Proportion Of Tumor-infiltrating Immune Cells Research Articles

Comprehensive analysis of NOTCH pathway with tumor environment in pancreatic adenocarcinoma

Abstract Objectives Pancreatic adenocarcinoma (PAAD) ranks among the most prevalent malignant neoplasms, and multiple pathways are involved in its pathogenesis, including the NOTCH pathway. However, the variable biological functions of the pathway in PAAD are controversial. Methods RNA-seq data for PAAD was analyzed using data from The Cancer Genome Atlas and Genotype-Tissue Expression databases. Utilizing Kaplan-Meier survival curves and Cox regression analyses, we examined the prognostic significance. The tumor microenvironment and immunotherapy responses were investigated using ssGSEA, ESTIMATE, and TIDE models. Functional enrichment analysis was used to explore gene functions. Results We identified NOTCH2, JAG1, NOTCH4, and DLL3 as high-priority members of the NOTCH pathway that modulates PAAD. Elevated NOTCH2 and JAG1 levels were markedly linked to reduced overall survival (OS), while increased NOTCH4 and DLL3 levels were significantly related to extended OS. Immune analyses showed that NOTCH-based scores were closely related to the immune microenvironment. NOTCH scores were not only closely correlated with tumor-infiltrating immune cells, but also with immunologically activated and immune checkpoint gene expression. The high NOTCH score group had a higher proportion of tumor-infiltrating immune cells and had better responses to immune checkpoint inhibitor therapy. Conclusions These data indicate that NOTCH2, JAG1, NOTCH4, and DLL3 could function as efficient prognostic biomarkers and therapeutic targets in PAAD, and patients with a high NOTCH score may have a significant response to immune checkpoint inhibitor treatment.

Abnormally High Expression of DNAJB6 Accelerates Malignant Progression of Lung Adenocarcinoma.

DNAJB6, a major member of the DNAJ/HSP40 family, plays an important role in tumor development. We explored the effect of DNAJB6 expression on the prognosis of patients and its biological role in lung adenocarcinoma (LUAD). mRNA and clinical data were obtained from The Cancer Genome Atlas (TCGA). Enriched pathways were determined by the Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses. A nomogram incorporating DNAJB6 and three clinical features was constructed to predict the survival rate. DNAJB6 expression and function in LUAD were explored using immunohistochemistry, Western blotting, proliferation, cell cycle analysis, RNA sequencing, and xenograft tumor assays. DNAJB6 mRNA levels were elevated in the LUAD-TCGA dataset. DNAJB6 protein levels were higher in LUAD tumor tissues than in normal tissues. A high DNAJB6 level was an independent risk factor for poor prognosis in patients with LUAD. The proportion of tumor-infiltrating immune cells significantly differed between high and low DNAJB6 expression. DNAJB6 was associated with cell cycle pathways; therefore, its knockdown induced G2/M cell cycle arrest and inhibited LUAD cell proliferation. This is the first report of the DNAJB6 requirement for LUAD cell proliferation and its potentially crucial role in LUAD prognosis.

Prediction of Tumor-Associated Macrophages and Immunotherapy Benefits Using Weakly Supervised Contrastive Learning in Breast Cancer Pathology Images.

The efficacy of immune checkpoint inhibitors is significantly influenced by the tumor immune microenvironment (TIME). RNA sequencing of tumor tissue can offer valuable insights into TIME, but its high cost and long turnaround time seriously restrict its utility in routine clinical examinations. Several recent studies have suggested that ultrahigh-resolution pathology images can infer cellular and molecular characteristics. However, few study pay attention to the quantitative estimation of various tumor infiltration immune cells from pathology images. In this paper, we integrated contrastive learning and weakly supervised learning to infer tumor-associated macrophages and potential immunotherapy benefit from whole slide images (WSIs) of H &E stained pathological sections. We split the high-resolution WSIs into tiles and then apply contrastive learning to extract features of each tile. After aggregating the features at the tile level, we employ weak supervisory signals to fine-tune the encoder for various downstream tasks. Comprehensive experiments on two independent breast cancer cohorts and spatial transcriptomics data demonstrate that the computational pathological features accurately predict the proportion of tumor-infiltrating immune cells, particularly the infiltration level of macrophages, as well as the immune subtypes and potential immunotherapy benefit. These findings demonstrate that our model effectively captures pathological features beyond human vision, establishing a mapping relationship between cellular compositions and histological morphology, thus expanding the clinical applications of digital pathology images.

The anoikis-related gene signature predicts survival and correlates with immune infiltration in osteosarcoma.

Anoikis is essential for the progression of many malignant tumors. However, the understanding of anoikis' roles in osteosarcoma remains scarce. This study conducted an extensive bioinformatics analysis to identify anoikis-related genes (ARGs), developed ARGs modeles for predicting OS and RFS, and evaluated the effect of these ARGs on osteosarcoma cell migration and invasion. The GSE16088 and GSE28425 datasets provided the differentially expressed genes (DEGs). The prognostic significance and functions of these DEGs were systematically investigated using several bioinformatics techniques. Transwell assays were conducted to determine the effect of OGT on osteosarcoma cell migration and invasion. Seven genes were identified as hub genes, including FN1, CD44, HRAS, TP53, PPARG, CTNNB1, and VEGFA, while 71 ARGs were identified as DEGs. Four ARGs-BRMS, COL4A2, FGF2, and OGT-were used to develop an RFS-predicting model, whereas seven ARGs-CD24, FASN, MMP2, EIF2AK3, ID2, PPARG, and PIK3R3-were used to develop an OS-predicting model in patients with osteosarcoma. In both the training and validation cohorts, high-risk group patients had significantly shorter OS and RFS duration than low-risk group patients. Furthermore, using the aforementioned ARGs, we developed clinically applicable nomograms for OS and RFS prediction. The proportion of tumor-infiltrating immune cells was significantly linked to risk scores. In vitro experiments revealed that knocking down OGT significantly inhibited the ability of MG63 and U2OS cells to invade and migrate. ARG-based gene signatures reliably predicted RFS and OS in osteosarcoma, and OGT showed promise as a potential biomarker. These findings contribute to a better understanding of ARGs' prognostic roles in osteosarcoma.

The microRNA target site profile is a novel biomarker in the immunotherapy response.

MicroRNAs (miRNAs) bind on the 3' untranslated region (3'UTR) of messenger RNAs (mRNAs) and regulate mRNA expression in physiological and pathological conditions, including cancer. Thus, studies have identified miRNAs as potential biomarkers by correlating the miRNA expression with the expression of important mRNAs and/or clinical outcomes in cancers. However, tumors undergo pervasive 3'UTR shortening/lengthening events through alternative polyadenylation (APA), which varies the number of miRNA target sites in mRNA, raising the number of miRNA target sites (numTS) as another important regulatory axis of the miRNA binding effects. In this study, we developed the first statistical method, BIOMATA-APA, to identify predictive miRNAs based on numTS features. Running BIOMATA-APA on The Cancer Genome Atlas (TCGA) and independent cohort data both with immunotherapy and no immunotherapy, we demonstrated for the first time that the numTS feature 1) distinguishes different cancer types, 2) predicts tumor proliferation and immune infiltration status, 3) explains more variation in the proportion of tumor-infiltrating immune cells, 4) predicts response to immune checkpoint blockade (ICB) therapy, and 5) adds prognostic power beyond clinical and miRNA expression. To the best of our knowledge, this is the first pan-cancer study to systematically demonstrate numTS as a novel type of biomarker representing the miRNA binding effects underlying tumorigenesis and pave the way to incorporate miRNA target sites for miRNA biomarker identification. Another advantage of examining the miRNA binding effect using numTS is that it requires only RNA-Seq data, not miRNAs, thus resulting in high power in the miRNA biomarker identification.

Immunoglobulin superfamily 6 is a molecule involved in the anti-tumor activity of macrophages in lung adenocarcinoma

BackgroundImmunoglobulin superfamily 6 (IGSF6) is a novel member of the immunoglobulin superfamily and has been implicated in various diseases. However, the specific role of IGSF6 in the anti-tumor immunity within lung adenocarcinoma (LUAD) remains unclear.MethodsWe analyzed the IGSF6 expression in LUAD using data from TCGA, and we performed qRT-PCR and western blotting to validate these findings using tissue samples obtained from LUAD patients. Images of IHC staining were obtained from HPA. To assess the clinical relevance of IGSF6 expression, we utilized UALCAN and SPSS to analyze its association with major clinical features of LUAD. Additionally, we employed ROC curves and survival analysis to evaluate the potential diagnostic and prognostic value of IGSF6 in LUAD. To gain insights into the functional implications of IGSF6, we performed enrichment analysis using the R software clusterProfiler package. Moreover, we utilized TIMER2.0 and TISIDB to investigate the relationship between IGSF6 and immune infiltrates in LUAD. The proportion of tumor-infiltrating immune cells in LUAD was assessed using FCM, and their correlation with IGSF6 expression in tumor tissues was analyzed. The localization of IGSF6 protein on macrophages was confirmed using the HPA and FCM. To determine the regulatory role of IGSF6 on macrophage activity in LUAD, we employed ELISA, FCM, and tumor-bearing models.ResultsWe discovered that both IGSF6 mRNA and protein levels were significantly decreased in LUAD. Additionally, we observed a negative correlation between IGSF6 expression and TNM stages as well as pathologic stages in LUAD. Notably, IGSF6 exhibited high sensitivity and specificity in diagnosing LUAD, and was positively associated with the survival rate of LUAD patients. Furthermore, IGSF6 expression was closely linked to gene sets involved in immune response. IGSF6 expression showed a positive correlation with immune infiltrates exhibiting anti-tumor activity, particularly M1 macrophages. We confirmed the predominant localization of the IGSF6 protein on the membrane of M1 macrophages. Importantly, the knockdown of IGSF6 resulted in a reduction in the anti-tumor activity of M1 macrophages, thereby promoting tumor progression.ConclusionIGSF6 is a molecule that is essential for the anti-tumor activity of macrophages in LUAD.

A Novel Patient-Derived Xenograft Model IO-FIVE for Drug Discovery and Precision Medicine in Lymphoma and Leukemia Patients

Lymphoma and leukemia have been the top two hematopoietic cancers in US, leading to more than 40,000 deaths each year. Although the 5-year relative survival rates have been improved up to 60%, there is still a lot of patients showed not response to standard therapy. Patient-Derived Xenograft model (PDX model), as the classical mouse xenograft tumor model that best represents the genetic information characteristics of human tumors, can be used to predict the therapeutic effect, and develop individualized treatment for patients. Its greatest advantage is that the model retains the microenvironment of the original tumor cells, inherits all the molecular biological properties of the primary tumor, and preserves the heterogeneity of the tumor. However, due to the factors that the loss of tumor heterogeneity in hematopoietic cancers after ex vivo clonal expansion and selection, and this in vitro assay based system could not fully reflect the host response to the drugs, including a range of novel immunotherapeutic agents such as PD-1 monoclonal antibody, CD38 monoclonal antibody, etc. With the increasing clinical use of oncology immunological agents, recently we are developing a new assay, named IO-FIVE (Immuno-Oncology drugs Fast In Vivo Efficacy test) for research and therapy, by retaining an appropriate proportion of tumor-infiltrating immune cells (and stromal cells) mixed with tumor cells when digesting clinical tumor samples or patient-derived xenografts-preserved tumor samples into cell suspensions, and then injecting them into the specialized IO-FIVE device, a modified microencapsulation and hollow fiber culture system (OncoVee® MiniPDX), which is implanted subcutaneously into mice and administered systematically for immuno-drug susceptibility testing to screen for immuno-drugs or combinations suitable for individual treatment. The IO-FIVE testing cycle takes only 14 days and is performed using Celltiter Glo before and after the injection of cell suspensions into the specialized device. Celltiter Glo, flow cytometry and Omics (RNA-seq, DNA-seq) are used to measure the viability of total cell subpopulations, the relative ratio of tumor cells to immune cells and the alteration of cellular transcription levels in the device, in order to further explore the sensitivity of tumor tissues to the immunotherapeutic drugs and the potential molecular mechanism for the responders and non-responders. Immune regulatory CD38 antibody Daratumumab has been tested in more than 40 acute myeloid leukemia (AML) cases, with its overall in vivo efficacy around 25%, which is consistent with previous research. It is interesting to uncover novel pathways for drug discovery and clinic treatment since CD38 is well expressed in most AML patients, but at least half of those CD38 + AML patients are not sensitive or resistant to Daratumumab monotherapy. Expression of CD38 on AML is not enough to distinguish the responder vs non-responder of Daratumumab. IO-FIVE could potentially be a companion diagnosis on AML for the patient stratification of Daratumumab. PD1 antibody Sintilimab has been tested in all the patients (12 lymphoma, 52 leukemia), and the only two laboratory PD1-Ab non-responsive T cell lymphoma patients have been showed to be clinically non-responders as well. Immunotherapy and chemical or targeted therapy combination could enhance tumor killing ability and disease control. We will use the IO-FIVE data to further compare it with its source of real-world clinical patients to obtain more evidence-based evidence that can predict individualized treatment with immunotherapy.

Low OGDHL expression affects the prognosis and immune infiltration of kidney renal clear cell carcinoma.

Oxoglutarate dehydrogenase-like (OGDHL) modulates glutamine metabolism to influence tumor progression. Therefore, we aimed to explore the potential role of OGDHL in the prognosis of kidney renal clear cell carcinoma (KIRC) and its effect on immune infiltration. The Cancer Genome Atlas, Tumor Immune Estimation Resource, Gene Expression Profiling Interactive Analysis, Human Protein Atlas, and The University of Alabama at Birmingham Cancer databases and the GSE53757 dataset were utilized to analyze expression difference and prognosis of OGDHL in tumor and normal tissue; diagnostic value was assessed using receiver operating characteristic curves. Correlations with clinical features and survival prognosis were analyzed. Independent prognostic factors were identified using univariate and multifactorial Cox regression analysis. We used the CIBERSORT analysis tool to discover the proportion of tumor-infiltrating immune cells (TIICs) in KIRC patients. Next, the differences in the proportion of TIICs under different OGDHL expression were analyzed. Finally, we explored the potential mechanisms by which OGDHL expression affects patient survival using Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG), and Gene Set Enrichment Analysis (GSEA). OGDHL expression was markedly downregulated in KIRC tissues compared to in normal tissues, and the downregulation of OGDHL expression was significantly associated with tumor progression (including tumor stage and grade) and poor prognosis. Cox regression analyses revealed OGDHL to be an independent prognostic factor for KIRC. CIBERSORT analysis showed that OGDHL expression is associated with differences in the proportion of several TIICs, particularly resting mast cells. Finally, GO and KEGG analysis showed that OGDHL was associated with extracellular matrix and epithelial cell differentiation involved in kidney development. GSEA indicated that low OGDHL was closely related to the activation of carcinogenic signaling pathways, including epithelial mesenchymal transition, tumor necrosis factor alpha and nuclear factor kappa B signaling pathway, negative regulation of apoptotic signaling, collagen formation, etc. OGDHL level can be monitored for diagnosing KIRC. Reduced expression is associated with poor prognosis and immune infiltration of KIRC. OGDHL is expected to become a new target for the treatment of KIRC.

ATP2C2 as a novel immune-related marker that defines the tumor microenvironment in triple-negative breast cancer.

Triple-negative breast cancer (TNBC) is an aggressive cancer that affects about 13/100,000 women yearly. Patients with TNBC are often resistant to endocrine and molecular targeted therapy, making clinical treatment challenging. Researches indicate that tumor microenvironment (TME) is related to prognosis in many cancers. Therefore, we aim to identify TME immune-related biomarkers to enhance the prognosis and immunotherapy efficacy in patients with TNBC. The bulk mRNA transcriptome data and clinical information of the (GSE58812) and (GSE25055) datasets were downloaded from the Gene Expression Omnibus (GEO) database, and the ESTIMATE algorithm was used to calculate the ImmuneScore, StromalScore, and ESTIMATEScore. Patients were divided into low and high groups according to the quartiles of ImmuneScore, StromalScore, and the median of ESTIMATEScore to filter differential expression genes (DEGs), respectively. The DEGs were then evaluated using univariate and multivariate Cox regression to identify TME-related genes and its association with survival rate for the construction of a TMErisk model with three biomarkers. Then Gene Expression Profiling Interactive Analysis (GEPIA) and The Cancer Genome Atlas (TCGA) data were used to compare the gene expression in cancer and normal tissues. xCell analysis calculated the proportion of tumor-infiltrating immune cells in low and high expression of ATPase Secretory Pathway Ca2+ Transporting 2 (ATP2C2). In addition, samples from 20 TNBC patients admitted to our institution were used for immunohistochemical (IHC) examination. Three immune-related DEGs were identified, including prolyl 3-hydroxylase 2 (P3H2), sodium voltage-gated channel beta subunit 3 (SCN3B), and ATP2C2 and a TMErisk model was constructed and validated. However, only ATP2C2 was selected for further analysis. ATP2C2 mRNA level of TNBC patients was higher than that of normal breast tissue. Survival analysis showed that patients with high expression of ATP2C2 had a bad prognosis. xCell analysis demonstrated that the expression of ATP2C2 was associated with 16 kinds of tumor-infiltrating immune cells. Protein expression of ATP2C2 in TNBC tissues was higher compared to paired normal tissues in IHC. This study constructed and validated a TMErisk model that can effectively predict 3- and 5-year survival rate for TNBC patients. TNBC patients with lower expression of ATP2C2 had a good prognosis.

SAA1 Has Potential as a Prognostic Biomarker Correlated with Cell Proliferation, Migration, and an Indicator for Immune Infiltration of Tumor Microenvironment in Clear Cell Renal Cell Carcinoma.

The tumor microenvironment (TME) plays an important part in the initiation and development of clear cell renal cell carcinoma (ccRCC). However, an understanding of the immune infiltration in TME is still unknown. Our study aims to explore the correlation between the TME and the clinical features, as well as the prognosis of ccRCC. In the present study, ESTIMATE and CIBERSORT computational methods were applied to calculate the proportion of tumor-infiltrating immune cells (TICs) and the amount of immune and stromal fractions in the ccRCC form The Cancer Genome Atlas (TCGA) database. Then, we sought to find out those immune cell types and genes which may play a significant role and validated them in the GEO database. Furthermore, an immunohistochemical analysis of our external validation dataset was used to detect SAA1 and PDL1 expression in the ccRCC cancer tissues and corresponding normal tissues. Statistical analysis was performed to study the relationship between SAA1 and clinical characteristics, as well as PDL1 expression. Furthermore, a ccRCC cell model with SAA1 knockdown was constructed, which was used for cell proliferation and the migration test. The intersection analysis of the univariate COX and PPI analysis were performed to imply Serum Amyloid A1 (SAA1) as a predictive factor. The expression of SAA1 was significantly negatively correlated to OS and positively correlated to the clinical TMN stage system. The genes in the high-expression SAA1 group were basically enriched in immune-related activities. The proportion of mast cells resting was negatively correlated with SAA1 expression, indicating that SAA1 may be involved in the maintenance of the immune status for the TME. Moreover, the PDL1 expression was positively related to the SAA1 expression and negatively correlated with the patients' prognosis. Further experiments revealed that the knockdown of SAA1 inhibited ccRCC development through suppressing cell proliferation and migration. SAA1 may be a novel marker for the prognosis prediction of ccRCC patients and may play a vital role in the TME by mast cell resting and PDL1 expression. SAA1 has the potential to become a therapeutic target and indicator for immune target therapy in ccRCC treatment.

Immune microenvironment and clinical feature analyses based on a prognostic model in lymph node-positive breast cancer.

If lymph node metastasis occurs in breast cancer patients, the disease can progress rapidly. Based on the infiltrative immune cells of breast cancer patients with lymph node positivity, we constructed the LNPRS for selecting prognostic predictors. The LNPRS was established and the predictive value of the LNPRS was verified by independent testing cohorts. A nomogram was also established to confirm the therapeutic guidance significance of the LNPRS. The correlation of the LNPRS with tumor mutation burden, immune microenvironment score, immune checkpoints, the proportion of tumor-infiltrating immune cells, and GSEA and GSVA enrichment pathways were also evaluated. In the training cohort, the overall survival of breast cancer patients who had high LNPRS was shorter than that of patients who had low LNPRS (7.98 years versus 20.42 years, P-value< 8.16E-11). The AUC values for 5-, 10-, and 15-years were 0.787, 0.739, and 0.800, respectively. The ability to predict prognosis for the LNPRS was also tested in 3 independent testing cohorts. Furthermore, the predictive value of the LNPRS for chemotherapy and immunotherapy was also proven. The GSEA and GSVA showed that the LNPRS was closely related to the activation of T and B lymphocytes and IFN-γ secretion. Moreover, breast cancer patients with low LNPRS had higher TME scores than those with high LNPRS. We can conclude that the LNPRS is a robust prognostic biomarker in breast cancer patients with positive lymph nodes and may be helpful for patients to make a clinical decision.

GRAP2: A Novel Immune-Related Prognosis Biomarker in Cervical Cancer

Background: Immune infiltration of the tumor microenvironment offers unlimited possibilities for therapeutic strategies in cervical cancer, where GRAP2 is an adaptor protein engaged in diverse signal activations. However, uncertainty exists regarding GRAP2’s prognostic significance and its relationship to immune infiltration. Methods: The data on cervical cancer cases were downloaded from The Cancer Genome Atlas (TCGA) database. The ESTIMATE computational technique was utilized to calculate the amount of immunological and stromal components, which helped us to identify the differential expression genes (DEGs). Among them, GRAP2 was considered to be related to overall survival based on a protein-protein interaction network and a univariate Cox regression analysis. Thus, based on the Gene Expression Omnibus (GEO) and TCGA databases, we evaluated GRAP2’s influence on clinical prognosis. Furthermore, GRAP2 expression was analyzed by Gene Set Enrichment Analysis (GSEA). Finally, we used CIBERSORTx analysis to assess the proportion of tumor-infiltrating immune cells (TICs) and the connection between GRAP2 and the tumor immune microenvironment. Results: ESTIMATEScore was associated with cervical cancer patient’s prognosis. There are 791 DEGs and 11 potential key genes were identified including GRAP2. In survival analyses with clinical information, We found that the GRAP2 high expression group exhibited a significantly longer overall survival (OS) than the low expression group and that the gene expression gradually declined as the Federation of International of Gynecologists and Obstetricians (FIGO) stage and M classification increased. GRAP2 was strongly linked with immunity and metabolism, according to GSEA. Finally, we discovered that 11 different TIC types and GRAP2 expressions were linked. Conclusions: GRAP2 may be a novel immune-related prognosis biomarker in cervical cancer.

CTHRC1 is associated with immune cell infiltration and functions as an adverse marker for prognosis in head and neck squamous cell carcinoma.

Collagen triple helix repeat containing 1 (CTHRC1) is a secreted glycoprotein that decreases the deposition of collagen matrix and accelerates tumor metastasis. However, the relationship between CTHRC1 and the outcomes of head and neck squamous cell carcinoma (HNSCC) and tumor-infiltrating lymphocytes remains unclear. In the present study, the transcriptional level of CTHRC1 and its association with overall survival (OS) and relapse-free survival (RFS) time in diverse cancer types were evaluated using The Cancer Genome Atlas, Tumor Immune Estimation Resource (TIMER), ONCOMINE and Kaplan-Meier plotter databases. The association of CTHRC1 expression level with the clinicopathological parameters of patients with HNSCC from The University of ALabama at Birmingham CANcer data analysis Portal (UALCAN) database were also evaluated. Enrichment analysis of CTHRC1 was carried out using gene set enrichment analysis software. CIBERSORT and TIMER databases were used to evaluate the relationship between the expression level of CTHRC1 and the proportion of tumor-infiltrating immune cells (TICs) in multiple cancer types. Moreover, immunohistochemistry was used to verify the expression of CTHRC1 in clinical samples of HNSCC. CTHRC1 was upregulated in HNSCC and high expression of CTHRC1 was associated with worsening clinicopathologic parameters and shorter OS and RFS times. There were eight HALLMARK gene sets, 1,231 immune signature gene sets and 14 KEGG gene sets significantly enriched in the high CTHRC1 expression group, while no gene set was enriched in the low CTHRC1 expression group. The expression of CTHRC1 was closely correlated with the proportion of TICs, where the expression of CTHRC1 was significantly positively correlated with the amount of infiltrated M0 and M2 macrophages, and significantly negatively associated with the levels of M1 macrophages. These findings suggest that CTHRC1 is an adverse prognostic marker and is associated with immune cell infiltration in HNSCC.

CXCL10 is a prognostic marker for pancreatic adenocarcinoma and tumor microenvironment remodeling

Background:The tumor microenvironment (TME) plays a crucial role in the progression of pancreatic adenocarcinoma (PAAD). However, challenges remain regarding the role played by TME associated genes in the prognosis of PAAD.Methods:The scores of tumor infiltrating immune cells (TICs), the immune and stroma scores of 182 PAAD patients in the Cancer Genome Atlas (TCGA) database were determined using CIBERSORT and ESTIMATE calculations. The final genes were identified by protein-protein interaction (PPI) networks and univariate Cox regression of differentially expressed genes. Finally, the correlation between gene expression and TCGA and clinical characteristics of patients in local hospital database was discussed. Gene set enrichment analysis (GSEA), the association between CXCL10 expression and TICs components were conducted.Results:In TCGA database and local hospital data, CXCL10 expression was correlated with the survival rate and TNM classification of patients with PAAD. Immune-related activities were enriched in the CXCL10 high expression group, while metabolic pathways were enriched in the CXCL10 low expression group. The expression of CXCL10 correlated with the proportion of TICs. CXCL10 expression was correlated with the proportion of TICs.Conclusion:CXCL10 is a potential prognostic marker for PAAD and provide additional insights into the treatment of PAAD based on TME transformation. However, more independent experimentation with the CXCL10 is need.

Bioinformatics analysis identifies PSMB8 as a key gene in the cutaneous malignant melanoma tumor microenvironment.

Cutaneous tumors are commonly seen in clinical practice, and malignant melanoma (MM) is the leading cause of cutaneous tumor-induced death. The tumor microenvironment (TME), a critical part of tumorigenesis, has been a research hotspot in recent years. However, the effects of the MM microenvironment components remain elusive. This study aimed to analyze the various components in the TME of MM to identify factors affecting the tumorigenesis, progression, and metastasis of MM and the survival of MM patients. We also aimed to identify biomarkers related to TME rehabilitation to provide a new direction for MM treatment. We used bioinformatics to analyze the RNA-seq and somatic mutation data of 473 MM patients from The Cancer Genome Atlas database. Firstly, the patients' immunity and stroma were separately scored by the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data (ESTIMATE) method. According to the median score, the participants were split into high- and low-score groups. Then, Gene Set Enrichment Analysis (GSEA) was performed, showing that high-expression genes were highly abundant in biological and metabolic activities associated with the immune system. Differentially expressed genes (DEGs) and differentially mutated genes (DMGs) were identified and intersected to obtain the key immune-related genes PSMB8, FAM216B, DYSF, and FAM131C. PSMB8 was finally selected as the preferred immune-related prognostic marker; it was positively associated with overall survival and therefore considered a protective gene for MM patients. The GSEA analysis showed that PSMB8 with high expression had greater gene abundance in biological and metabolic processes related to immune system. In addition, CIBERSORT analysis showed an association between the proportion of tumor-infiltrating immune cells and PSMB8 expression. Our results suggest that PSMB8 might be associated with tumorigenesis and MM progression and could serve as a biomarker for the TME rehabilitation of MM. Our findings provide a new perspective and direction for the treatment of MM.

Inhibition of MYC suppresses programmed cell death ligand-1 expression and enhances immunotherapy in triple-negative breast cancer.

Cancer immunotherapy has emerged as a promising strategy against triple-negative breast cancer (TNBC). One of the immunosuppressive pathways involves programmed cell death-1 (PD-1) and programmed cell death ligand-1 (PD-L1), but many patients derived little benefit from PD-1/PD-L1 checkpoint blockades treatment. Prior research has shown that MYC, a master transcription amplifier highly expressed in TNBC cells, can regulate the tumor immune microenvironment and constrain the efficacy of immunotherapy. This study aims to investigate the regulatory relationship between MYC and PD-L1, and whether a cyclin-dependent kinase (CDK) inhibitor that inhibits MYC expression in combination with anti-PD-L1 antibodies can enhance the response to immunotherapy. Public databases and TNBC tissue microarrays were used to study the correlation between MYC and PD-L1. The expression of MYC and PD-L1 in TNBCs was examined by quantitative real-time polymerase chain reaction and Western blotting. A patient-derived tumor xenograft (PDTX) model was used to evaluate the influence of a CDK7 inhibitor THZ1 on PD-L1 expression. Cell proliferation and migration were detected by 5-ethynyl-2'-deoxyuridine (EdU) cell proliferation and cell migration assays. Tumor xenograft models were established for in vivo verification. A high MYC expression level was associated with a poor prognosis and could alter the proportion of tumor-infiltrating immune cells (TIICs). The positive correlation between MYC and PD-L1 was confirmed by immunostaining samples from 165 TNBC patients. Suppression of MYC in TNBC caused a reduction in the levels of both PD-L1 messenger RNA and protein. In addition, antitumor immune response was enhanced in the TNBC cancer xenograft mouse model with suppression of MYC by CDK7 inhibitor THZ1. The combined therapy of CDK7 inhibitor THZ1 and anti-PD-L1 antibody appeared to have a synergistic effect, which might offer new insight for enhancing immunotherapy in TNBC.

CD40LG is a novel immune- and stroma-related prognostic biomarker in the tumor microenvironment of invasive breast cancer

To identify tumor microenvironment (TME)- related genes associated with the occurrence of invasive breast cancer as potential prognostic biomarkers and therapeutic targets. RNA transcriptome data and clinically relevant data were retrieved from TCGA database, and the StromalScore and ImmuneScore were calculated using the ESTIMATE algorithm. The differentially expressed genes (DEGs) were screened by taking the intersection. A protein- protein interaction network was established, and univariate COX regression analysis was used to identify the core genes among the DEGs. A core gene was selected for GSEA and CIBERSORT analysis to determine the function of the core gene and the proportion of tumor-infiltrating immune cells, respectively. Western blotting and qRT-PCR were performed to verify the expression level of CD40LG in breast cancer cell lines and clinical specimens. A total of 1222 samples (124 normal and 1098 tumor samples) were extracted from TCGA for analysis, from which 487 DEGs were identified. These genes were mainly enriched in immune-related pathways, and crossover analysis identified 11 key genes (CD40LG, ITK, CD5, CD3E, SPN, IL7R, CD48, CCL19, CD2, CD52, and CD2711) associated with breast cancer TME status. CD40LG was selected as the core gene, whose high expression was found to be associated with a longer overall survival of breast cancer patients (P=0.002), and its expression level differed significantly with TNM stage and tumor size (P < 0.05). GSEA and CIBERSORT analyses indicated that CD40LG expression level was associated with immune activity in the TME. Western blotting and qRT-PCR showed that the protein and mRNA expression of CD40LG were significantly lower in breast cancer cells and cancer tissues than in normal breast cells and adjacent tissues. The high expression of CD40LG in TME is positively correlated with the survival of patients with invasive breast cancer, suggesting its value as a potential new biomarker for predicting prognosis of the patients.

NAV3 Is a Novel Prognostic Biomarker Affecting the Immune Status of the Tumor Microenvironment in Colorectal Cancer.

Colorectal cancer (CRC) is one of the most common malignant tumors in the world. Tumor microenvironment (TME) plays a crucial role in the development of CRC. With the deep understanding of TME function, growing studies have demonstrated that alteration in tumor-infiltrating immune cells (TICs) and gene expressions are associated with clinical outcomes of various tumors. In this study, we aimed to recognize critical prognostic genes involved in immune states in TME of CRC. Hence, the proportion of TICs and the number of immune and stromal components in CRC samples from TCGA datasets were calculated by the use of CIBERSORT and ESTIMATE calculation methods. Different assays were applied to collect differential expression genes (DEGs) shared by the ImmuneScore and StromalScore. DEGs were further analyzed by the use of univariate Cox regression. Our attention focused on neuron navigator 3 (NAV3) which was highly expressed in CRC specimens and associated with an advanced clinical stage and poor prognosis of CRC patients. KEGG assays revealed that NAV3 may be involved in Alzheimer disease, amyotrophic lateral sclerosis, Huntington disease, FoxO signaling pathway, and human papillomavirus infection. Correlation assays showed that macrophage M0 and B cells memory, NK cells activated, dendritic cells resting, T cells CD4 memory activated, and T cells CD8 were correlated with NAV3 expression, indicating that NAV3 may represent the immune status of TME. Finally, RT-PCR confirmed that NAV3 expression was distinctly increased in CRC cells, and its knockdown suppressed the proliferation of CRC cells. Overall, NAV3 could be used as a novel predictor for TME of CRC and might be a novel prognostic biomarker. In the future, drugs targeting NAV3 might be developed as a potential immunotherapy for CRC patients.

A Multiomics Profiling Based on Online Database Revealed Prognostic Biomarkers of BLCA.

Background Bladder cancer (BLCA) is one of the most common urological malignancies globally, posing a severe threat to public health. In combination with protein-protein interaction (PPI) network analysis of proteomics, Gene Set Variation Analysis (GSVA) and “CancerSubtypes” package of R software for transcriptomics can help identify biomarkers related to BLCA prognosis. This will have significant implications for prevention and treatment. Method BLCA data were downloaded from The Cancer Genome Atlas (TCGA) database and GEO database (GSE13507). GSVA analysis converted the gene expression matrix to the gene set expression matrix. “CancerSubtypes” classified patients into three subtypes and established a prognostic model based on differentially expressed gene sets (DEGSs) among the three subtypes. For genes from prognosis-related DEGSs, functional and pathway enrichment analyses and PPI network analysis were carried out. The Human Protein Atlas (HPA) database was used for validation. Finally, the proportion of tumor-infiltrating immune cells (TIICs) was determined using the CIBERSORT algorithm. Results In total, 414 tumor samples and 19 adjacent-tumor samples were obtained from TCGA, with 145 samples belonging to subtype A, 126 samples belonging to subtype B, and 136 samples belonging to subtype C. Then, we identified 83 DEGSs and constituted a prognostic signature with two of them: “GSE1460_CD4_THYMOCYTE_VS_THYMIC_STROMAL_CELL_DN” and “MODULE_253.” Finally, five subnets of two PPI networks were established, and nine core proteins were obtained: CDH2, COL1A1, EIF2S2, PSMA3, NAA10, DNM1L, TUBA4A, KIF11, and KIF23. The HPA database confirmed the expression of the nine core proteins in BLCA tissues. Furthermore, EIF2S2, PSMA3, DNM1L, and TUBA4A could be novel BLCA prognostic biomarkers. Conclusions In this study, we discovered two gene sets linked to BLCA prognosis. PPI analysis confirmed the network's core proteins, and several newly discovered biomarkers of BLCA prognosis were identified.

Four differentially expressed genes can predict prognosis and microenvironment immune infiltration in lung cancer: a study based on data from the GEO

BackgroundLung cancer is among the major diseases threatening human health. Although the immune response plays an important role in tumor development, its exact mechanisms are unclear.Materials and methodsHere, we used CIBERSORT and ESTIMATE algorithms to determine the proportion of tumor-infiltrating immune cells (TICs) as well as the number of immune and mesenchymal components from the data of 474 lung cancer patients from the Gene Expression Omnibus database. And we used data from The Cancer Genome Atlas database (TCGA) for validation.ResultsWe observed that immune, stromal, and assessment scores were only somewhat related to survival with no statistically significant differences. Further investigations revealed these scores to be associated with different pathology types. GO and KEGG analyses of differentially expressed genes revealed that they were strongly associated with immunity in lung cancer. In order to determine whether the signaling pathways identified by GO and KEGG signaling pathway enrichment analyses were up- or down-regulated, we performed a gene set enrichment analysis using the entire matrix of differentially expressed genes. We found that signaling pathways involved in hallmark allograft rejection, hallmark apical junction, hallmark interferon gamma response, the hallmark P53 pathway, and the hallmark TNF-α signaling via NF-ĸB were up-regulated in the high-ESTIMATE-score group. CIBERSORT analysis for the proportion of TICs revealed that different immune cells were positively correlated with the ESTIMATE score. Cox regression analysis of the differentially expressed genes revealed that CPA3, C15orf48, FCGR1B, and GNG4 were associated with patient prognosis. A prognostic model was constructed wherein patients with high-risk scores had a worse prognosis (p < 0.001 using the log-rank test). The Area Under Curve (AUC)value for the risk model in predicting the survival was 0.666. The validation set C index was 0.631 (95% CI: 0.580–0.652). The AUC for the risk formula in the validation set was 0.560 that confirmed predictivity of the signature.ConclusionWe found that immune-related gene expression models could predict patient prognosis. Moreover, high- and low-ESTIMATE-score groups had different types of immune cell infiltration.