposite was true for the high-risk score group. In conclusion, the cellular senescence-related risk score signature could be used for the prediction of prognosis and immunotherapy response in colon cancer.

Cutting-Edge: Preclinical and Clinical Development of the First Approved Lag-3 Inhibitor

Immune checkpoint inhibitors (ICIs) have revolutionized medical practice in oncology since the FDA approval of the first ICI 11 years ago. In light of this, Lymphocyte-Activation Gene 3 (LAG-3) is one of the most important next-generation immune checkpoint molecules, playing a similar role as Programmed cell Death protein 1 (PD-1) and Cytotoxic T-Lymphocyte Antigen 4 (CTLA-4). 19 LAG-3 targeting molecules are being evaluated at 108 clinical trials which are demonstrating positive results, including promising bispecific molecules targeting LAG-3 simultaneously with other ICIs. Recently, a new dual anti-PD-1 (Nivolumab) and anti-LAG-3 (Relatimab) treatment developed by Bristol Myers Squibb (Opdualag), was approved by the Food and Drug Administration (FDA) as the first LAG-3 blocking antibody combination for unresectable or metastatic melanoma. This novel immunotherapy combination more than doubled median progression-free survival (PFS) when compared to nivolumab monotherapy (10.1 months versus 4.6 months). Here, we analyze the large clinical trial responsible for this historical approval (RELATIVITY-047), and discuss the preclinical and clinical developments that led to its jump into clinical practice. We will also summarize results achieved by other LAG-3 targeting molecules with promising anti-tumor activities currently under clinical development in phases I, I/II, II, and III. Opdualag will boost the entry of more LAG-3 targeting molecules into clinical practice, supporting the accumulating evidence highlighting the pivotal role of LAG-3 in cancer.

Immunological role and prognostic potential of CLEC10A in pan-cancer

OBJECTIVES

CLEC10A is expressed in a variety of cells, involved in a variety of biological pathways including immune response, and is closely related to the development of tumor immune response. However, the role of CLEC10A from a pan-cancer perspective has not yet been analyzed, and its role in human cancer prognosis and immunology remains largely unclear.

METHODS

We studied the expression levels of CLEC10A and investigated its prognostic value in various cancers across distinct datasets including Oncomine, cBioPortal, and TCGA, and conducted immunohistochemical experiments using fresh bladder cancer and breast cancer samples to verify the results. In addition, we also performed GSEA of CLEC10A and explored the relationship between CLEC10A expression and immune infiltration, immune checkpoints, immune activation genes, immunosuppressive genes, chemokines and chemokine receptors.

RESULTS

The results showed that the expression level of CLEC10A in most tumors was significantly lower compared with non-cancerous tissue, and the decreased expression was related to poor prognosis and more advanced cancer stages. We also found that the expression of CLEC10A was significantly related to the immunomodulatory interaction between lymph and non-lymphocytes. Furthermore, the expression of CLEC10A was not only significantly correlated with the level of infiltration of CD4+T cells and CD8+T cells, but also closely related to immune checkpoints, immune activation genes, immunosuppressive genes, chemokines, and chemokine receptors. Importantly, our analysis of the relationship between CLEC10A and TMB and MSI also confirmed the speculation that CLEC10A may influence antitumor immunity by regulating the composition and immune mechanisms of the tumor microenvironment.

CONCLUSIONS

In conclusion, CLEC10A may serve as a new target for tumor immunotherapy and has great potential as a molecular biomarker for predicting pan-cancer prognosis and immune infiltration.

Single-cell atlas of the aging mouse colon

We performed massive single-cell sequencing in the aging mouse colonic epithelium and immune cells. We identified novel compartment-specific markers as well as dramatic aging-associated changes in cell composition and signaling pathways, including a shift from absorptive to secretory epithelial cells, depletion of naive lymphocytes, and induction of eIF2 signaling. Colon cancer is one of the leading causes of death within the western world, incidence of which increases with age. The colonic epithelium is a rapidly renewing tissue, tasked with water and nutrient absorption, as well as hosting intestinal microbes. The colonic submucosa is populated with immune cells interacting with and regulating the epithelial cells. However, it is unknown whether compartment-specific changes occur during aging and what impact this would cause. We show that both epithelial and immune cells differ significantly between colonic compartments and experience significant age-related changes in mice. We found a shift in the absorptive-secretory cell balance, possibly linked to age-associated intestinal disturbances, such as malabsorption. We demonstrate marked changes in aging immune cells: population shifts and interactions with epithelial cells, linking cytokines (Ifn-γ, Il1B) with the aging of colonic epithelium. Our results provide new insights into the normal and age-associated states of the colon.

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Mouse colon shows compartment-specific transcriptional and population differences

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Old animal colon switches to a pro-inflammatory state

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Changes in epithelium linked to changes in tissue-resident immune cells

Based on Molecular Subtypes, Immune Characteristics and Genomic Variation to Constructing and Verifying Multi-Gene Prognostic Characteristics of Colorectal Cancer

Background: Colon cancer (COAD) has been identified as being among the most prevalent tumors globally and ranked the third major contributor to cancer-related mortality. COAD is a molecularly heterogeneous disease. There are great differences in clinical manifestations and prognosis among different molecular subtypes.

Methods:379 TCGA-COAD samples were divided into four subtypes: primary proliferative, with collective, crypt-like, and EMT invasion. The differences among the four subtypes were analyzed from the multidimensional perspectives of immunity, genomic variation, and prognosis. The limma package was utilized to identify differentially expressed genes (DEGs) amongst different molecular subtypes. Phenotype-related coexpressed gene modules were identified using WGCNA. The polygenic prognosis model was created utilizing the lasso Cox analysis and verified by time-dependent subject operating characteristics (ROC).

Results: There are some differences in prognosis, TMB and common gene variation, immune score, and immunotherapy/chemotherapy between proliferative and three invasive molecular subtypes. 846 differential genes (DEGs) were obtained by limma packet analysis. Differential gene analysis was utilized to screen the DEGs among distinct subtypes, which were significantly enriched in the pathways related to tumorigenesis and development. Co-expression network analysis found 46 co-expressed genes correlated with proliferative and three invasive phenotypes. Based on differentially co-expressed genes, we developed a prognostic risk model of 8-genes signature, which exhibited strong stability regardless of external and internal validation. RT-PCR experiments proved the expression of eight genes in tumor and normal samples.

Conclusion: We have developed an eight-gene signature prognostic stratification system. Furthermore, we proposed that this classifier can serve as a molecular diagnostic tool to assess the prognosis of colon cancer patients.

A Prognostic Model Using Immune-Related Genes for Colorectal Cancer

There is evidence suggesting that immune genes play pivotal roles in the development and progression of colorectal cancer (CRC). Colorectal carcinoma patient data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO) were randomly classified into a training set, a test set, and an external validation set. Differentially expressed gene (DEG) analyses, univariate Cox regression, and the least absolute shrinkage and selection operator (LASSO) were used to identify survival-associated immune genes and develop a prognosis model. Receiver operating characteristic (ROC) analysis and principal component analysis (PCA) were used to evaluate the discrimination of the risk models. The model genes predicted were verified using the Human Protein Atlas (HPA) databases, colorectal cell lines, and fresh CRC and adjacent tissues. To understand the relationship between IRGs and immune invasion and the TME, we analyzed the content of immune cells and scored the TME using CIBERSORT and ESTIMATE algorithms. Finally, we predicted the potential sensitive chemotherapeutic drugs in different risk score groups by the Genomics of Drug Sensitivity in Cancer (GDSC). A total of 491 IRGs were screened, and 14 IRGs were identified to be significantly related to overall survival (OS) and applied to construct an immune-related gene (IRG) prognostic signature (IRGSig) for CRC patients. Calibration plots showed that nomograms have powerful predictive ability. PCA and ROC analysis further verified the predictive value of this fourteen-gene prognostic model in three independent databases. Furthermore, we discovered that the tumor microenvironment changed significantly during the tumor development process, from early to middle to late stage, which may be an essential factor for tumor deterioration. Finally, we selected six commonly used chemotherapeutic drugs that have the potential to be useful in the treatment of CRC. Altogether, immune genes were used to construct a prognosis model for CRC patients, and a variety of methods were used to test the accuracy of this model. In addition, we explored the immune mechanisms of CRC through immune cell infiltration and TME in CRC. Furthermore, we assessed the therapeutic sensitivity of many commonly used chemotherapeutic medicines in individuals with varying risk factors. Finally, the immune risk model and immune mechanism of CRC were thoroughly investigated in this paper.

Links Between N6-Methyladenosine and Tumor Microenvironments in Colorectal Cancer

N⁶-methyladenosine (m⁶A) is a critical epigenetic modification for tumor malignancies, but its role in regulating the tumor microenvironments (TMEs) has not been fully studied. By integrating multiple data sets and multi-omics data, we comprehensively evaluated the m⁶A “writers,” “erasers,” and “readers” in colorectal cancer and their association with TME characteristics. The m⁶A regulator genes showed specific patterns in co-mutation, copy number variation, and expression. Based on the transcriptomic data of the m⁶A regulators and their correlated genes, two types of subtyping systems, m⁶A_regCluster and m⁶A_sigCluster, were developed. The clusters were distinct in pathways (metabolism/inflammation/extracellular matrix and interaction), immune phenotypes (immune-excluded/immune-inflamed/immune-suppressive), TME cell composition (lack immune and stromal cells/activated immune cells/stromal and immune-suppressive cells), stroma activities, and survival outcomes. We also established an m⁶Ascore associated with molecular subgroups, microsatellite instability, DNA repair status, mutation burdens, and survival and predicted immunotherapy outcomes. In conclusion, our work revealed a close association between m⁶A modification and TME formation. Evaluating m⁶A in cancer has helped us comprehend the TME status, and targeting m⁶A in tumor cells might help modulate the TME and improve tumor therapy and immunotherapy.

An Immune-Related Prognostic Signature for Predicting Clinical Outcomes and Immune Landscape in IDH-Mutant Lower-Grade Gliomas

BACKGROUND

IDH mutation is the most common in diffuse LGGs, correlated with a favorable prognosis. However, the IDH-mutant LGGs patients with poor prognoses need to be identified, and the potential mechanism leading to a worse outcome and treatment options needs to be investigated.

METHODS

A six-gene immune-related prognostic signature in IDH-mutant LGGs was constructed based on two public datasets and univariate, multivariate, and LASSO Cox regression analysis. Patients were divided into low- and high-risk groups based on the median risk score in the training and validation sets. We analyzed enriched pathways and immune cell infiltration, applying the GSEA and the immune evaluation algorithms.

RESULTS

Stratification and multivariate Cox analysis unveiled that the six-gene signature was an independent prognostic factor. The signature (0.806/0.795/0.822) showed a remarkable prognostic performance, with 1-, 3-, and 5-year time-dependent AUC, higher than for grade (0.612/0.638/0.649) and 1p19q codeletion status (0.606/0.658/0.676). High-risk patients had higher infiltrating immune cells. However, the specific immune escape was observed in the high-risk group after immune activation, owing to increasing immunosuppressive cells, inhibitory cytokines, and immune checkpoint molecules. Moreover, a novel nomogram model was developed to evaluate the survival in IDH-mutant LGGs patients.

CONCLUSION

The six-gene signature could be a promising prognostic biomarker, which is promising to promote individual therapy and improve the clinical outcomes of IDH-mutant gliomas. The study also refined the current classification system of IDH-mutant gliomas, classifying patients into two subtypes with distinct immunophenotypes and overall survival.

Establishment and verification of a prognostic tumor microenvironment-based and immune-related gene signature in colon cancer

Background

Gastrointestinal malignant cancers affect many sites in the intestinal tract, including the colon. In this study, we purposed to improve prognostic predictions for colon cancer (CC) patients by establishing a novel biosignature of immune-related genes (IRGs) based on the tumor microenvironment (TME).

Methods

Using the estimation of stromal and immune cells in malignant tumor tissues using expression data (ESTIMATE) algorithm, we calculated the stromal and immune scores of every CC patient extracted from The Cancer Genome Atlas (TCGA). We then identified 4 immune-related messenger RNA (mRNA) biosignatures through a Cox and least absolute shrinkage and selection operator (LASSO) univariate analysis, and a Cox multivariate analysis. Relationships between tumor immune infiltration and the risk score were evaluated through the CIBERSORT algorithm and Tumor Immune Estimation Resource (TIMER) database.

Results

Our studies showed that individuals who had a high immune score (P=0.017) and low stromal score (P=0.041) had a favorable overall survival (OS) rate. By comparing high/low scores cohort, 220 differentially expressed genes (DEGs) were determined. Then an immune-related four-mRNA biosignature, including PDIA2, NAFTC1, VEGFC, and CD1B was identified. Kaplan-Meier, calibration, and receiver operating characteristic (ROC) curves verified the model's performance. By using univariate and multivariate Cox analyses, we found each biosignature was an independent risk factor for assessing a CC patient's survival. Three external GEO cohorts validated its good efficiency in estimating OS among individuals with CC. Moreover, the signature was also related to infiltration of several cells of the immune system in the tumor microenvironment.

Conclusions

The resultant model in our study included 4 IRGs associated with the TME. These IRGs can be utilized as an auxiliary variable to estimate and help improve the prognosis of individuals with CC.

A four-gene prognostic signature for predicting the overall survival of patients with lung adenocarcinoma

Background

The prognosis of patients for lung adenocarcinoma (LUAD) is known to vary widely; the 5-year overall survival rate is just 63% even for the pathological IA stage. Thus, in order to identify high-risk patients and facilitate clinical decision making, it is vital that we identify new prognostic markers that can be used alongside TNM staging to facilitate risk stratification.

Methods

We used mRNA expression from The Cancer Genome Atlas (TCGA) cohort to identify a prognostic gene signature and combined this with clinical data to develop a predictive model for the prognosis of patients for lung adenocarcinoma. Kaplan-Meier curves, Lasso regression, and Cox regression, were used to identify specific prognostic genes. The model was assessed via the area under the receiver operating characteristic curve (AUC-ROC) and validated in an independent dataset (GSE50081) from the Gene Expression Omnibus (GEO).

Results

Our analyses identified a four-gene prognostic signature (CENPH, MYLIP, PITX3, and TRAF3IP3) that was associated with the overall survival of patients with T1-4N0-2M0 in the TCGA dataset. Multivariate regression suggested that the total risk score for the four genes represented an independent prognostic factor for the TCGA and GEO cohorts; the hazard ratio (HR) (high risk group vs low risk group) were 2.34 (p < 0.001) and 2.10 (p = 0.017). Immune infiltration estimations, as determined by an online tool (TIMER2.0) showed that CD4+ T cells were in relative abundance in the high risk group compared to the low risk group in both of the two cohorts (both p < 0.001). We established a composite prognostic model for predicting OS, combined with risk-grouping and clinical factors. The AUCs for 1-, 3-, 5- year OS in the training set were 0.750, 0.737, and 0.719; and were 0.645, 0.766, and 0.725 in the validation set. The calibration curves showed a good match between the predicted probabilities and the actual probabilities.

Conclusions

We identified a four-gene predictive signature which represents an independent prognostic factor and can be used to identify high-risk patients from different TNM stages of LUAD. A new prognostic model that combines a prognostic gene signature with clinical features exhibited better discriminatory ability for OS than traditional TNM staging.

M6A Classification Combined With Tumor Microenvironment Immune Characteristics Analysis of Bladder Cancer

Background

Studies have shown that N6-methyl adenosine (m6A) plays an important role in cancer progression; however, the underlying mechanism of m6A modification in tumor microenvironment (TME) cell infiltration of bladder cancer remains unclear. This study aimed to investigate the role of m6A modification in TME cell infiltration of bladder cancer.

Methods

The RNA expression profile and clinical data of bladder cancer were obtained from The Cancer Genome Atlas and Gene Expression Omnibus. We assessed the m6A modification patterns of 664 bladder cancer samples based on 20 m6A regulators through unsupervised clustering analysis and systematically linked m6A modification patterns to TME cell infiltration characteristics. Gene ontology and gene set variation analyses were conducted to analyze the underlying mechanism based on the assessment of m6A methylation regulators. Principal component analysis was used to construct the m6A score to quantify m6A modification patterns of bladder cancer.

Results

The genetic and expression alterations in m6A regulators were highly heterogeneous between normal and bladder tissues. Three m6A modification patterns were identified. The cell infiltration characteristics were highly consistent with the three immune phenotypes, including immune rejection, immune inflammation, and immune desert. The biological functions of three m6A modification patterns were different. Cox regression analyses revealed that the m6A score was an independent signature with patient prognosis (HR = 1.198, 95% CI: 1.031-1.390). Patients with a low-m6A score were characterized by increased tumor mutation burden, PD-L1 expression, and poorer survival. Patients in the low-m6A score group also showed significant immune responses and clinical benefits in the CTLA-4 immunotherapy cohort (p =0.0069).

Conclusions

The m6A methylation modification was related to the formation of TME heterogeneity and complexity. Assessing the m6A modification pattern of individual bladder cancer will improve the understanding of TME infiltration characteristics.

Identification of a Tumor Microenvironment-Related Gene Signature Indicative of Disease Prognosis and Treatment Response in Colon Cancer

Background

The tumor microenvironment (TME) is associated with disease outcomes and treatment response in colon cancer. Here, we constructed a TME-related gene signature that is prognosis of disease survival and may predict response to immunotherapy in colon cancer.

Methods

We calculated immune and stromal scores for 385 colon cancer samples from The Cancer Genome Atlas (TCGA) database using the ESTIMATE algorithm. We identified nine TME-related prognostic genes using Cox regression analysis. We evaluated associations between protein expression, extent of immune cell infiltrate, and patient survival. We calculated risk scores and built a clinical predictive model for the TME-related gene signature. Receiver operating characteristic (ROC) curves were generated to assess the predictive power of the signature. We estimated the half-maximal inhibitory concentration (IC50) of chemotherapeutic drugs in patients using the pRRophetic algorithm. The expression of immune checkpoint genes was evaluated.

Results

High immune and stromal scores are significantly associated with poor overall survival (p < 0.05). We identified 773 differential TME-related prognostic genes associated with survival; these genes were enriched in immune-related pathways. Nine key prognostic genes were identified and were used to construct a TME-related prognostic signature: CADM3, LEP, CD1B, PDE1B, CCL22, ABI3BP, IGLON5, SELE, and TGFB1. This signature identified a high-risk group with worse survival outcomes, based on Kaplan-Meier analysis. A nomogram composed of clinicopathological factors and risk score exhibited good accuracy. Drug sensitivity analysis identified no difference in sensitivity between the high-risk and low-risk groups. High-risk patients had higher expression of PD-1, PDL-1, and CTLA-4 and lower expression of LAG-3 and VSIR. Infiltration of dendritic cells was higher in the high-risk group.

Conclusions

We identified a novel prognostic TME-related gene expression signature in colon cancer. Stratification of patients based on this gene signature could be used to improve outcomes and guide better therapy for colon cancer patients.

Immune cell infiltration-associated signature in colon cancer and its prognostic implications

Tumor immune cell infiltration (ICI) has been reported in various studies to be correlated with tumor diagnosis, clinical treatment sensitivity and prognosis. It is an important direction to study the characteristics of immune cell infiltration and develop new prognostic markers to improve the treatment of colon cancer. In this paper, we systematically analyzed the ICI characteristics and obtained three ICI clusters. Then, the ICI scores were constructed and its prognostic implications were discussed. From the results, the ICI score patterns were linked to a great survival difference (p<0.001). A high ICI score was characterized by a higher fraction of plasma cells, CD8⁺ T cells, memory resting CD4⁺ T cells, monocytes, eosinophils and dendritic cells, which had better prognosis. Macrophages and neutrophils were increased in low ICI score patients with decreased overall survival. Immune checkpoint molecules (PDCD1, CD274, LAG3, IDO1, CTLA-4, TIGHT and HAVCR2) were found to be significantly overexpressed in the low ICI score subgroup. In addition, we also studied the correlation between the tumor mutation burden (TMB) and ICI score. This study indicated the ICI score could serve as a potential prognostic biomarker for colon cancer patients’ immunotherapy.

Comparative Analysis and in vitro Experiments of Signatures and Prognostic Value of Immune Checkpoint Genes in Colorectal Cancer

Purpose

Immune checkpoints, as pivotal regulators of immune escape in cancer, can motivate the emergence of immune checkpoint inhibitors (ICIs). The aim of this study is to identify the expression of the immune checkpoint genes (ICGs) in colorectal cancer (CRC) and to relate their individual as well as combined expression to prognosis and therapeutic effectiveness in CRC.

Methods

RNA expression of 47 ICGs and clinical information of CRC patients were collected from two public databases to elucidate the expression levels and prognostic values of these ICGs in CRC. Then, the Shapiro–Wilk normality test was used to determine the normality of variables. Overall survival (OS) rates of each subset were found by Kaplan–Meier method, and the statistical significance was determined by the Log rank test (p < 0.05).

Results

The expression of 13 and 9 ICGs was significantly associated with CRC prognosis in The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) cohorts. A series of ICGs was found to be significantly associated with TMB, neoantigens and MMR in CRC indicating that the combination of immunotherapy treatment biomarkers and ICGs may achieve accurate prognostic stratification of CRC, and potentially identify CRC cases that might respond to checkpoint inhibitors (CPIs). The subsets of high or low PD1/PD-L1/IDO1 expression stratified by CD48 were accurately associated with prognosis in CRC. In addition, in vitro experiments confirmed that VTCN1(B7-H4)-KD increases anti-PD-L1-mediated NK cell cytotoxicity on CRC tumor cells.

Conclusion

Although the expression of a single immune-checkpoint molecule does not predict the efficacy of immunotherapy in CRC, our findings infer that subsets defined by ICGs are associated with prognosis and imply the possibility that VTCN1 and CD48 serve as new immunotherapeutic targets.

Understanding LAG-3 Signaling

Lymphocyte activation gene 3 (LAG-3) is a cell surface inhibitory receptor with multiple biological activities over T cell activation and effector functions. LAG-3 plays a regulatory role in immunity and emerged some time ago as an inhibitory immune checkpoint molecule comparable to PD-1 and CTLA-4 and a potential target for enhancing anti-cancer immune responses. LAG-3 is the third inhibitory receptor to be exploited in human anti-cancer immunotherapies, and it is considered a potential next-generation cancer immunotherapy target in human therapy, right next to PD-1 and CTLA-4. Unlike PD-1 and CTLA-4, the exact mechanisms of action of LAG-3 and its relationship with other immune checkpoint molecules remain poorly understood. This is partly caused by the presence of non-conventional signaling motifs in its intracellular domain that are different from other conventional immunoregulatory signaling motifs but with similar inhibitory activities. Here we summarize the current understanding of LAG-3 signaling and its role in LAG-3 functions, from its mechanisms of action to clinical applications.

Immune cell infiltration landscape and immune marker molecular typing in preeclampsia

Preeclampsia (PE) is an important topic in obstetrics. In this study, we used weighted gene co-expression network analysis (WGCNA) to screen the key modules related to immune cell infiltration and to identify the hub genes for the molecular subtyping of PE. We first downloaded a set of PE transcriptional data (GSE75010; 157 samples: 80 PE and 77 non-PE) from the GEO database. We then analyzed the PE samples and non-PE samples for immune cell infiltration and screened cells with differences in such infiltration. Next, we downloaded the immune-related genes from an immune-related database to screen the expression profile of the immune-related genes. Then, we obtained a candidate gene set by screening the immune-related genes differentially expressed between the two groups. We used WGCNA to construct a weighted co-expression network for these candidate genes, mined co-expression modules, and then calculated the correlation between each module and immune cells with differential infiltration. We screened the modules related to infiltrating immune cells, identified the key modules' hub genes, and determined the key module genes that interacted with each other. Finally, we obtained the hub genes related to the infiltrating immune cells. We classified the preeclampsia patients by unsupervised cluster molecular typing, determined the difference of immune cell infiltration among the different PE subtypes, and calculated the expression of hub genes in these different subtypes. In conclusion, we found 41 hub genes that may be closely related to the molecular typing of PE.