A Computational Approach Identifies Immunogenic Features of Prognosis in Human Cancers

Deconvolution of bulk gene expression profiles reveals the association between immune cell polarization and the prognosis of hepatocellular carcinoma patients

BACKGROUND

Many studies have utilized computational methods, including cell composition deconvolution (CCD), to correlate immune cell polarizations with the survival of cancer patients, including those with hepatocellular carcinoma (HCC). However, currently available cell deconvolution estimated (CDE) tools do not cover the wide range of immune cell changes that are known to influence tumor progression.

RESULTS

A new CCD tool, HCCImm, was designed to estimate the abundance of tumor cells and 16 immune cell types in the bulk gene expression profiles of HCC samples. HCCImm was validated using real datasets derived from human peripheral blood mononuclear cells (PBMCs) and HCC tissue samples, demonstrating that HCCImm outperforms other CCD tools. We used HCCImm to analyze the bulk RNA-seq datasets of The Cancer Genome Atlas (TCGA)-liver hepatocellular carcinoma (LIHC) samples. We found that the proportions of memory CD8+ T cells and Tregs were negatively associated with patient overall survival (OS). Furthermore, the proportion of naïve CD8+ T cells was positively associated with patient OS. In addition, the TCGA-LIHC samples with a high tumor mutational burden had a significantly high abundance of nonmacrophage leukocytes.

CONCLUSIONS

HCCImm was equipped with a new set of reference gene expression profiles that allowed for a more robust analysis of HCC patient expression data. The source code is provided at https://github.com/holiday01/HCCImm.

Single-cell RNA sequencing in orthopedic research

Although previous RNA sequencing methods have been widely used in orthopedic research and have provided ideas for therapeutic strategies, the specific mechanisms of some orthopedic disorders, including osteoarthritis, lumbar disc herniation, rheumatoid arthritis, fractures, tendon injuries, spinal cord injury, heterotopic ossification, and osteosarcoma, require further elucidation. The emergence of the single-cell RNA sequencing (scRNA-seq) technique has introduced a new era of research on these topics, as this method provides information regarding cellular heterogeneity, new cell subtypes, functions of novel subclusters, potential molecular mechanisms, cell-fate transitions, and cell‒cell interactions that are involved in the development of orthopedic diseases. Here, we summarize the cell subpopulations, genes, and underlying mechanisms involved in the development of orthopedic diseases identified by scRNA-seq, improving our understanding of the pathology of these diseases and providing new insights into therapeutic approaches.

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.

ATG16L1 is a Potential Prognostic Biomarker and Immune Signature for Osteosarcoma: A Study Based on Bulk RNA and Single-Cell RNA-Sequencing

Background

Osteosarcoma is a common solid malignancy of the bone in children and adolescents, and its metastasis and recurrence are the principal causes of poor treatment outcomes.

Methods

Autophagy-related genes were used to cluster osteosarcoma patients by consensus clustering analysis using the GSE21257 database. Differentially expressed genes (DEGs) were identified by limma package. Multiple-gene risk signature was constructed using least absolute shrinkage and selection operator (LASSO) analysis and Cox regression analyses. Quantitative reverse transcription polymerase chain reaction (qRT-PCR) was used to determine gene expression levels. Then, single-cell RNA-sequencing dataset GSE152048 were used to identify the correlation between the DEGs and effector molecules expressed in specific tumor-infiltrating immune cells.

Results

Two clusters were identified in the consensus clustering analysis, which were confirmed by principal component analysis. Limma analysis revealed that 15 genes were related, and 9 genes were screened using protein-protein interaction network and LASSO regression analysis. Cox regression analyses identified 5 genes. Combined with survival analysis, only the autophagy related 16 like 1 gene (ATG16L1) was significant. The results of qRT-PCR showed low expression levels of ATG16L1 in tumor cells group. Immune infiltration analysis revealed significantly lower expression of CD8⁺ T cells in the high ATG16L1 gene expression group. ScRNA-seq revealed that in the ATG16L1⁺CD8⁺ T cell group, the expression of GZMB was lower, whereas the expression of ITGA1 was higher. These results showed that ATG16L1 is an immune-related gene, which is associated with poor prognosis in patients with osteosarcoma.

Conclusion

ATG16L1 is a potential prognostic biomarker and immune signature and may be a therapeutic target for osteosarcoma.

PBK/TOPK Is a Favorable Prognostic Biomarker Correlated with Antitumor Immunity in Colon Cancers

Immune checkpoint inhibitor therapy has proven efficacy in a subset of colon cancer patients featuring a deficient DNA mismatch repair system or a high microsatellite instability profile. However, there is high demand for more effective biomarkers to expand the colon cancer population responding to ICI therapy. PBK/TOPK, a serine/threonine kinase, plays a role in cell cycle regulation and mitotic progression. Here, we investigated the correlation between PBK/TOPK expression and tumor immunity and its prognostic value in colon cancer. Based on large-scale bioinformatics analysis, we discovered that elevated PBK/TOPK expression predicted a favorable outcome in patients with colon cancer and was positively associated with immune infiltration levels of CD8+ T cells, CD4+ T cells, natural killer cells, and M1 macrophages. In contrast, a negative correlation was found between PBK/TOPK expression and immune suppressor cells, including regulatory T cells and M2 macrophages. Furthermore, the expression of PBK/TOPK was correlated with the expression of T-cell cytotoxicity genes in colon cancer. Additionally, high PBK/TOPK expression was associated with mutations in DNA damage repair genes, and thus with increased tumor mutation and neoantigen burden. These findings suggest that PBK/TOPK may serve as a prognostic and predictive biomarker for immunotherapy in colon cancer.

Combinatorial therapy in tumor microenvironment: Where do we stand?

The tumor microenvironment plays a pivotal role in tumor initiation and progression by creating a dynamic interaction with cancer cells. The tumor microenvironment consists of various cellular components, including endothelial cells, fibroblasts, pericytes, adipocytes, immune cells, cancer stem cells and vasculature, which provide a sustained environment for cancer cell proliferation. Currently, targeting tumor microenvironment is increasingly being explored as a novel approach to improve cancer therapeutics, as it influences the growth and expansion of malignant cells in various ways. Despite continuous advancements in targeted therapies for cancer treatment, drug resistance, toxicity and immune escape mechanisms are the basis of treatment failure and cancer escape. Targeting tumor microenvironment efficiently with approved drugs and combination therapy is the solution to this enduring challenge that involves combining more than one treatment modality such as chemotherapy, surgery, radiotherapy, immunotherapy and nanotherapy that can effectively and synergistically target the critical pathways associated with disease pathogenesis. This review shed light on the composition of the tumor microenvironment, interaction of different components within tumor microenvironment with tumor cells and associated hallmarks, the current status of combinatorial therapies being developed, and various growing advancements. Furthermore, computational tools can also be used to monitor the significance and outcome of therapies being developed. We addressed the perceived barriers and regulatory hurdles in developing a combinatorial regimen and evaluated the present status of these therapies in the clinic. The accumulating depth of knowledge about the tumor microenvironment in cancer may facilitate further development of effective treatment modalities. This review presents the tumor microenvironment as a sweeping landscape for developing novel cancer therapies.

A Cancer-Specific Qualitative Method for Estimating the Proportion of Tumor-Infiltrating Immune Cells

Tumor-infiltrating immune cells are important components in the tumor microenvironment (TME) and different types of these cells exert different effects on tumor development and progression; these effects depend upon the type of cancer involved. Several methods have been developed for estimating the proportion of immune cells using bulk transcriptome data. However, there is a distinct lack of methods that are capable of predicting the immune contexture in specific types of cancer. Furthermore, the existing methods are based on absolute gene expression and are susceptible to experimental batch effects, thus resulting in incomparability across different datasets. In this study, we considered two common neoplasms as examples (colorectal cancer [CRC] and melanoma) and introduced the Tumor-infiltrating Immune Cell Proportion Estimator (TICPE), a cancer-specific qualitative method for estimating the proportion of tumor-infiltrating immune cells. The TICPE was based on the relative expression orderings (REOs) of gene pairs within a sample and is notably insensitive to batch effects. Performance evaluation using public expression data with mRNA mixtures, single-cell RNA-Seq (scRNA-Seq) data, immunohistochemistry data, and simulated bulk RNA-seq samples, indicated that the TICPE can estimate the proportion of immune cells with levels of accuracy that are clearly superior to other methods. Furthermore, we showed that the TICPE could effectively detect prognostic signals in patients with tumors and changes in the fractions of immune cells during immunotherapy in melanoma. In conclusion, our work presented a unique novel method, TICPE, to estimate the proportion of immune cells in specific cancer types and explore the effect of the infiltration of immune cells on the efficacy of immunotherapy and the prognosis of cancer. The source code for TICPE is available at https://github.com/huitingxiao/TICPE.

Major Histocompatibility Complex Genes as Therapeutic Opportunity for Immune Cold Molecular Cancer Subtypes

Current immunotherapies are effective only in a subset of patients, likely due to several factors including defects in tumor cell antigen presentation, decreased response to immune effectors, and molecular heterogeneity of cancers. Recent molecular classifications enable the categorization of many tumor types. However, deregulation of major histocompatibility complex (MHC) gene expression is poorly characterized in the context of molecular cancer subtypes. To suppress the confounding effect of immune infiltrates on expression patterns of immunoregulators, we identified and removed genes with strong correlation to estimated immune compartment levels in each tumor type. Next, we reanalyzed a total of 13 TCGA cancer types encompassing 5651 tumors and 485 normal adjacent tissues by performing unsupervised clustering of 14 MHC genes. Subsequently, resultant clusters were statistically compared in terms of expression of other immune-related genes. Three MHC expression clusters were discovered by unsupervised clustering. We identified concordantly decreased expression of MHC genes (MHC-low) in 26 out of 55 molecular subtypes. Consequently, our study underlines the urgent need for designing strategies to enhance tumor MHC expression that could improve immune cold tumor rejection by cytotoxic T lymphocytes.