The tumor immune microenvironmental analysis of 2,033 transcriptomes across 7 cancer types

Exosomes in tumor-stroma crosstalk: Shaping the immune microenvironment in colorectal cancer

Colorectal cancer (CRC) is a multifaceted disease characterized by a complex interaction between tumor cells and the surrounding microenvironment. Within this intricate landscape, exosomes have emerged as pivotal players in the tumor-stroma crosstalk, influencing the immune microenvironment of CRC. These nano-sized vesicles, secreted by both tumoral and stromal cells, serve as molecular transporters, delivering a heterogeneous mix of biomolecules such as RNAs, proteins, and lipids. In the CRC context, exosomes exert dual roles: they promote tumor growth, metastasis, and immune escape by altering immune cell functions and activating oncogenic signaling pathways and offer potential as biomarkers for early CRC detection and treatment targets. This review delves into the multifunctional roles of exosomes in the CRC immune microenvironment, highlighting their potential implications for future therapeutic strategies and clinical outcomes.

Oncometabolite 2-hydroxyglutarate regulates anti-tumor immunity

"Oncometabolite" 2-hydroxyglutarate (2-HG) is an aberrant metabolite found in tumor cells, exerting a pivotal influence on tumor progression. Recent studies have unveiled its impact on the proliferation, activation, and differentiation of anti-tumor T cells. Moreover, 2-HG regulates the function of innate immune components, including macrophages, dendritic cells, natural killer cells, and the complement system. Elevated levels of 2-HG hinder α-KG-dependent dioxygenases (α-KGDDs), contributing to tumorigenesis by disrupting epigenetic regulation, genome integrity, hypoxia-inducible factors (HIF) signaling, and cellular metabolism. The chiral molecular structure of 2-HG produces two enantiomers: D-2-HG and L-2-HG, each with distinct origins and biological functions. Efforts to inhibit D-2-HG and leverage the potential of L-2-HG have demonstrated efficacy in cancer immunotherapy. This review delves into the metabolism, biological functions, and impacts on the tumor immune microenvironment (TIME) of 2-HG, providing a comprehensive exploration of the intricate relationship between 2-HG and antitumor immunity. Additionally, we examine the potential clinical applications of targeted therapy for 2-HG, highlighting recent breakthroughs as well as the existing challenges.

Development and validation a prognostic model based on natural killer T cells marker genes for predicting prognosis and characterizing immune status in glioblastoma through integrated analysis of single-cell and bulk RNA sequencing

BACKGROUND

Glioblastoma (GBM) is an aggressive and unstoppable malignancy. Natural killer T (NKT) cells, characterized by specific markers, play pivotal roles in many tumor-associated pathophysiological processes. Therefore, investigating the functions and complex interactions of NKT cells is great interest for exploring GBM.

METHODS

We acquired a single-cell RNA-sequencing (scRNA-seq) dataset of GBM from Gene Expression Omnibus (GEO) database. The weighted correlation network analysis (WGCNA) was employed to further screen genes subpopulations. Subsequently, we integrated the GBM cohorts from The Cancer Genome Atlas (TCGA) and Chinese Glioma Genome Atlas (CGGA) databases to describe different subtypes by consensus clustering and developed a prognostic model by least absolute selection and shrinkage operator (LASSO) and multivariate Cox regression analysis. We further investigated differences in survival rates and clinical characteristics among different risk groups. Furthermore, a nomogram was developed by combining riskscore with the clinical characteristics. We investigated the abundance of immune cells in the tumor microenvironment (TME) by CIBERSORT and single sample gene set enrichment analysis (ssGSEA) algorithms. Immunotherapy efficacy assessment was done with the assistance of Tumor Immune Dysfunction and Exclusion (TIDE) and The Cancer Immunome Atlas (TCIA) databases. Real-time quantitative polymerase chain reaction (RT-qPCR) experiments and immunohistochemical profiles of tissues were utilized to validate model genes.

RESULTS

We identified 945 NKT cells marker genes from scRNA-seq data. Through further screening, 107 genes were accurately identified, of which 15 were significantly correlated with prognosis. We distinguished GBM samples into two distinct subtypes and successfully developed a robust prognostic prediction model. Survival analysis indicated that high expression of NKT cell marker genes was significantly associated with poor prognosis in GBM patients. Riskscore can be used as an independent prognostic factor. The nomogram was demonstrated remarkable utility in aiding clinical decision making. Tumor immune microenvironment analysis revealed significant differences of immune infiltration characteristics between different risk groups. In addition, the expression levels of immune checkpoint-associated genes were consistently elevated in the high-risk group, suggesting more prominent immune escape but also a stronger response to immune checkpoint inhibitors.

CONCLUSIONS

By integrating scRNA-seq and bulk RNA-seq data analysis, we successfully developed a prognostic prediction model that incorporates two pivotal NKT cells marker genes, namely, CD44 and TNFSF14. This model has exhibited outstanding performance in assessing the prognosis of GBM patients. Furthermore, we conducted a preliminary investigation into the immune microenvironment across various risk groups that contributes to uncover promising immunotherapeutic targets specific to GBM.

Identification of an immune subtype-related prognostic signature of clear cell renal cell carcinoma based on single-cell sequencing analysis

Background

There is growing evidence that immune cells are strongly associated with the prognosis and treatment of clear cell renal cell carcinoma (ccRCC). Our aim is to construct an immune subtype-related model to predict the prognosis of ccRCC patients and to provide guidance for finding appropriate treatment strategies.

Methods

Based on single-cell analysis of the GSE152938 dataset from the GEO database, we defined the immune subtype-related genes in ccRCC. Immediately afterwards, we used Cox regression and Lasso regression to build a prognostic model based on TCGA database. Then, we carried out a series of evaluation analyses around the model. Finally, we proved the role of VMP1 in ccRCC by cellular assays.

Result

Initially, based on TCGA ccRCC patient data and GEO ccRCC single-cell data, we successfully constructed a prognostic model consisting of five genes. Survival analysis showed that the higher the risk score, the worse the prognosis. We also found that the model had high predictive accuracy for patient prognosis through ROC analysis. In addition, we found that patients in the high-risk group had stronger immune cell infiltration and higher levels of immune checkpoint gene expression. Finally, cellular experiments demonstrated that when the VMP1 gene was knocked down, 786-O cells showed reduced proliferation, migration, and invasion ability and increased levels of apoptosis.

Conclusion

Our study can provide a reference for the diagnosis and treatment of patients with ccRCC.

Immune signature of pheochromocytoma and paraganglioma in context of neuroendocrine neoplasms associated with prognosis

PURPOSE

To understand prognostic immune cell infiltration signatures in neuroendocrine neoplasms (NENs), particularly pheochromocytoma and paraganglioma (PCPG), we analyzed tumor transcriptomic data from The Cancer Genome Atlas (TCGA) and other published tumor transcriptomic data of NENs.

METHODS

We used CIBERSORT to infer immune cell infiltrations from bulk tumor transcriptomic data from PCPGs, in comparison to gastroenteropancreatic neuroendocrine tumors (GEPNETs) and small cell lung carcinomas (SCLCs). PCPG immune signature was validated with NanoString immune panel in an independent cohort. Unsupervised clustering of the immune infiltration scores from CIBERSORT was used to find immune clusters. A prognostic immune score model for PCPGs and the other NENs were calculated as a linear combination of the estimated infiltration of activated CD8+/CD4+ T cells, activated NK cells, and M0 and M2 macrophages.

RESULTS

In PCPGs, we found five dominant immune clusters, associated with M2 macrophages, monocytes, activated NK cells, M0 macrophages and regulatory T cells, and CD8+/CD4+ T cells respectively. Non-metastatic tumors were associated with activated NK cells and metastatic tumors were associated with M0 macrophages and regulatory T cells. In GEPNETs and SCLCs, M0 macrophages and regulatory T cells were associated with unfavorable outcomes and features, such as metastasis and high-grade tumors. The prognostic immune score model for PCPGs and the NENs could predict non-aggressive and non-metastatic diseases. In PCPGs, the immune score was also an independent predictor of metastasis-free survival in a multivariate Cox regression analysis.

CONCLUSION

The transcriptomic immune signature in PCPG correlates with clinical features like metastasis and prognosis.

An immune infiltration-related long non-coding RNAs signature predicts prognosis for hepatocellular carcinoma

Background: With a high incidence and dismal survival rate, hepatocellular carcinoma (HCC) tops the list of the world's most frequent malignant tumors. Immunotherapy is a new approach to cancer treatment, and its effect on prolonging overall survival (OS) varies from patient to patient. For a more effective prognosis and treatment of HCC, we are committed to identifying immune infiltration-related long non-coding RNAs (IIRLs) with prognostic value in hepatocellular carcinoma. Methods: In our study, we calculated immune scores of 369 hepatocellular carcinoma samples from the Cancer Genome Atlas (TCGA) database by using an estimation algorithm, and obtained long non-coding RNAs (lncRNAs) associated with immune infiltration by using Weighted Gene Co-expression Network analysis (WGCNA). For training cohort, univariate Cox, least absolute shrinkage and selection operator (Lasso) and multivariate Cox regression analysis were used to determine prognostic IIRLs, we established a prognostic IIRLs signature. By testing cohort and entire cohort, we confirmed that the signature is practical. The prognosis of people with different clinicopathological stages and risk scores were predicted by the nomogram we constructed. In addition, Immune cell infiltration analysis and prediction of therapeutic drugs were performed. Results: 93 IIRLs were obtained by WGCNA. Furthermore, the prognostic value of these IIRLs were evaluated by using univariate Cox, Lasso and multivariate Cox analysis. Four IIRLs were used to create a signature with a prognosis. Time-related receiver operating characteristic (ROC) curve revealed that this model had an acceptable prognostic value for HCC patients. By using univariate and multivariate Cox regression analysis, this risk score has been shown to be an independent prognostic factor for HCC. The nomogram we made showed good predictions. Except for that, the treatment with immune checkpoint inhibitors (ICI) was likely to be more effective for low-risk patients. Conclusion: Based on four IIRLs, a prognostic signature was created in this research showed good accuracy in predicting OS. This study also provided valuable references for Immunotherapy of hepatocellular carcinoma.

New Biology of Pheochromocytoma and Paraganglioma

Pheochromocytomas and paragangliomas continue to be defined by significant morbidity and mortality despite their several recent advances in diagnosis, localization, and management. These adverse outcomes are largely related to mass effect as well as catecholamine-induced hypertension, tachyarrhythmias and consequent target organ damage, acute coronary syndromes, and strokes (ischemic and hemorrhagic stroke). Thus, a proper understanding of the physiology and pathophysiology of these tumors and recent advances are essential to affording optimal care. These major developments largely include a redefinition of metastatic behavior, a novel clinical categorization of these tumors into 3 genetic clusters, and an enhanced understanding of catecholamine metabolism and consequent specific biochemical phenotypes. Current advances in imaging of these tumors are shifting the paradigm from poorly specific anatomical modalities to more precise characterization of these tumors using the advent and development of functional imaging modalities. Furthermore, recent advances have revealed new molecular events in these tumors that are linked to their genetic landscape and, therefore, provide new therapeutic platforms. A few of these prospective therapies translated into new clinical trials, especially for patients with metastatic or inoperable tumors. Finally, outcomes are ever-improving as patients are cared for at centers with cumulative experience and well-established multidisciplinary tumor boards. In parallel, these centers have supported national and international collaborative efforts and worldwide clinical trials. These concerted efforts have led to improved guidelines collaboratively developed by healthcare professionals with a growing expertise in these tumors and consequently improving detection, prevention, and identification of genetic susceptibility genes in these patients.

The Tumor Microenvironment Reprograms Immune Cells

Tumor tissue comprises a highly complex network of diverse cell types. The tumor microenvironment (TME) can be mainly subdivided into cancer cells and stromal cell compartments, the latter include different types of immune cells, fibroblasts, endothelial cells, and pericytes. Tumor cells reprogram immune cells and other stromal cells in the TME to constrain their antitumor capacity by creating an immunosuppressive milieu and metabolism competition. Moreover, the reprogramming effect on immune cells is localized not only in the tumor but also at the systemic level. With wide application of single-cell sequencing technology, tumor-specific characteristics of immune cells and other stromal cells in the TME have been dissected. In this review, we mainly focus on how tumor cells reprogram immune cells both within the TME and peripheral blood. This information can further help us to improve the efficiency of current immunotherapy as well as bring up new ideas to combat cancer.

Three-dimensional in vitro culture models in oncology research

Cancer is a multifactorial disease that is responsible for 10 million deaths per year. The intra- and inter-heterogeneity of malignant tumors make it difficult to develop single targeted approaches. Similarly, their diversity requires various models to investigate the mechanisms involved in cancer initiation, progression, drug resistance and recurrence. Of the in vitro cell-based models, monolayer adherent (also known as 2D culture) cell cultures have been used for the longest time. However, it appears that they are often less appropriate than the three-dimensional (3D) cell culture approach for mimicking the biological behavior of tumor cells, in particular the mechanisms leading to therapeutic escape and drug resistance. Multicellular tumor spheroids are widely used to study cancers in 3D, and can be generated by a multiplicity of techniques, such as liquid-based and scaffold-based 3D cultures, microfluidics and bioprinting. Organoids are more complex 3D models than multicellular tumor spheroids because they are generated from stem cells isolated from patients and are considered as powerful tools to reproduce the disease development in vitro. The present review provides an overview of the various 3D culture models that have been set up to study cancer development and drug response. The advantages of 3D models compared to 2D cell cultures, the limitations, and the fields of application of these models and their techniques of production are also discussed.

TIMM13 as a prognostic biomarker and associated with immune infiltration in skin cutaneous melanoma (SKCM)

Objective

Providing protection against aggregation and guiding hydrophobic precursors through the mitochondria’s intermembrane space, this protein functions as a chaperone-like protein. SLC25A12 is imported by TIMM8 as a result of its interaction with TIMM13. In spite of this, it is still unknown how TIMM13 interacts with skin cutaneous melanoma (SKCM) and tumor-infiltrating lymphocytes (TILs).

Methods

Aberrant expression of TIMM13 in SKCM and its clinical outcome was evaluated with the help of multiple databases, including the Xiantao tool (https://www.xiantao.love/), HPA, and UALCAN. TISIDB and Tumor Immune Estimation Resources (TIMER) databases were applied to explore the association between TIMM13 and tumor infiltration immune cells. OS nomogram was constructed, and model performance was examined. Finally, TIMM13 protein expression was validated by immunohistochemistry (IHC).

Results

TIMM13 expression was higher in SKCM samples than in peritumor samples. TIMM13 was strongly associated with sample type, subgroup, cancer stage, lymph node stage, and worse survival. Further, upregulation of TIMM13 was significantly associated with immunoregulators, and chemokines, as well as T cells, B cells, monocytes, neutrophils, macrophages, and T-cell regulators. An analysis of bioinformatic data uncovered that TIMM13 expression was strongly associated with PD1 (T-cell exhaustion marker). The nomogram showed good predictive performance based on calibration plot. TIMM13 was highly expressed in melanoma tissue samples than in normal samples.

Conclusion

In brief, TIMM13 may be a prognostic biomarker for SKCM. It might modulate the tumor immune microenvironment and lead to a poorer prognosis. In addition, it is necessary to study the targeted therapy of TIMM13.

Integrative Bioinformatics Analysis Reveals CHEK1 and UBE2C as Luminal A Breast Cancer Subtype Biomarkers

In light of the limited number of targetable oncogenic drivers in breast cancer (BRCA), it is important to identify effective and druggable gene targets for the treatment of this devastating disease. Herein, the GSE102484 dataset containing expression profiling data from 683 BRCA patients was re-analyzed using weighted gene co-expression network analysis (WGCNA). The yellow module with the highest correlation to BRCA progression was screened out, followed by functional enrichment analysis and establishment of a protein–protein interaction (PPI) network. After further validation through survival analysis and expression evaluation, CHEK1 and UBE2C were finally identified as hub genes related to the progression of BRCA, especially the luminal A breast cancer subtype. Notably, both hub genes were found to be dysregulated in multiple types of immune cells and closely correlated with tumor infiltration, as revealed by Tumor Immune Estimation Resource (TIMER) along with other bioinformatic tools. Construction of transcription factors (TF)-hub gene network further confirmed the existence of 11 TFs which could regulate both hub genes simultaneously. Our present study may facilitate the invention of targeted therapeutic drugs and provide novel insights into the understanding of the mechanism beneath the progression of BRCA.

Histology-agnostic approvals for antibody-drug conjugates in solid tumours: is the time ripe?

Several antibody-drug conjugates (ADCs) have been recently approved to treat solid tumours. Since ADCs seem to have activity in multiple malignancies sharing the expression of a specific antigen, they may be mirroring the experience of histology-agnostic-targeted treatments. So, the possibility to interpret the activity of some ADCs across different cancer types in a biomarker-driven perspective arises. However, relevant biological, methodological, and regulatory challenges should be highlighted and addressed, in order to grant ADCs biomarker-driven regulatory approvals in the next future. In this review, we discuss challenges and opportunities posed by the pan-histological expansion of ADCs in solid tumours. In particular, we provide an overview about technological and manufacturing advancements; we offer up-to-date highlights of the current evidence from clinical trials investigating ADCs in solid tumours; we discuss the need for the identification of optimal predictive biomarkers, as well as major methodological, statistical, and regulatory considerations for a biomarker-driven histology-agnostic approach.

DNA Methylation based prognostic subtypes of chordoma tumors in tissue and plasma

BACKGROUND

Chordomas are rare malignant bone cancers of the skull-base and spine. Patient survival is variable and not reliably predicted using clinical factors or molecular features. This study identifies prognostic epigenetic chordoma subtypes that are detected non-invasively using plasma methylomes.

METHODS

Methylation profiles of 68 chordoma surgical samples were obtained between 1996-2018 across three international centres along with matched plasma methylomes where available.

RESULTS

Consensus clustering identified two stable tissue clusters with a disease-specific survival difference that was independent of clinical factors in a multivariate Cox analysis (HR=14.2, 95%CI: 2.1-94.8, p=0.0063). Immune-related pathways with genes hypomethylated at promoters and increased immune cell abundance were observed in the poor-performing "Immune-infiltrated" subtype. Cell-to-cell interaction plus extracellular matrix pathway hypomethylation and higher tumor purity was observed in the better-performing "Cellular" subtype. The findings were validated in additional DNA methylation and RNA sequencing datasets as well as with immunohistochemical staining. Plasma methylomes distinguished chordomas from other clinical differential diagnoses by applying fifty chordoma-versus-other binomial generalized linear models in random 20% testing sets (mean AUROC=0.84, 95%CI: 0.52-1.00). Tissue-based and plasma-based methylation signals were highly correlated in both prognostic clusters. Additionally, leave-one-out models accurately classified all tumors into their correct cluster based on plasma methylation data.

CONCLUSIONS

Here, we show the first identification of prognostic epigenetic chordoma subtypes and first use of plasma methylome-based biomarkers to non-invasively diagnose and subtype chordomas. These results may transform patient management by allowing treatment aggressiveness to be balanced with patient risk according to prognosis.

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.

Identification and Validation of a Tumor Microenvironment-Related Gene Signature for Prognostic Prediction in Advanced-Stage Non-Small-Cell Lung Cancer

The development of immunotherapy has greatly changed the advanced-stage non-small-cell lung cancer (NSCLC) treatment landscape. The complexity and heterogeneity of tumor microenvironment (TME) lead to discrepant immunotherapy effects among patients at the same pathologic stages. This study is aimed at exploring potential biomarkers of immunotherapy and accurately predicting the prognosis for advanced NSCLC patients. RNA-seq data and clinical information on stage III/IV NSCLC were obtained from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO). In TCGA-NSCLC with stage III/IV (n = 192), immune scores and stromal scores were calculated by using the ESTIMATE algorithms. Univariate, LASSO, and multivariate Cox regression analyses were performed to screen prognostic TME-related genes (TMERGs) and constructed a gene signature risk score model. It was validated in external dataset including GSE41271 (n = 91) and GSE81089 (n = 36). Additionally, a nomogram incorporating TMERG signature risk score and clinical characteristics was established. Further, we accessed the proportion of 22 types of tumor-infiltrating immune cells (TIIC) from the CIBERSORT website and analyzed the difference between two risk groups. OS of patients with high immune/stromal scores were higher (log-rank P = 0.044/log-rank P = 0.048). Multivariate Cox regression identified six prognostic TMERGs, including CD200, CHI3L2, CNTN1, CTSL, FYB1, and SLC52A1. We developed a six-gene risk score model, which was validated as an independent prognostic factor for OS (HR: 3.32, 95% CI: 2.16-5.09). Time-ROC curves showed useful discrimination for TCGA-NSCLC cohort (1-, 2-, and 3-year AUCs were 0.718, 0.761, and 0.750). The predictive robustness was validated in the external dataset. The C-index and 1-, 2-, and 3-year AUCs of nomogram were the largest, which demonstrated the nomogram had the greatest predictive accuracy and effectiveness and could be used for clinical guidance. Besides, the increased infiltration of T cells regulatory (Tregs) and macrophages M2 in the high-risk group suggested that chronic inflammation may reduce survival probability in patients with advanced NSCLC. We conducted a comprehensive analysis of the tumor microenvironment and identified the TMERG signature, which could predict prognosis accurately and provide a reference for the personalized immunotherapy for advanced NSCLC patients.

Integrative Genomic and Transcriptomic Analyses of Tumor Suppressor Genes and Their Role on Tumor Microenvironment and Immunity in Lung Squamous Cell Carcinoma

Non-small-cell lung cancers (NSCLCs) are largely classified into lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), which have different therapeutic options according to its molecular profiles and immune checkpoint expression, especially PD-L1, which is a suppressive factor in the tumor microenvironment. The tumor microenvironment can be altered by the genomic mutations on specific innate immune genes as well as tumor suppressor genes, so it is essential to comprehend the association between tumor microenvironment and tumor suppressor genes to discover the promising immunotherapeutic strategy to overcome the resistance of immune check point blockade. In this study, we aimed to analyze how the somatic mutations in tumor suppressor genes affect the tumor immune microenvironment through a comprehensive analysis of mutational profiling on the representative tumor suppressor genes (TP53, CDKN2A, PTEN, RB1, BRCA1, BRCA2) and immune gene expression in The Cancer Genome Atlas (TCGA) 155 lung squamous cell carcinoma (LUSC) and 196 lung adenocarcinoma (LUAD) samples. Several microenvironmental factors, such as the infiltrating immune and stromal cells, were suppressed by the mutated tumor suppressor genes in LUSC, unlike in the LUAD samples. In particular, infiltrating immune cells such as macrophage, neutrophil, and dendritic cells were significantly reduced in tumors with mutated tumor suppressor genes' group. In addition, the gene expressions for interleukin production and lymphocyte differentiation and PGC, C7, HGF, PLA2G2A, IL1RL1, CCR2, ALOX15B, CXCL11, FCN3 were significantly down-regulated, which were key immune genes for the cross-talk between LUSC microenvironment and tumor suppressors. Therefore, we generated evidence that TSG mutations in LUSC have an impact on tumor immune microenvironment, which suggests that TSG non-mutated patients will have the more inflamed tumors and are more likely to respond to immune checkpoint blockade therapy.

Comparison of Methods for Feature Selection in Clustering of High-Dimensional RNA-Sequencing Data to Identify Cancer Subtypes

Cancer subtype identification is important to facilitate cancer diagnosis and select effective treatments. Clustering of cancer patients based on high-dimensional RNA-sequencing data can be used to detect novel subtypes, but only a subset of the features (e.g., genes) contains information related to the cancer subtype. Therefore, it is reasonable to assume that the clustering should be based on a set of carefully selected features rather than all features. Several feature selection methods have been proposed, but how and when to use these methods are still poorly understood. Thirteen feature selection methods were evaluated on four human cancer data sets, all with known subtypes (gold standards), which were only used for evaluation. The methods were characterized by considering mean expression and standard deviation (SD) of the selected genes, the overlap with other methods and their clustering performance, obtained comparing the clustering result with the gold standard using the adjusted Rand index (ARI). The results were compared to a supervised approach as a positive control and two negative controls in which either a random selection of genes or all genes were included. For all data sets, the best feature selection approach outperformed the negative control and for two data sets the gain was substantial with ARI increasing from (-0.01, 0.39) to (0.66, 0.72), respectively. No feature selection method completely outperformed the others but using the dip-rest statistic to select 1000 genes was overall a good choice. The commonly used approach, where genes with the highest SDs are selected, did not perform well in our study.

The Periphery of Salivary Gland Carcinoma Tumors Reveals a PD-L1/PD-1 Biomarker Niche for the Evaluation of Disease Severity and Tumor-Immune System Interplay

The treatment options for patients with advanced salivary gland cancers (SGCs) are limited. Immune checkpoint inhibitors (ICIs) have revolutionized cancer treatment. However, the response to ICI immunotherapy is largely driven by the immune cell signatures within the tumor tissue and the para-tumoral tissue compartments. To date, there are no data on the expression of programed cell death protein-1/programed cell death protein-ligand 1 (PD-1/PD-L1) in SGC, which may enable the implementation of ICI immunotherapy for this disease. Thus, we performed an immunohistochemical analysis of PD-1 and PD-L1 expression in tumor cells and tumor-infiltrating immune cells (TIICs) in the tumor center and periphery of 62 SGC patients. The tumor periphery showed significantly higher expression of PD-L1 in tumor cells than in TIICs. Moreover, peripheral TIICs had significantly higher PD-1 expression than peripheral tumor cells. PD-1-positive tumor cells were detected exclusively in the tumor center of high-grade tumors, and most importantly, the presence of lymph node (LN) metastases and primary tumor stage significantly correlated with the presence of PD-L1-positive tumor cells in the tumor periphery. The PD-1/PD-L1 molecular signatures in SGC are clustered predominantly in the tumor periphery, reflect disease severity, and may predict the response to ICI immunotherapy in SGC patients.

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

The heterogeneity and complexity of tumor-immune microenvironments lead to diverse immunotherapy effects among colon cancer patients. It is crucial to identify immune microenvironment-related biomarkers and construct prognostic risk models. In this study, the immune and stromal scores of 415 cases from TCGA were calculated using the ESTIMATE algorithm. AXIN2, CCL22, CLEC10A, CRIP2, RUNX3, and TRPM5 were screened and established a prognostic immune-related gene (IRG) signature using by univariate, LASSO, and multivariate Cox regression models. The predicted performance of IRG signature was external validated by GSE39582 (n=519). Stratified survival analysis showed IRG signature was an effective predictor of survival in patients with different clinical characteristics. The protein expression level of six genes was validated by immunohistochemistry analysis. Difference analysis indicated the mutation rate, immune cell of resting NK cells and regulatory T cells infiltration and four immune checkpoints of PD-1, PD-L1, LAG3 and VSIR expression levels in the high-risk group were significantly higher than those in the low-risk group. A nomogram incorporating the gene signatures and clinical factors was demonstrated had a good accuracy (1-, 3-, and 5-year AUC= 0.799, 0.791, 0.738). Our study identified a novel IRG signature, which may provide some references for the clinical precision immunotherapy of patients.