Identification of an Immune Gene Signature Based on Tumor Microenvironment Characteristics in Colon Adenocarcinoma

Radiogenomic biomarkers for immunotherapy in glioblastoma: A systematic review of magnetic resonance imaging studies

Background

Immunotherapy is an effective "precision medicine" treatment for several cancers. Imaging signatures of the underlying genome (radiogenomics) in glioblastoma patients may serve as preoperative biomarkers of the tumor-host immune apparatus. Validated biomarkers would have the potential to stratify patients during immunotherapy clinical trials, and if trials are beneficial, facilitate personalized neo-adjuvant treatment. The increased use of whole genome sequencing data, and the advances in bioinformatics and machine learning make such developments plausible. We performed a systematic review to determine the extent of development and validation of immune-related radiogenomic biomarkers for glioblastoma.

Methods

A systematic review was performed following PRISMA guidelines using the PubMed, Medline, and Embase databases. Qualitative analysis was performed by incorporating the QUADAS 2 tool and CLAIM checklist. PROSPERO registered: CRD42022340968. Extracted data were insufficiently homogenous to perform a meta-analysis.

Results

Nine studies, all retrospective, were included. Biomarkers extracted from magnetic resonance imaging volumes of interest included apparent diffusion coefficient values, relative cerebral blood volume values, and image-derived features. These biomarkers correlated with genomic markers from tumor cells or immune cells or with patient survival. The majority of studies had a high risk of bias and applicability concerns regarding the index test performed.

Conclusions

Radiogenomic immune biomarkers have the potential to provide early treatment options to patients with glioblastoma. Targeted immunotherapy, stratified by these biomarkers, has the potential to allow individualized neo-adjuvant precision treatment options in clinical trials. However, there are no prospective studies validating these biomarkers, and interpretation is limited due to study bias with little evidence of generalizability.

Integrative pan-cancer analysis of cuproplasia-associated genes for the genomic and clinical characterization of 33 tumors

BACKGROUND

The molecular mechanisms driving tumorigenesis have continually been the focus of researchers. Cuproplasia is defined as copper-dependent cell growth and proliferation, including its primary and secondary roles in tumor formation and proliferation through signaling pathways. In this study, we analyzed the differences in the expression of cuproplasia-associated genes (CAGs) in pan-cancerous tissues and investigated their role in immune-regulation and tumor prognostication.

METHODS

Raw data from 11,057 cancer samples were acquired from multiple databases. Pan-cancer analysis was conducted to analyze the CAG expression, single-nucleotide variants, copy number variants, methylation signatures, and genomic signatures of micro RNA (miRNA)-messenger RNA (mRNA) interactions. The Genomics of Drug Sensitivity in Cancer and the Cancer Therapeutics Response Portal databases were used to evaluate drug sensitivity and resistance against CAGs. Using single-sample Gene Set Enrichment Analysis (ssGSEA) and Immune Cell Abundance Identifier database, immune cell infiltration was analyzed with the ssGSEA score as the standard.

RESULTS

Aberrantly expressed CAGs were found in multiple cancers. The frequency of single-nucleotide variations in CAGs ranged from 1% to 54% among different cancers. Furthermore, the correlation between CAG expression in the tumor microenvironment and immune cell infiltration varied among different cancers. ATP7A and ATP7B were negatively correlated with macrophages in 16 tumors including breast invasive carcinoma and esophageal carcinoma, while the converse was true for MT1A and MT2A . In addition, we established cuproplasia scores and demonstrated their strong correlation with patient prognosis, immunotherapy responsiveness, and disease progression ( P  <0.05). Finally, we identified potential candidate drugs by matching gene targets with existing drugs.

CONCLUSIONS

This study reports the genomic characterization and clinical features of CAGs in pan-cancers. It helps clarify the relationship between CAGs and tumorigenesis, and may be helpful in the development of biomarkers and new therapeutic agents.

Immune gene signatures as prognostic criteria for cancer patients

Recently, the possibility of using immune gene signatures (IGSs) has been considered as a novel prognostic tool for numerous cancer types. State-of-the-art methods of genomic, transcriptomic, and protein analysis have allowed the identification of a number of immune signatures correlated to disease outcome. The major adaptive and innate immune components are the T lymphocytes and macrophages, respectively. Herein, we collected essential data on IGSs consisting of subsets of T cells and tumor-associated macrophages and indicating cancer patient outcomes. We discuss factors that can introduce errors in the recognition of immune cell types and explain why the significance of immune signatures can be interpreted with uncertainty. The unidirectional functions of cell types should be entirely addressed in the signatures constructed by the combination of innate and adaptive immune cells. The state of the antitumor immune response is the key basis for IGSs and should be considered in gene signature construction. We also analyzed immune signatures for the prediction of immunotherapy response. Finally, we attempted to explain the present-day limitations in the use of immune signatures as robust criteria for prognosis.

A lipid metabolism-related gene signature reveals dynamic immune infiltration of the colorectal adenoma-carcinoma sequence

BACKGROUND

Lipid metabolism-related genes (LMRGs) have been reported to be correlated with the immune infiltration of colorectal cancer (CRC). This study aimed to investigate the immune infiltration characteristics along the colorectal adenoma-carcinoma sequence (ACS) based on LMRGs.

METHODS

Gene expression data of colorectal adenoma and carcinoma samples were obtained from the public databases. The "limma" package was applied to determine the differentially expressed LMRGs. Unsupervised consensus clustering was used to cluster colorectal samples. The features of the tumor microenvironment were analyzed by the "ESTIMATE", "GSVA", and "TIDE" algorithms.

RESULTS

The expression of 149 differentially expressed LMRGs was defined as the LMRG signature. Based on this signature, the adenoma and carcinoma samples were divided into three clusters. Unexpectedly, these sequential clusters showed a directional relationship and collectively constituted the progressive course of colorectal ACS. Interestingly, the LMRG signature revealed that adenoma progression was accompanied by a progressive loss of immune infiltration and a stepwise establishment of a cold microenvironment, but carcinoma progression was characterized by a progressive gain of immune infiltration and a gradual establishment of a hot microenvironment.

CONCLUSIONS

The LMRG signature reveals dynamic immune infiltration along the colorectal ACS, which substantially changes the understanding of the tumor microenvironment of CRC carcinogenesis and provides novel insight into the role of lipid metabolism in this process.

Wilms Tumor 1-Associated Protein Expression Is Linked to a T-Cell-Inflamed Phenotype in Pancreatic Cancer

BACKGROUND

The molecular driving forces of anti-tumor immunity in pancreatic ductal adenocarcinoma (PDAC) remain unclear, which causing great difficulty in identifying an appropriate treatment strategy.

AIMS

This study aims to explore the associations between expression of Wilms tumor 1-associated protein (WTAP) and effector T-cell infiltration in PDAC.

METHODS

In this study, we explored the association between WTAP expression and infiltration level of CD8+ T cells in PDAC. 178 PDAC samples were selected from The Cancer Genome Atlas (TCGA) database. The associations between diverse immune-cell infiltration, Tumor Mutation Burden (TMB), immune checkpoints, and WTAP expression were performed via R software. Transcriptional hallmarks of anti-tumor immunity and known T-cell-inflamed signature of PDAC were both selected to explore the relevance to WTAP expression. Potential immune checkpoint blockade (ICB) response to different WTAP expression was predicted with tumor immune dysfunction and exclusion (TIDE) algorithm.

RESULTS

WTAP was closely linked to CD8+ T-cell infiltration (r ≥ 0.5, P value < 0.05) and did not show notable association with TMB in PDAC. WTAP positively linked to T-cell-inflamed gene expression profiles (GEP) (IL2RB, IL2RA, ZAP70, ITK, CD3E, CD38, CD27, CD276, CD8A, CMKLR1, CXCR6, HLA-DQA1, HLA-DRB1, HLA-E, NKG7, and STAT1), cytolytic activity (GZMA and PRF1), various immune checkpoints (IDO1, CD274, HAVCR2, PDCD1, CTLA4, LAG3, and PDCD1LG2) and 4-chemokine signature (CCL4, CCL5, CXCL9, and CXCL10). Besides, increased expression of WTAP was related to a higher TIDE score.

CONCLUSIONS

WTAP marks PDAC tumors with an active anti-tumor phenotype and might help the identification of PDAC patients who might benefit from immunotherapies.

Distinct tumor microenvironment landscapes of rectal cancer for prognosis and prediction of immunotherapy response

PURPOSE

Tumor microenvironment (TME) affects the progression of rectal cancer (RC), and the clinical relevance of its immune elements was widely reported. Here we aim to delineate the complete TME landscape, including non-immune features, to improve our understanding of RC heterogeneity and provide a better strategy for precision medicine.

METHODS

Single-cell analysis of GSE161277 using Seurat and Cellcall was performed to identify cell-cell interactions. The ssGSEA was employed to quantify the TME elements in TCGA patients, which were further clustered into subtypes by hclust. WGCNA and LASSO were combined to construct a degenerated signature for prognosis, and its performance was validated in two GEO datasets.

RESULTS

We proposed a subtyping strategy based on the abundance of both immune and non-immune components, which divided all RC patients into 4 subtypes (Immune-, Canonical-, Dormant- and Stem-like). Different subtypes exhibited distinct mutation landscapes, biological features, immune characteristics, immunotherapy responses and prognoses. Next, WGCNA and LASSO regression were combined to construct a 10-gene signature based on differentially expressed genes among different subtypes. Subgroups divided by this signature also exhibited different clinical parameters and responses to immune checkpoint blockades. Diverse machine learning algorithms were applied to achieve higher accuracy for survival prediction and a nomogram was further established in combination with M stage and age to provide an accurate and visual prediction of prognosis.

CONCLUSIONS

We identified four TME-based RC subtypes with distinct biological and clinical features. Based on those subtypes, we also proposed a degenerated 10-gene signature to predict the prognosis and immunotherapy response.

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.

Long non-coding RNA tumor protein 73 antisense RNA 1 influences an interaction between lysine demethylase 5A and promoter of tumor protein 73 to enhance the malignancy of colorectal cancer

Colorectal cancer (CRC) is one of the leading causes of cancer-related death worldwide. The aim of the present study was to explore the expression level of tumor protein 73 (TP73) in highly malignant CRC tumors and how the long non-coding RNA tumor protein 73 antisense RNA 1 (TP73-AS1) influences that transcription. We found that TP73-AS1 was highly expressed in malignant CRC samples in The Cancer Genome Atlas (TCGA) database. We also demonstrated TP73-AS1 was expressed in thirty samples of CRC tissues collected from China Medical University patients as well as in HCT116, RKO and SW480 CRC cell lines but not in HCoEpiC or CCD-18Co normal colon cells. Only wild-type TP73-AS1, but not any of its alternate splicing isoforms, was positively correlated with tumor malignancy. TP73-AS1 transcripts were shown to be located in cell nuclei especially in close proximity to the TP73 promoter in CRC cells, but not in normal colon cells. In addition, an interaction between lysine demethylase 5A (KDM5A) and TP73-AS1 in CRC cells, but not normal colon cells, and KDM5A localization on the TP73 promoter were influenced by TP73-AS1. Interestingly, the H3K4me3 level on the TP73 promoter was reduced, but was elevated by TP73-AS1 knockdown in CRC cells. In conclusion, these results suggest a novel epigenetic role of TP73-AS1 on histone demethylation that influences TP73 transcription, and shed light on malignancy in CRC.

Novel Immune-Related Gene Signature for Risk Stratification and Prognosis of Survival in ER (+) and/or PR (+) and HER2 (-) Breast Cancer

Background: Although intrinsic molecular subtype has been widely used, there remains great clinical heterogeneity of prognosis in the estrogen receptor (ER)- and/or progesterone receptor (PR)-positive and human epidermal growth factor receptor 2 (HER2)-negative breast cancer (BC).

Methods: The transcriptome expression data of messenger RNA (mRNA) were downloaded from The Cancer Genome Atlas (TCGA), Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), and the Gene Expression Omnibus (GEO) databases. Immune-related genes were acquired from the ImmPort database and additional literature search. Univariate Cox, LASSO regression, and multivariate Cox regression were used to screen prognostic immune-related genes and establish the risk signature. The correlation between the risk signature and clinical characteristics, the abundances of immune cells within the tumor microenvironment, and cancer phenotypes were further assessed.

Results: Of note, 102 immune-related prognostic genes were identified in the METABRIC dataset by univariate Cox analysis. Consecutively, 7 immune-related genes (SHMT2, AGA, COL17A1, FLT3, SLC7A2, ATP6AP1, and CCL19) were selected to establish the risk signature by LASSO regression and multivariate Cox analysis. Its performance was further verified in TCGA and GSE21653 datasets. Multivariate Cox analysis showed that the risk signature was an independent prognostic factor. The 7-gene signature showed a significant correlation with intrinsic molecular subtypes and 70-gene signature. Furthermore, the CD4⁺ memory T cells were significantly higher in the low-risk group while a significantly higher proportion of M0-type macrophages was found in the high-risk group in both METABRIC and TCGA cohorts, which may have an influence on the prognosis. Furthermore, we found that the low-risk group may be associated with the immune-related pathway and the high-risk group was with the cell cycle-related pathway, which also showed an impact on the prognosis.

Conclusion: These seven immune-related gene risk signatures provided an effective method for prognostic stratification in ER (+) and/or PR (+) and HER2 (−) BC.

Th1 immune maturation effects of

Nocardia rubra cell-wall skeleton (Nr-CWS) is reported as an external immunotherapeutic enhancer with the advantage of antitumor effect on human cancers. However, the immune regulatory role of Nr-CWS is not fully illustrated. We studied mouse CD4+ T lymphocytes isolated from mice spleen were induced by Nr-CWS and observed that the differentiation of Th1 CD4+ T cells and the cytokines of IL-2, TNF-α, IFN-γ were all enhanced by Nr-CWS. Furthermore, RNA sequencing was conducted to investigate the different mRNA profiling induced by Nr-CWS. We observed that paired box 8 (PAX8) was significantly up-regulated in Nr-CWS-treated Th1 cells compared to control. As a transcription factor, chromatin immunoprecipitation sequencing was carried out to study the genome-wide distribution of PAX8. Interestingly, we found that the binding domain of PAX8 was elevated by Nr-CWS, and the target genes associated with these binding sites showed a positive correlation between their transcription and PAX8 binding strength. Finally, we determined that Nr-CWS could enhance the activity of the PI3 K/Akt signaling pathway. Akt agonist could mimic the effect of Nr-CWS for PAX8 up-regulation, while Akt inhibitor compromised the expression of PAX8. Taken together, we determined a novel role of Nr-CWS in boosting the activity of Th1 maturation via the PI3 K/Akt/PAX8 axis.

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.