Multi-omics analysis of tumor mutation burden combined with immune infiltrates in melanoma

Based on scRNA-seq and bulk RNA-seq to establish tumor immune microenvironment-associated signature of skin melanoma and predict immunotherapy response

Skin cutaneous melanoma (SKCM), a form of skin cancer, ranks among the most formidable and lethal malignancies. Exploring tumor microenvironment (TME)-based prognostic indicators would help improve the efficacy of immunotherapy for SKCM patients. This study analyzed SKCM scRNA-seq data to cluster non-malignant cells that could be used to explore the TME into nine immune/stromal cell types, including B cells, CD4 T cells, CD8 T cells, dendritic cells, endothelial cells, Fibroblasts, macrophages, neurons, and natural killer (NK) cells. Using data from The Cancer Genome Atlas (TCGA), we employed SKCM expression profiling to identify differentially expressed immune-associated genes (DEIAGs), which were then incorporated into weighted gene co-expression network analysis (WGCNA) to investigate TME-associated hub genes. Discover candidate small molecule drugs based on pivotal genes. Tumor immune microenvironment-associated genes (TIMAGs) for constructing TIMAS were identified and validated. Finally, the characteristics of TIAMS subgroups and the ability of TIMAS to predict immunotherapy outcomes were analyzed. We identified five TIMAGs (CD86, CD80, SEMA4D, C1QA, and IRF1) and used them to construct TIMAS. In addition, five potential SKCM drugs were identified. The results showed that TIMAS-low patients were associated with immune-related signaling pathways, high MUC16 mutation frequency, high T cell infiltration, and M1 macrophages, and were more favorable for immunotherapy. Collectively, TIMAS constructed by comprehensive analysis of scRNA-seq and bulk RNA-seq data is a promising marker for predicting ICI treatment outcomes and improving individualized therapy for SKCM patients.

Liquid-liquid phase separation throws novel insights into treatment strategies for skin cutaneous melanoma

BACKGROUND

In recent years, there has been growing evidence indicating a relationship between liquid-liquid phase separation (LLPS) and cancer development. However, to date, the clinical significance of LLPS in skin cutaneous melanoma (SKCM, hereafter referred to as melanoma) remains to be elucidated. In the current study, the impact of LLPS-related genes on melanoma prognosis has been explored.

METHODS

LLPS-related genes were retrieved from the DrLLPS database. The prognostic feature for LLPS in melanoma was developed in The Cancer Genome Atlas (TCGA) dataset and verified in the GSE65904 cohort. Based on risk scores, melanoma patients were categorized into high- and low-risk groups. Thereafter, the differences in clinicopathological correlation, functional enrichment, immune landscape, tumor mutational burden, and impact of immunotherapy between the two groups were investigated. Finally, the role of key gene TROAP in melanoma was validated by in vitro and in vivo experiments.

RESULTS

The LLPS-related gene signature was developed based on MLKL, PARVA, PKP1, PSME1, RNF114, and TROAP. The risk score was a crucial independent prognostic factor for melanoma and patients with high-risk scores were related to a worse prognosis. Approximately, all immune-relevant characteristics, such as immune cell infiltration and immune scores, were extremely evident in patients with low-risk scores. The findings from the in vitro and in vivo experiments indicated that the viability, proliferation, and invasion ability of melanoma cells were drastically decreased after the knockdown of TROAP.

CONCLUSION

Our gene signature can independently predict the survival of melanoma patients. It provides a basis for the exploration of the relationship between LLPS and melanoma and can offer a fresh perspective on the clinical diagnosis and treatment of the disease.

Association between HMGA1 and immunosuppression in hepatocellular carcinoma: A comprehensive bioinformatics analysis

The high mobility group A1 (HMGA1) gene is overexpressed in malignant tumors, and its expression level correlates with the progression and metastasis of tumors. However, the specific role of HMGA1 in hepatocellular carcinoma (HCC) and relevant influencing approaches in tumor immunity remain unclear. In this study, the expression and clinical significance of HMGA1 in HCC immunity were analyzed. The expression levels of HMGA1 mRNA and protein in HCC tissue and normal liver tissue were analyzed based on the cancer genome atlas, the gene expression omnibus and the Human Protein Atlas databases. The correlation between HMGA1 and clinicopathological factors was analyzed, and survival was estimated based on the expression of HMGA1. Gene set cancer analysis and the TISIDB database were used to identify tumor-infiltrating immune cells and immune inhibitors. Gene set enrichment analysis was performed to determine the involved signaling pathway. The HMGA1 genetic alterations were identified with the cBioPortal for Cancer Genomics. The expression of HMGA1 mRNA and protein was significantly higher in HCC tissue and negatively correlated with survival. Neutrophils, Th17 cells, several immune inhibitors, and signaling pathways were positively correlated with the expression of HMGA1. Amplification was the main type of genetic alteration in HMGA1. These findings demonstrate that HMGA1 can be a therapeutic target and a potential biomarker to predict the prognosis of patients with HCC. HMGA1 may affect the progression of HCC by suppressing the immune function of these patients.

A new ferroptosis-related genetic mutation risk model predicts the prognosis of skin cutaneous melanoma

Background: Ferroptosis is an iron-dependent cell death mode and closely linked to various cancers, including skin cutaneous melanoma (SKCM). Although attempts have been made to construct ferroptosis-related gene (FRG) signatures for predicting the prognosis of SKCM, the prognostic impact of ferroptosis-related genetic mutations in SKCM remains lacking. This study aims to develop a prediction model to explain the relationship between ferroptosis-related genetic mutations and clinical outcomes of SKCM patients and to explore the potential value of ferroptosis in SKCM treatment. Methods: FRGs which significantly correlated with the prognosis of SKCM were firstly screened based on their single-nucleotide variant (SNV) status by univariate Cox regression analysis. Subsequently, the least absolute shrinkage and selection operator (LASSO) and Cox regressions were performed to construct a new ferroptosis-related genetic mutation risk (FerrGR) model for predicting the prognosis of SKCM. We then illustrate the survival and receiver operating characteristic (ROC) curves to evaluate the predictive power of the FerrGR model. Moreover, independent prognostic factors, genomic and clinical characteristics, immunotherapy, immune infiltration, and sensitive drugs were compared between high-and low-FerrGR groups. Results: The FerrGR model was developed with a good performance on survival and ROC analysis. It was a robust independent prognostic indicator and followed a nomogram constructed to predict prognostic outcomes for SKCM patients. Besides, FerrGR combined with tumor mutational burden (TMB) or MSI (microsatellite instability) was considered as a combined biomarker for immunotherapy response. The high FerrGR group patients were associated with an inhibitory immune microenvironment. Furthermore, potential drugs target to high FerrGR samples were predicted. Conclusion: The FerrGR model is valuable to predict prognosis and immunotherapy in SKCM patients. It offers a novel therapeutic option for SKCM.

Mutations in lysine methyltransferase 2C and PEG3 are associated with tumor mutation burden, prognosis, and antitumor immunity in pancreatic adenocarcinoma patients

BACKGROUND

As a common cancer-related death worldwide, pancreatic adenocarcinoma (PAAD) has significantly increased mortality in recent years. In recent years, tumor mutation burden (TMB) has been regarded as the most popular biomarker for PAAD immunotherapy. However, it remains unclear which gene mutations affect TMB and immune response in pancreatic adenocarcinoma.

METHODS

The somatic mutation images of PAAD samples were downloaded from The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Based on the expression data of the TCGA and IGCC cohorts, various bioinformatics algorithms are used for evaluating the prognostic value and functional annotation of some frequently somatically mutated genes. Furthermore, the correlation between gene mutation and tumor immunity was also evaluated.

RESULTS

The results showed that lysine methyltransferase 2C (KMT2C) and paternally expressed 3 (PEG3) are frequently mutated genes in PAAD. Patients with KMT2C and PEG3 mutations have higher TMB severity and a lousy prognosis. In addition, the mutations of KMT2C and PEG3 genes positively regulate the metabolic and protein-related pathways in PAAD. Meanwhile, significant differences in the composition of the immune cells were observed for KMT2C and PEG3 mutations PAAD patients, for providing additional guidelines for antitumor treatments in various KMT2C and PEG3 mutation states in PAAD.

CONCLUSION

This study reveals that KMT2C and PEG3 mutation may serve as biomarkers for predicting prognosis and guiding anti-PAAD immunotherapy for PAAD patients.

Development and validation of a robust necroptosis related classifier for colon adenocarcinoma

Background: Necroptosis, a novel form of apoptosis, plays a crucial function in the progression of colon adenocarcinoma (COAD) and is expected to be triggered in cancer therapy for enhancing anti-tumor immunity. However, the function of necroptosis in tumors and its relationship with the tumor microenvironment (TME) remains largely unclear.

Methods: Necroptosis-related genes (NRGs) were collected from high-quality literature. Using The Cancer Genome Atlas (TCGA) (https://cancergenome.nih.gov) and the Gene Expression Omnibus (GEO) (www.ncbi.nlm.nih.gov/geo) meta-cohorts, a robust risk model was constructed to systematically examine the clinical value, functional status, the role of TME based on the risk model, as also the genomic variations.

Results: A risk model containing nine NRGs, including TNF receptor-associated factor (TRAF2), TNF receptor 1 associated via death domain (TRADD), ubiquitin carboxyl-terminal hydrolase 21 (USP21), TNF receptor superfamily, member 6 (FAS), tumor necrosis factor receptor superfamily 10B (TNFRSF10B), mitogen-activated protein kinase 8 (MAPK8), mixed lineage kinase domain-like (MLKL), TNF receptor-associated factor 5 (TRAF5), and recombinant receptor-interacting serine-threonine kinase 3 (RIPK3), was constructed. The risk model’s stability and accuracy were demonstrated in training, as also the validation cohorts; it was verified as an independent prognostic model for COAD. High-risk group patients developed “cold” tumors having active tumor proliferation and immunosuppression, while those in the low-risk group developed “hot” tumors with active immune and cell killing functions. Moreover, a higher number of copy number variations in the genome and fewer somatic mutations were found in high-risk group patients. Furthermore, higher sensitivity towards immunotherapy and chemotherapy was seen in patients of the low-risk group.

Conclusion: A reliable risk model based on NRGs to assess patient prognosis and guide clinical decision-making was constructed and validated. Our findings may contribute to the understanding of necroptosis and are expected to aid clinical management and guide precision treatment for patients with COAD.

Characterization of Fatty Acid Metabolism-Related Genes Landscape for Predicting Prognosis and Aiding Immunotherapy in Glioma Patients

Glioma is a highly malignant brain tumor with a poor survival rate. The involvement of fatty acid metabolism in glioma was examined to find viable treatment options. The information was gathered from the Cancer Genome Atlas (TCGA) and the Chinese Glioma Genome Atlas (CGGA) databases. A prognostic signature containing fatty acid metabolism-dependent genes (FAMDs) was developed to predict glioma outcome by multivariate and most minor absolute shrinkage and selection operator (LASSO) regression analyses. In the TCGA cohort, individuals with a good score had a worse prognosis than those with a poor score, validated in the CGGA cohort. According to further research by “pRRophetic” R package, higher-risk individuals were more susceptible to crizotinib. According to a complete study of the connection between the predictive risk rating model and tumor microenvironment (TME) features, high-risk individuals were eligible for activating the immune cell-associated receptor pathway. We also discovered that anti-PD-1/PD-L1 and anti-CTLA4 immunotherapy are more effective in high-risk individuals. Furthermore, we demonstrated that CCNA2 promotes glioma proliferation, migration, and invasion and regulates macrophage polarization. Therefore, examining the fatty acid metabolism pathway aids our understanding of TME invasion properties, allowing us to develop more effective immunotherapies for glioma.

Identification of several senescence-associated genes signature in head and neck squamous cell carcinoma

Background

As one of the core aging processes, cellular senescence is associated with tumorigenesis, growth, and immune modulation in cancers. Nevertheless, the prognosis of senescence‐associated genes (SAGs) signature in head and neck squamous cell carcinoma (HNSCC) remains to be further evaluated.

Methods

The transcriptome and corresponding clinical datasets of SAGs in patients with HNSCC were downloaded from public databases. A new prognostic SAGs signature was established with least absolute shrinkage and selection operator discussion. Patients with HNSCC were fallen into two risk groups based on each sample's risk mark and the cutoff point. The survival analysis was extended to determine the predictive accuracy of the SAGs signature. Furthermore, the evaluation of SAGs signature was made according to clinicopathological characteristics, survival state, the infiltration of inflammatory cells, and efficacy of immunotherapy.

Results

41 SAGs were recognized and adopted to establish the forecast signature. The survival analysis indicated that patients with HNSCC in the high‐senescent score group had significantly reduced overall survival compared with those in the low‐senescent score group. It was certified that the risk score of SAGs signature was a separate predicting agent for HNSCC applying Cox regression analysis. According to functional analysis, some immune‐associated pathways were increased in the low‐senescent score group significantly. High‐senescent score group was correlated with poor clinicopathological characteristics, given less the infiltration of inflammatory cells state and worse immunotherapeutic effect.

Conclusion

A new SAG signature predicting result and response to immunotherapy of HNSCC was identified. Cellular senescence may be a hidden target for HNSCC.

Evaluation of the TCR Repertoire as a Predictive and Prognostic Biomarker in Cancer: Diversity or Clonality?

Simple Summary

The TCR is the T cell antigen receptor, and it is responsible of the T cell activation, through the HLA-antigen complex recognition. Studying the TCR repertoire in patients with cancer can help to better understand the anti-tumoural responses and it has been suggested to have predictive and or/prognostic values, both for the disease and in response to treatments. The aim of this review is to summarize TCR repertoire studies performed in patients with cancer found in the literature, thoroughly analyse the different factors that can be involved in shaping the TCR repertoire, and draw the current conclusions in this field, especially focusing on whether the TCR diversity—or its opposite, the clonality—can be used as predictors or prognostic biomarkers of the disease.

Abstract

T cells play a vital role in the anti-tumoural response, and the presence of tumour-infiltrating lymphocytes has shown to be directly correlated with a good prognosis in several cancer types. Nevertheless, some patients presenting tumour-infiltrating lymphocytes do not have favourable outcomes. The TCR determines the specificities of T cells, so the analysis of the TCR repertoire has been recently considered to be a potential biomarker for patients’ progression and response to therapies with immune checkpoint inhibitors. The TCR repertoire is one of the multiple elements comprising the immune system and is conditioned by several factors, including tissue type, tumour mutational burden, and patients’ immunogenetics. Its study is crucial to understanding the anti-tumoural response, how to beneficially modulate the immune response with current or new treatments, and how to better predict the prognosis. Here, we present a critical review including essential studies on TCR repertoire conducted in patients with cancer with the aim to draw the current conclusions and try to elucidate whether it is better to encounter higher clonality with few TCRs at higher frequencies, or higher diversity with many different TCRs at lower frequencies.

Expression and Prognosis Value of the KLF Family Members in Colorectal Cancer

Krüppel-like factors (KLFs) are some kind of transcriptional regulator that regulates a broad range of cellular functions and has been linked to the development of certain malignancies. KLF expression patterns and prognostic values in colorectal cancer (CRC) have, however, been investigated rarely. To investigate the differential expression, predictive value, and gene mutations of KLFs in CRC patients, we used various online analytic tools, including ONCOMINE, TCGA, cBioPortal, and the TIMER database. KLF2-6, KLF8-10, KLF12-15, and KLF17 mRNA expression levels were dramatically downregulated in CRC tissues, but KLF1, KLF7, and KLF16 mRNA expression levels were significantly elevated in CRC tissues. According to the findings of Cox regression analysis, upregulation of KLF3, KLF5, and KLF6 and downregulation of KLF15 were linked with a better prognosis in CRC. For functional enrichment, our findings revealed that KLF members are involved in a variety of cancer-related biological processes. In colon cancer and rectal cancer, KLFs were also shown to be associated with a variety of immune cells. The findings of this research reveal that KLF family members' mRNA expression levels are possible prognostic indicators for patients with CRC.

Characterization of Aging-Related Genes to Predict Prognosis and Evaluate the Tumor Immune Microenvironment in Malignant Melanoma

Objective. Malignant melanoma (MM) is one of the most malignant types of skin cancer and its incidence and mortality rates are increasing worldwide. Aging is well recognized as a significant risk factor for cancer. However, few studies have analyzed in depth the association between aging-related genes (AGs) and malignant melanoma prognosis with tumor immune microenvironment. Methods. Here, we downloaded 471 MM patients from The Cancer Genome Atlas (TCGA) with RNA sequence and clinicopathological data. 58 AGs from the TCGA dataset were examined using Cox regression and the LASSO assay. As a result, a gene signature for aging-related genes was created. The time-dependent ROC curve and Kaplan–Meier analysis were calculated to determine its predictive capability. Moreover, we created a nomogram for the clinicopathologic variables and the AGs gene signature to determine overall survival (OS). We also explored the association between three immune checkpoints, immune cell infiltration, and the aging-related gene signature. Results. We established an aging risk model to identify and predict the immune microenvironment in malignant melanoma. Then we developed and validated a prognosis risk model using three AGs (CSNK1E, C1QA, and SOD-2) in the GSE65904 dataset. The aging signature was positively associated with clinical and molecular characteristics and can be used as a prognostic factor for malignant melanoma. The low aging risk score was associated with a poor prognosis and indicated an immunosuppressive microenvironment. Conclusions. To summarize, we established and validated a model of aging risk based on three aging-related genes that acted as an independent prognostic predictor of overall survival. Besides, it also characterized the immune response in the malignant melanoma microenvironment and could provide a potential indicator of individualized immunotherapy in malignant melanoma.

Exploration of Prognostic Immune-Related Genes and lncRNAs Biomarkers in Kidney Renal Clear Cell Carcinoma and Its Crosstalk with Acute Kidney Injury

Kidney renal clear cell carcinoma (KIRC) has a poor prognosis and a high death rate globally. Cancer prognosis is strongly linked to immune-related genes (IRGs), according to numerous research. We utilized KIRC RNA-seq data from the TCGA database to build a prognostic model incorporating seven immune-related (IR) lncRNAs, and we constructed the model using LASSO regression. Additionally, we calculated a risk score for each patient using a prognostic model that divided patients into high-risk and low-risk groups. The ESTIMATE and CIBERSORT methodologies were then used to analyze the differences in the tumor microenvironment of the two groups of patients. Finally, we predicted three small molecule drugs that may have potential therapeutic effects for high-risk patients. We combined the acute kidney injury dataset to obtain differential genes that may serve standard biological functions with two risk groups. Our study shows that the model we constructed for IR-lncRNAs has reliable predictive efficacy for patients with KIRC.

Characterization of the Immune Cell Infiltration Landscape of Thyroid Cancer for Improved Immunotherapy

Within the endocrine system, thyroid cancer (THCA) is the most typical malignant tumor. Tumor-infiltrating immune cells play vital roles in tumor progression, recurrence, metastasis as well as response to immunotherapy. However, THCA’s immune infiltrative landscape is still not clarified. Therefore, we utilized two statistical algorithms to investigate the immune cell infiltration (ICI) landscape of 505 THCA samples and defined three ICI immune subtypes. The ICI scores were calculated using principal-component analysis. Increased tumor mutation burden (TMB) and immune-related signaling pathways were associated to a high ICI score. The high ICI score group indicated a relatively longer overall survival (OS) than the low ICI score group. Most immune checkpoint-related and immune activation-related genes were considerably upregulated in the ICI high group, which indicates stronger immunogenicity and a greater likelihood of benefiting from immunotherapy. In two cohort studies of patients receiving immunotherapy, high-ICI-score group showed notable therapeutic effects and clinical advantages compared to those with lower ICI scores. These results demonstrate that ICI score acts as an effective prognostic indicator and predictor of response to immunotherapy.

Identification of subtypes correlated with tumor immunity and immunotherapy in cutaneous melanoma

Because immune checkpoint inhibitors (ICIs) are effective for a subset of melanoma patients, identification of melanoma subtypes responsive to ICIs is crucial. We performed clustering analyses to identify immune subtypes of melanoma based on the enrichment levels of 28 immune cells using transcriptome datasets for six melanoma cohorts, including four cohorts not treated with ICIs and two cohorts treated with ICIs. We identified three immune subtypes (Im-H, Im-M, and Im-L), reproducible in these cohorts. Im-H displayed strong immune signatures, low stemness and proliferation potential, genomic stability, high immunotherapy response rate, and favorable prognosis. Im-L showed weak immune signatures, high stemness and proliferation potential, genomic instability, low immunotherapy response rate, and unfavorable prognosis. The pathways highly enriched in Im-H included immune, MAPK, apoptosis, calcium, VEGF, cell adhesion molecules, focal adhesion, gap junction, and PPAR. The pathways highly enriched in Im-L included Hippo, cell cycle, and ErbB. Copy number alterations correlated inversely with immune signatures in melanoma, while tumor mutation burden showed no significant correlation. The molecular features correlated with favorable immunotherapy response included immune-promoting signatures and pathways of PPAR, MAPK, VEGF, calcium, and glycolysis/gluconeogenesis. Our data recapture the immunological heterogeneity in melanoma and provide clinical implications for the immunotherapy of melanoma.

Uncovering the Subtype-Specific Molecular Characteristics of Breast Cancer by Multiomics Analysis of Prognosis-Associated Genes, Driver Genes, Signaling Pathways, and Immune Activity

Breast cancer is a heterogeneous malignant disease with different prognoses and has been divided into four molecular subtypes. It is believed that molecular events occurring in breast stem/progenitor cells contribute to the carcinogenesis and development of different breast cancer subtypes. However, these subtype-specific molecular characteristics are largely unknown. In this study, we employed 1217 breast cancer samples from The Cancer Genome Atlas (TCGA) database for a multiomics analysis of the molecular characteristics of different breast cancer subtypes based on PAM50 algorithms. We detected the expression changes of subtype-specific genes and revealed that the expression of particular subtype-specific genes significantly affected prognosis. We also investigated the mutations and copy number variations (CNVs) of breast cancer driver genes and the representative genes of ten signaling pathways in different subtypes and revealed several subtype-specifically altered genes. Moreover, we detected the infiltration of various immune cells in different subtypes of breast cancer and showed that the infiltration levels of major immune cell types are different among these subtypes. Additionally, we investigated the factors affecting the immune infiltration level and the immune cytolytic activity in different breast cancer subtypes, namely, the mutation burden, genome instability and cancer-associated fibroblast (CAF) infiltration. This study may shed light on the molecular events contributing to carcinogenesis and development and provide potential markers and targets for the clinical diagnosis and treatment of different breast cancer subtypes.