The expression pattern of matrix-producing tumor stroma is of prognostic importance in breast cancer

PWWP2A/B: Prominent players in the proteomic landscape

The PWWP domain is a conserved motif unique to eukaryotes, playing a critical role in various cellular processes. Proteins containing the PWWP domain are typically found in chromatin, where they bind to DNA and histones in nucleosomes, facilitating chromatin-associated functions. Among these proteins, PWWP-domain containing proteins 2A and 2B (PWWP2A and PWWP2B), identified during the H2A interactome analysis, are DNA methyltransferase-related proteins, that are structurally disordered, except for their PWWP domain. While their precise functions remain to be fully elucidated, PWWP2A and PWWP2B have been implicated in essential processes such as embryonic development, mitotic regulation, adipose thermogenesis, transcriptional control, and DNA damage response. Their involvement in disease pathology is an emerging area of research, with PWWP2B downregulation linked to recurrent gastric cancer, promoting cell proliferation and migration. Literature reveals that the circular RNA, cPWWP2A sequesters miR-203, miR-223, and miR-27, to modulate TGF-β signalling by inhibiting key regulators like SMAD3 and SP3. Additionally, PWWP2A/B proteins may interact with P4HA3, a regulator of the TGF-β/SMAD signalling pathway that influences tumour invasiveness, though the precise nature of this interaction is not yet fully understood. The PWWP2-miRNA-TGF-β axis, particularly the PWWP2-P4HA3 association, provides valuable insights into therapeutic strategies, especially under adverse conditions where this pathway is differentially regulated. Overall, given their essential roles in fundamental cellular processes and their involvement in disease mechanisms, PWWP2A and PWWP2B proteins could be ideal targets for therapeutic intervention. Thus, these proteins occupy a prominent position in the human proteome and epigenetic landscape.

Spatial Localization of Collagen Hydroxylated Proline Site Variation as an Ancestral Trait in the Breast Cancer Microenvironment

Collagen stroma interactions within the extracellular microenvironment of breast tissue play a significant role in breast cancer, including risk, progression, and outcomes. Hydroxylation of proline (HYP) is a common post-translational modification directly linked to breast cancer survival and progression. Changes in HYP status lead to alterations in epithelial cell signaling, extracellular matrix remodeling, and immune cell recruitment. In the present study, we test the hypothesis that the breast cancer microenvironment presents unique PTMs of collagen, which form bioactive domains at these sites that are associated with spatial histopathological characteristics and influence breast epithelial cell signaling. Mass spectrometry imaging proteomics targeting collagens were paired with comprehensive proteomic methods to identify novel breast cancer-related collagen domains based on spatial localization and regulation in 260 breast tissue samples. As ancestry plays a significant role in breast cancer outcomes, these methods were performed on ancestry diverse breast cancer tissues. Lumpectomies from the Cancer Genome Atlas (TCGA; n=10) reported increased levels of prolyl 4-hydroxylase subunit alpha-3 (P4HA3) accompanied by spatial regulation of fibrillar collagen protein sequences. A concise set of triple negative breast cancer lumpectomies (n=10) showed spatial regulation of specific domain sites from collagen alpha-1(I) chain. Tissue microarrays identified proteomic alterations around post-translationally modified collagen sites in healthy breast (n=81) and patient matched normal adjacent (NAT; n=76) and invasive ductal carcinoma (n=83). A collagen alpha-1(I) chain domain encompassing amino acids 506-514 with site-specific proline hydroxylation reported significant alteration between patient matched normal adjacent tissue and invasive breast cancer. Functional testing of domain 506-514 on breast cancer epithelial cells showed proliferation, chemotaxis and cell signaling response dependent on site localization of proline hydroxylation within domain 506-514 variants. These findings support site localized collagen HYP forms novel bioactive domains that are spatially distributed within the breast cancer microenvironment and may play a role in ancestral traits of breast cancer.

Pan-cancer analysis and experimental validation of FPR3 as a prognostic and immune infiltration-related biomarker for glioma

Formyl peptide receptor 3 (FPR3) is known to have implications in the progression of various cancer types. Despite this, its biological significance within pan-cancer datasets has yet to be investigated. In this investigation, we scrutinized FPR3's expression profiles, genetic alterations, prognostic significance, immune-related characteristics, methylation status, tumor mutation burden (TMB), and microsatellite instability (MSI) across different types of cancer. We utilized TISCH's single-cell data to identify immune cells closely associated with FPR3. The predictive significance of FPR3 was evaluated independently in gliomas using data from TCGA and CGGA datasets, leading to the development of a prognostic nomogram. Immunohistochemistry and Western blot analysis confirmed FPR3 expression in gliomas. Lastly, the CCK-8 and wound-healing assays were employed to assess the impact of FPR3 on the proliferation and metastasis of GBM cell lines. In numerous cancer types, heightened FPR3 expression correlated with adverse outcomes, immune cell infiltration, immune checkpoints, TMB, and MSI. In glioma, FPR3 emerged as a notable risk factor, with the prognostic model effectively forecasting patient results. The potential biological relevance of FPR3 was confirmed in glioma, and it was shown to have significant involvement in the processes of glioma growth, immune infiltration, and metastasis. Our results imply a potential association of FPR3 with tumor immunity, indicating its viability as a prognostic indicator in glioma.

The Integrated Bioinformatic Approach Reveals the Prognostic Significance of LRP1 Expression in Ovarian Cancer

A hyperactive tumour microenvironment (TME) drives unrestricted cancer cell survival, drug resistance, and metastasis in ovarian carcinoma (OC). However, therapeutic targets within the TME for OC remain elusive, and efficient methods to quantify TME activity are still limited. Herein, we employed an integrated bioinformatics approach to determine which immune-related genes (IRGs) modulate the TME and further assess their potential theragnostic (therapeutic + diagnostic) significance in OC progression. Using a robust approach, we developed a predictive risk model to retrospectively examine the clinicopathological parameters of OC patients from The Cancer Genome Atlas (TCGA) database. The validity of the prognostic model was confirmed with data from the International Cancer Genome Consortium (ICGC) cohort. Our approach identified nine IRGs, AKT2, FGF7, FOS, IL27RA, LRP1, OBP2A, PAEP, PDGFRA, and PI3, that form a prognostic model in OC progression, distinguishing patients with significantly better clinical outcomes in the low-risk group. We validated this model as an independent prognostic indicator and demonstrated enhanced prognostic significance when used alongside clinical nomograms for accurate prediction. Elevated LRP1 expression, which indicates poor prognosis in bladder cancer (BLCA), OC, low-grade gliomas (LGG), and glioblastoma (GBM), was also associated with immune infiltration in several other cancers. Significant correlations with immune checkpoint genes (ICGs) highlight the potential importance of LRP1 as a biomarker and therapeutic target. Furthermore, gene set enrichment analysis highlighted LRP1's involvement in metabolism-related pathways, supporting its prognostic and therapeutic relevance also in BLCA, OC, low-grade gliomas (LGG), GBM, kidney cancer, OC, BLCA, kidney renal clear cell carcinoma (KIRC), stomach adenocarcinoma (STAD), and stomach and oesophageal carcinoma (STES). Our study has generated a novel signature of nine IRGs within the TME across cancers, that could serve as potential prognostic predictors and provide a valuable resource to improve the prognosis of OC.

Single-cell RNA sequencing to explore cancer-associated fibroblasts heterogeneity: "Single" vision for "heterogeneous" environment

Cancer-associated fibroblasts (CAFs), a phenotypically and functionally heterogeneous stromal cell, are one of the most important components of the tumour microenvironment. Previous studies have consolidated it as a promising target against cancer. However, variable therapeutic efficacy-both protumor and antitumor effects have been observed not least owing to the strong heterogeneity of CAFs. Over the past 10 years, advances in single-cell RNA sequencing (scRNA-seq) technologies had a dramatic effect on biomedical research, enabling the analysis of single cell transcriptomes with unprecedented resolution and throughput. Specifically, scRNA-seq facilitates our understanding of the complexity and heterogeneity of diverse CAF subtypes. In this review, we discuss the up-to-date knowledge about CAF heterogeneity with a focus on scRNA-seq perspective to investigate the emerging strategies for integrating multimodal single-cell platforms. Furthermore, we summarized the clinical application of scRNA-seq on CAF research. We believe that the comprehensive understanding of the heterogeneity of CAFs form different visions will generate innovative solutions to cancer therapy and achieve clinical applications.

Construction of iron metabolism-related prognostic features of gastric cancer based on RNA sequencing and TCGA database

BACKGROUND

Researches have manifested that the disorder of iron metabolism is participated in Gastric cancer (GC), but whether iron metabolism-relevant genes (IMRGs) is related to the survival outcome of GC remain unknown.

METHODS

Eleven tumor as well as nine adjacent normal tissues from GC patients were underwent mRNA sequencing, and the The Cancer Genome Atlas Stomach Cancer (TCGA-STAD) datasets were acquired from the TCGA database. Cox analyses and least absolute shrinkage and selection operator (LASSO) regression were applied to build a IMRGs signature. The relationship between signature genes and the infiltration profiling of 24 immune cells were investigated using single-sample GSEA (ssGSEA). Meanwhile, the potential biological significance, genes that act synergistically with signature genes, and the upstream regulatory targets were predicted. Finally, the abundance of the signature genes were measured via the quantitative real-time PCR (qRT-PCR).

RESULTS

A IMRGs signature was constructed according to the expression and corresponding coefficient of DOHH, P4HA3 and MMP1 (The Schoenfeld individual test showed risk score was not significant with P values = 0.83). The prognostic outcome of patients in the high-risk group was terrible (p < 0.05). Receiver operating characteristic (ROC) curves confirmed that the IMRGs signature presented good efficiency for predicting GC prognosis (AUC > 0.6). The nomogram was performed well for clinical utilize (C-index = 0.60), and the MMP1 expression significantly increased in the cohorts at age > 60 and Stage II-IV (p < 0.05). The positive correlation of P4HA3 and MMP1 expression as well as the negative correlation of DOHH expression with risk score (p < 0.0001) and worse prognosis (p < 0.05) were detected as well. Furthermore, 11 differential immune cells were associated with these signature genes (most p < 0.01). Finally, qRT-PCR revealed that the abundance of DOHH, P4HA3 and MMP1 were high in tumor cases, indicating the complex mechanism between the high expression of DOHH as a protective factor and the high expression of P4HA3 and MMP1 as the risk factors in the development of GC.

CONCLUSION

An iron metabolism-related signature was constructed and has significant values for foretelling the OS of GC.

A prognostic mathematical model based on tumor microenvironment-related genes expression for breast cancer patients

Background

Tumor microenvironment (TME) status is closely related to breast cancer (BC) prognosis and systemic therapeutic effects. However, to date studies have not considered the interactions of immune and stromal cells at the gene expression level in BC as a whole. Herein, we constructed a predictive model, for adjuvant decision-making, by mining TME molecular expression information related to BC patient prognosis and drug treatment sensitivity.

Methods

Clinical information and gene expression profiles were extracted from The Cancer Genome Atlas (TCGA), with patients divided into high- and low-score groups according to immune/stromal scores. TME-related prognostic genes were identified using Kaplan-Meier analysis, functional enrichment analysis, and protein-protein interaction (PPI) networks, and validated in the Gene Expression Omnibus (GEO) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was used to construct and verify a prognostic model based on TME-related genes. In addition, the patients' response to chemotherapy and immunotherapy was assessed by survival outcome and immunohistochemistry (IPS). Immunohistochemistry (IHC) staining laid a solid foundation for exploring the value of novel therapeutic target genes.

Results

By dividing patients into low- and high-risk groups, a significant distinction in overall survival was found (p < 0.05). The risk model was independent of multiple clinicopathological parameters and accurately predicted prognosis in BC patients (p < 0.05). The nomogram-integrated risk score had high prediction accuracy and applicability, when compared with simple clinicopathological features. As predicted by the risk model, regardless of the chemotherapy regimen, the survival advantage of the low-risk group was evident in those patients receiving chemotherapy (p < 0.05). However, in patients receiving anthracycline (A) therapy, outcomes were not significantly different when compared with those receiving no-A therapy (p = 0.24), suggesting these patients may omit from A-containing adjuvant chemotherapy. Our risk model also effectively predicted tumor mutation burden (TMB) and immunotherapy efficacy in BC patients (p < 0.05).

Conclusion

The prognostic score model based on TME-related genes effectively predicted prognosis and chemotherapy effects in BC patients. The model provides a theoretical basis for novel driver-gene discover in BC and guides the decision-making for the adjuvant treatment of early breast cancer (eBC).

Characterization of the immune environment in pregnancy-associated breast cancer

Pregnancy-associated breast cancer (PrBC) is a rare and clinically challenging condition. Specific immune mechanisms and pathways are involved in maternal-fetal tolerance and tumor-host immunoediting. The comprehension of the molecular processes underpinning this immune synergy in PrBC is needed to improve patients' clinical management. Only a few studies focused on the immune biology of PrBC and attempted to identify bona fide biomarkers. Therefore, clinically actionable information remains extremely puzzling for these patients. In this review article, we discuss the current knowledge on the immune environment of PrBC, in comparison with pregnancy-unrelated breast cancer and in the context of maternal immune changes during pregnancy. A particular emphasis is given to the actual role of potential immune-related biomarkers for PrBC clinical management.

Up-regulation of PUM1 by miR-218-5p promotes colorectal tumor-initiating cell properties and tumorigenesis by regulating the PI3K/AKT axis

Background

Colorectal cancer (CRC) is the third most common cancer and the fourth most common cause of cancer-related death worldwide. Advanced stage CRC, during the recent past, had a dismal prognosis and only a few available treatments. Pumilio homologous protein 1 (PUM1) is reportedly aberrant in human malignancies, including CRC. However, the role of PUM1 in the regulation of tumor-initiating cells (T-ICs) remains unknown.

Methods

The levels of messenger RNAs (mRNAs) were determined by quantitative reverse transcription polymerase chain reaction (qRT-PCR) and immunoblot analyses. Statistical analyses were performed to determine the associations between the levels of PUM1 and tumor features and patient outcomes. Whether PUM1 is a downstream target of miR-218-5p was verified by bioinformatics target gene prediction and qRT-PCR.

Results

Herein, it was found that T-ICs, chemoresistance, and recurrent CRC samples all manifest increased PUM1 expression. Functional investigations have shown that PUM1 increased the self-renewal, tumorigenicity, malignant proliferation, and chemoresistance of colorectal cells. PUM1 activates the phosphatidylinositol-3-kinase (PI3K)/protein kinase B (AKT) signaling pathway biochemically. Furthermore, it was discovered that miR-218-5p specifically targets T-ICs' PUM1 3'-untranslated region (3'-UTR). More importantly, the PUM1/PI3K/AKT axis regulates CRC cells' responses to treatment with cetuximab, and PUM1 overexpression increased cetuximab resistance. More evidence points to the possibility that low PUM1 may predict cetuximab benefits in CRC patients after analysis of the patient cohort, patient-derived tumor organoids, and patient-derived xenografts (PDXs).

Conclusions

Taken together, the result of this work points to the critical function of the miR-218-5p/PUM1/PI3K/AKT regulatory circuit in regulating T-ICs characteristics and thus suggests possible therapeutic targets for CRC.

Functional characterization of TSPAN7 as a novel indicator for immunotherapy in glioma

Glioma is the most common primary malignant tumor of the central nervous system in clinical practice. Most adult diffuse gliomas have poor efficacy after standard treatment, especially glioblastoma. With the in-depth understanding of brain immune microenvironment, immunotherapy as a new treatment has attracted much attention. In this study, through analyzing a large number of glioma cohorts, we reported that TSPAN7, a member of the tetraspanin family, decreased in high-grade gliomas and low expression was associated with poor prognosis in glioma patients. Meanwhile, the expression pattern of TSPAN7 was verified in glioma clinical samples and glioma cell lines by qPCR, Western Blotting and immunofluorescence. In addition, functional enrichment analysis showed that cell proliferation, EMT, angiogenesis, DNA repair and MAPK signaling pathways were activated in the TSPAN7 lower expression subgroup. Lentiviral plasmids were used to overexpress TSPAN7 in U87 and LN229 glioma cell lines to explore the anti-tumor role of TSPAN7 in glioma. Moreover, by analyzing the relationship between TSPAN7 expression and immune cell infiltration in multiple datasets, we found that TSPAN7 was significantly negatively correlated with the immune infiltration of tumor-related macrophages, especially M2-type macrophages. Further analysis of immune checkpoints showed that, the expression level of TSPAN7 was negatively correlated with the expression of PD-1, PD-L1 and CTLA-4. Using an independent anti-PD-1 immunotherapy cohorts of GBM, we demonstrated that TSPAN7 expression may had a synergistic effect with PD-L1 on the response to immunotherapy. Based on the above findings, we speculate that TSPAN7 can serve as a biomarker for prognosis and a potential immunotherapy target in glioma patients.

Role of Single Nucleotide Polymorphism-Related Genes in Tumour Immune Cell Infiltration and Prognosis of Cutaneous Melanoma

Background

Advances in cancer research have allowed for early diagnosis and improved treatment of cutaneous melanoma (CM). However, its invasiveness and recurrent metastasis, along with rising resistance to newer therapies, have lent urgency to the search for novel biomarkers and the underlying molecular mechanisms of CM.

Methods

Single nucleotide polymorphism- (SNP-) related genes were obtained from the sequencing data of 428 CM samples in The Cancer Genome Atlas. Functional enrichment of these genes was analysed in clusterProfiler. Additionally, a protein-protein interaction (PPI) network was constructed with the Search Tool for the Retrieval of Interacting Gene (STRING) database. Gene Expression Profiling Interactive Analysis (GEPIA) was used to identify the expression and prognostic value of mutated genes. Finally, the Tumour Immune Estimation Resource (TIMER) analysed the relationship between gene expression and immune cell infiltration.

Results

We constructed a PPI network from the top 60 SNP-related genes. Mutated genes were mainly involved in calcium and oxytocin signalling pathways, as well as circadian entrainment. In addition, three SNP-related genes, BRAF, FLG, and SORL1, were significantly associated with patient prognosis. BRAF and SORL1 were positively associated with infiltration abundance of B cells, CD8+ T cells, CD4+ T cells, neutrophils, and dendritic cells, whereas FLG expression was negatively associated. Furthermore, higher immune cell infiltration was positively correlated with good prognosis.

Conclusions

Our study provides vital bioinformatic data and a relevant theoretical basis to further explore the molecular pathogenesis of CM and improve patient prognosis.

Integrated machine learning methods identify FNDC3B as a potential prognostic biomarker and correlated with immune infiltrates in glioma

Background

Recent discoveries have revealed that fibronectin type III domain containing 3B (FNDC3B) acts as an oncogene in various cancers; however, its role in glioma remains unclear.

Methods

In this study, we comprehensively investigated the expression, prognostic value, and immune significance of FNDC3B in glioma using several databases and a variety of machine learning algorithms. RNA expression data and clinical information of 529 patients from the Cancer Genome Atlas (TCGA) and 1319 patients from Chinese Glioma Genome Atlas (CGGA) databases were downloaded for further investigation. To evaluate whether FNDC3B expression can predict clinical prognosis of glioma, we constructed a clinical nomogram to estimate long-term survival probabilities. The predicted nomogram was validated by CGGA cohorts. Differentially expressed genes (DEGs) were detected by the Wilcoxon test based on the TCGA-LGG dataset and the weighted gene co-expression network analysis (WGCNA) was implemented to identify the significant module associated with the expression level of FNDC3B. Furthermore, we investigated the correlation between FNDC3B with cancer immune infiltrates using TISIDB, ESTIMATE, and CIBERSORTx.

Results

Higher FNDC3B expression displayed a remarkably worse overall survival and the expression level of FNDC3B was an independent prognostic indicator for patients with glioma. Based on TCGA LGG dataset, a co-expression network was established and the hub genes were identified. FNDC3B expression was positively correlated to the tumor-infiltrating lymphocytes and immune infiltration score, and high FNDC3B expression was accompanied by the increased expression of B7-H3, PD-L1, TIM-3, PD-1, and CTLA-4. Moreover, expression of FNDC3B was significantly associated with infiltrating levels of several types of immune cells and most of their gene markers in glioma.

Conclusion

This study demonstrated that FNDC3B may be involved in the occurrence and development of glioma and can be regarded as a promising prognostic and immunotherapeutic biomarker for the treatment of glioma.

High expression of P4HA3 in obesity: a potential therapeutic target for type 2 diabetes

The aims of the present study were to evaluate the expression of prolyl 4-hydroxylase subunit alpha 3 (P4HA3) in adipocytes and adipose tissue and to explore its effect on obesity and type 2 diabetes mellitus (T2DM). We initially demonstrated that P4HA3 was significantly upregulated in the subcutaneous adipose tissue of obesity and T2DM patients, and its functional roles in adipocyte differentiation and insulin resistance were investigated using in vitro and in vivo models. The knockdown of P4HA3 inhibited adipocyte differentiation and improved insulin resistance in 3T3-L1 cells. In C57BL/6J db/db mice fed with a high fat diet (HFD), silencing P4HA3 significantly decreased fasting blood glucose and triglycerides (TG) levels, with concomitant decrease of body weight and adipose tissue weight. Further analysis showed that P4HA3 knockdown was correlated with the augmented IRS-1/PI3K/Akt/FoxO1 signaling pathway in the adipose and hepatic tissues of obese mice, which could improve hepatic glucose homeostasis and steatosis of mice. Together, our study suggested that the dysregulation of P4HA3 may contribute to the development of obesity and T2DM.

Computational portraits of the tumoral microenvironment in human breast cancer

Breast cancer is the most diagnosed cancer in humans. In recent years, myxoid and proportionated stroma have been described as clinically significant in many cancer subtypes. Here computational portraits of tumor-associated stromata were created from a machine learning (ML) classifier using QuPath to evaluate proportionated stromal area (PSA), myxoid stromal ratio (MSR), and immune stroma proportion (ISP) from whole slide images (WSI). The ML classifier was validated in independent training (n = 40) and validation (n = 109) cohorts finding MSR, PSA, and ISP to be associated with tumor stage, lymph node status, Nottingham grade, stromal differentiation (SD), tumor size, estrogen receptor (ER), progesterone receptor (PR), and receptor tyrosine-protein kinase erbB-2 (HER-2). Overall, MSR correlated better with the clinicopathologic profile than PSA and ISP. High MSR was found to be associated with high tumor stage, low ISP, and high Nottingham histologic score. As a computational biomarker, high MSR was more likely to be associated with luminal B like, Her-2 enriched, and triple-negative biomarker status when compared to luminal A like. The supervised ML superpixel approach demonstrated here can be performed by a trained pathologist to provide a faster and more uniformed approach to the analysis to the tumoral microenvironment (TME). The TME may be relevant for clinical decision-making, determining chemotherapeutic efficacy, and guiding a more overall precision-based breast cancer care.

Predictive value of collagen in cancer

Cancer is a complex disease and a significant cause of mortality worldwide. Over the course of nearly all cancer types, collagen within the tumor microenvironment influences emergence, progression, and metastasis. This review discusses collagen regulation within the tumor microenvironment, pathological involvement of collagen, and predictive values of collagen and related extracellular matrix components in main cancer types. A survey of predictive tests leveraging collagen assays using clinical cohorts is presented. A conclusion is that collagen has high predictive value in monitoring cancer processes and stratifying by outcomes. New approaches should be considered that continue to define molecular facets of collagen related to cancer.

HSPA6 is Correlated With the Malignant Progression and Immune Microenvironment of Gliomas

Gliomas are primary intracranial space lesions with a high mortality rate. Current treatments for glioma are very limited. Recently, immunotargeted therapy of the glioma microenvironment has been developed. Members of the 70 kDa heat shock protein (HSP70) family are involved in the development of many tumors and immunity. HSPA6 protein belongs to the HSP70 family; However, the biological function of this protein in gliomas has yet to be evaluated. In the present study, a range of analyses, involving protein networks, survival, clinical correlation, and function, revealed that the expression of HSPA6 was negatively correlated with clinical prognosis and closely associated with immunity, invasion, and angiogenesis. Quantitative protein analysis confirmed that HSPA6 was expressed at high levels in patients with glioblastoma. Vitro experiments further verified that HSPA6 enhanced the malignant progression of glioma cells by promoting proliferation, invasion and anti-apoptosis. We also found that HSPA6 was closely correlated with genomic variations and tumor microenvironment. Collectively, we demonstrated that HSPA6 may represent a new therapeutic target to improve the prognosis of patients with gliomas.

PUM1 is upregulated by DNA methylation to suppress antitumor immunity and results in poor prognosis in pancreatic cancer

Background

Pancreatic carcinoma (PAAD) is a highly malignant cancer with a poor prognosis and high mortality rate. Pumilio homologous protein 1 (PUM1) promotes cell growth, invasion, and metastasis and suppresses apoptosis in many different kinds of cancers, such as non-small-cell lung carcinoma (NSCLC), ovarian cancer and lymphocyte leukemia. However, the underlying mechanism and potential role of PUM1 in PAAD have not been investigated.

Methods

Bioinformatics analysis was performed using multiple databases [The Cancer Genome Atlas (TCGA), Gene Expression Profiling Interactive Analysis (GEPIA), BBCancer, Human Protein Atlas (HPA), MethSurv, cBioPortal, The Cancer Imaging Archive (TCIA), xCell, Gene Expression Omnibus (GEO)] to explore the diagnostic and prognostic role of PUM1, and the relationship between expression of PUM1 and prognosis of patients with PAAD. The analysis was further validated using the Kaplan-Meier plotter.

Results

PUM1 plays a role in both diagnostic and prognostic prediction. The PUM1 mRNA expression level correlates with both the prognosis and incidence of pancreatic cancer. PUM1 can serve as a potential diagnostic indicator for pancreatic cancer. Furthermore, the DNA methylation levels of PUM1 affects its oncogene function in pancreatic cancer. PUM1 can also inhibit the immune microenvironment by altering immune cell infiltration, which affects immunotherapy response in pancreatic cancer.

Conclusions

PUM1 takes a crucial part in the immune microenvironment and immunotherapy response of PAAD and is potentially useful for the development of novel diagnostic and treatment strategies.

Gene Expression Profiling in Early Breast Cancer-Patient Stratification Based on Molecular and Tumor Microenvironment Features

Patients with early-stage hormone receptor-positive, human epidermal growth factor receptor 2-negative (HER2−) breast cancer (BC) are typically treated with surgery, followed by adjuvant systemic endocrine therapy with or without adjuvant chemotherapy and radiation therapy. Current guidelines regarding the use of adjuvant systemic therapy depend on clinical and pathological factors, such as the morphological assessment of tumor subtype; histological grade; tumor size; lymphovascular invasion; and lymph node status combined with estrogen receptor, progesterone receptor, and HER2 biomarker profiles assessed using immunohistochemistry and in situ hybridization. Additionally, the prognostic and predictive value of tumor-infiltrating lymphocytes and their composition is emerging as a key marker in triple negative (TNBC) and HER2-enriched molecular breast tumor subtypes. However, all these factors do not necessarily reflect the molecular heterogeneity and complexity of breast cancer. In the last two decades, gene expression signatures or profiling (GEP) tests have been developed to predict the risk of disease recurrence and estimate the potential benefit of receiving adjuvant systemic chemotherapy in patients with luminal breast cancer. GEPs have been utilized to help physicians to refine decision-making process, complementing clinicopathological parameters, and can now be used to classify the risk of recurrence and tailoring personalized treatments. Several clinical trials using GEPs validate the increasing value of such assays in different clinical settings, addressing relevant clinical endpoints. Finally, the recent approval of immune checkpoint inhibitors in TNBC and the increasing use of immunotherapy in different molecular BC populations highlight the opportunity to refine current GEPs by including a variety of immune-related genes that may help to improve predicting drug response and finetune prognosis.

Identification of key tumor stroma-associated transcriptional signatures correlated with survival prognosis and tumor progression in breast cancer

BACKGROUND

The aberrant expression of stromal gene signatures in breast cancer has been widely studied. However, the association of stromal gene signatures with tumor immunity, progression, and clinical outcomes remains lacking.

METHODS

Based on eight breast tumor stroma (BTS) transcriptomics datasets, we identified differentially expressed genes (DEGs) between BTS and normal breast stroma. Based on the DEGs, we identified dysregulated pathways and prognostic hub genes, hub oncogenes, hub protein kinases, and other key marker genes associated with breast cancer. Moreover, we compared the enrichment levels of stromal and immune signatures between breast cancer patients with bad and good clinical outcomes. We also investigated the association between tumor stroma-related genes and breast cancer progression.

RESULTS

The DEGs included 782 upregulated and 276 downregulated genes in BTS versus normal breast stroma. The pathways significantly associated with the DEGs included cytokine-cytokine receptor interaction, chemokine signaling, T cell receptor signaling, cell adhesion molecules, focal adhesion, and extracellular matrix-receptor interaction. Protein-protein interaction network analysis identified the stromal hub genes with prognostic value in breast cancer, including two oncogenes (COL1A1 and IL21R), two protein kinases encoding genes (PRKACA and CSK), and a growth factor encoding gene (PLAU). Moreover, we observed that the patients with bad clinical outcomes were less enriched in stromal and antitumor immune signatures (CD8 + T cells and tumor-infiltrating lymphocytes) but more enriched in tumor cells and immunosuppressive signatures (MDSCs and CD4 + regulatory T cells) compared with the patients with good clinical outcomes. The ratios of CD8 + /CD4 + regulatory T cells were lower in the patients with bad clinical outcomes. Furthermore, we identified the tumor stroma-related genes, including MCM4, SPECC1, IMPA2, and AGO2, which were gradually upregulated through grade I, II, and III breast cancers. In contrast, COL14A1, ESR1, SLIT2, IGF1, CH25H, PRR5L, ABCA6, CEP126, IGDCC4, LHFP, MFAP3, PCSK5, RAB37, RBMS3, SETBP1, and TSPAN11 were gradually downregulated through grade I, II, and III breast cancers. It suggests that the expression of these stromal genes has an association with the progression of breast cancers. These progression-associated genes also displayed an expression association with recurrence-free survival in breast cancer patients.

CONCLUSIONS

This study identified tumor stroma-associated biomarkers correlated with deregulated pathways, tumor immunity, tumor progression, and clinical outcomes in breast cancer. Our findings provide new insights into the pathogenesis of breast cancer.

The “Virtual Biopsy” of Cancerous Lesions in 3D: Non-Invasive Differentiation between Melanoma and Other Lesions Using Vibrational Optical Coherence Tomography

Early detection of skin cancer is of critical importance to provide five year survival rates that approach 99%. By 2050, one out of five Americans by age 70 will develop some form of skin cancer. This will result in a projected rate of 50 million skin biopsies per year given the current rate of escalation. In addition, the ability to differentiate between pigmented lesions and melanomas has proven a diagnostic challenge. While dermoscopy and visual analysis are useful in identifying many skin lesions, additional non-invasive techniques are needed to assist in the analysis of difficult to diagnose skin tumors. To augment dermoscopy data, we have developed 3D maps based on physical biomarker characteristics of benign and cancerous lesions using vibrational optical coherence tomography (VOCT). 3D images based on quantitative physical data involving changes in cellular and fibrous tissue stiffness along with changes in vascular quality are used to map and evaluate different types of cancers. 3D tumor maps constructed using quantitative VOCT data and OCT images have been used to characterize the differences between melanoma and other lesions. These characteristics can be used to plan the excision of difficult lesions where extensive surgery may be needed to remove the entire tumor in one step. In addition, it is now possible to use dermoscopy and VOCT to non-invasively differentiate between different cancerous lesion types using measurements of the resonant frequency of new cellular and vascular peaks. Quantitative VOCT information along with dermoscopic findings can be collected and analyzed remotely using artificial intelligence to improve cancerous tissue diagnosis.

Characterization of the Biomechanical Properties of Skin Using Vibrational Optical Coherence Tomography: Do Changes in the Biomechanical Properties of Skin Stroma Reflect Structural Changes in the Extracellular Matrix of Cancerous Lesions?

Early detection of skin cancer is of critical importance since the five-year survival rate for early detected skin malignancies is 99% but drops to 27% for cancer that has spread to distant lymph nodes and other organs. Over 2.5 million benign skin biopsies (55% of the total) are performed each year in the US at an alarming cost of USD ~2.5 B. Therefore there is an unmet need for novel non-invasive diagnostic approaches to better differentiate between cancerous and non-cancerous lesions, especially in cases when there is a legitimate doubt that a biopsy may be required. The purpose of this study is to determine whether the differences in the extracellular matrices among normal skin, actinic keratosis (AK), basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) can be assessed non-invasively using vibrational optical coherence tomography (VOCT). VOCT is a new diagnostic technology that uses infrared light and audible sound applied transversely to tissue to measure the resonant frequencies and elastic moduli of cells, dermal collagen, blood vessels and fibrous tissue in skin and lesion stroma without physically touching the skin. Our results indicate that the cellular, vascular and fibrotic resonant frequency peaks are altered in AK, BCC and SCC compared to those peaks observed in normal skin and can serve as physical biomarkers defining the differences between benign and cancerous skin lesions. The resonant frequency is increased from a value of 50 Hz in normal skin to a value of about 80 Hz in pre- and cancerous lesions. A new vascular peak is seen at 130 Hz in cancerous lesions that may reflect the formation of new tumor blood vessels. The peak at 260 Hz is similar to that seen in the skin of a subject with Scleroderma and skin wounds that have healed. The peak at 260 Hz appears to be associated with the deposition of large amounts of stiff fibrous collagen in the stroma surrounding cancerous lesions. Based on the results of this pilot study, VOCT can be used to non-invasively identify physical biomarkers that can help differentiate between benign and cancerous skin lesions. The appearance of new stiff cellular, fragile new vessels, and stiff fibrous material based on resonant frequency peaks and changes in the extracellular matrix can be used as a fingerprint of pre- and cancerous skin lesions.

Fibroblast pyruvate carboxylase is required for collagen production in the tumour microenvironment

The aberrant production of collagen by fibroblasts is a hallmark of many solid tumours and can influence cancer progression. How the mesenchymal cells in the tumour microenvironment maintain their production of extracellular matrix proteins as the vascular delivery of glutamine and glucose becomes compromised remains unclear. Here we show that pyruvate carboxylase (PC)-mediated anaplerosis in tumour-associated fibroblasts contributes to tumour fibrosis and growth. Using cultured mesenchymal and cancer cells, as well as mouse allograft models, we provide evidence that extracellular lactate can be utilized by fibroblasts to maintain tricarboxylic acid (TCA) cycle anaplerosis and non-essential amino acid biosynthesis through PC activity. Furthermore, we show that fibroblast PC is required for collagen production in the tumour microenvironment. These results establish TCA cycle anaplerosis as a determinant of extracellular matrix collagen production, and identify PC as a potential target to inhibit tumour desmoplasia.

Identification of prognostic immune-related gene signature associated with tumor microenvironment of colorectal cancer

Background

The tumor microenvironment (TME) has significantly correlation with tumor occurrence and prognosis. Our study aimed to identify the prognostic immune-related genes (IRGs)in the tumor microenvironment of colorectal cancer (CRC).

Methods

Transcriptome and clinical data of CRC cases were downloaded from TCGA and GEO databases. Stromal score, immune score, and tumor purity were calculated by the ESTIMATE algorithm. Based on the scores, we divided CRC patients from the TCGA database into low and high groups, and the differentially expressed genes (DEGs) were identified. Immune-related genes (IRGs) were selected by venn plots. To explore underlying pathways, protein-protein interaction (PPI) networks and functional enrichment analysis were used. After utilizing LASSO Cox regression analysis, we finally established a multi-IRGs signature for predicting the prognosis of CRC patients. A nomogram consists of the thirteen-IRGs signature and clinical parameters was developed to predict the overall survival (OS). We investigated the association between prognostic validated IRGs and immune infiltrates by TIMER database.

Results

Gene expression profiles and clinical information of 1635 CRC patients were collected from the TCGA and GEO databases. Higher stromal score, immune score and lower tumor purity were observed positive correlation with tumor stage and poor OS. Based on stromal score, immune score and tumor purity, 1517 DEGs, 1296 DEGs, and 1892 DEGs were identified respectively. The 948 IRGs were screened by venn plots. A thirteen-IRGs signature was constructed for predicting survival of CRC patients. Nomogram with a C-index of 0.769 (95%CI, 0.717–0.821) was developed to predict survival of CRC patients by integrating clinical parameters and thirteen-IRGs signature. The AUC for 1-, 3-, and 5-year OS were 0.789, 0.783 and 0.790, respectively. Results from TIMER database revealed that CD1B, GPX3 and IDO1 were significantly related with immune infiltrates.

Conclusions

In this study, we established a novel thirteen immune-related genes signature that may serve as a validated prognostic predictor for CRC patients, thus will be conducive to individualized treatment decisions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08629-3.

Machine learning reveals mesenchymal breast carcinoma cell adaptation in response to matrix stiffness

Epithelial-mesenchymal transition (EMT) and its reverse process, mesenchymal-epithelial transition (MET), are believed to play key roles in facilitating the metastatic cascade. Metastatic lesions often exhibit a similar epithelial-like state to that of the primary tumour, in particular, by forming carcinoma cell clusters via E-cadherin-mediated junctional complexes. However, the factors enabling mesenchymal-like micrometastatic cells to resume growth and reacquire an epithelial phenotype in the target organ microenvironment remain elusive. In this study, we developed a workflow using image-based cell profiling and machine learning to examine morphological, contextual and molecular states of individual breast carcinoma cells (MDA-MB-231). MDA-MB-231 heterogeneous response to the host organ microenvironment was modelled by substrates with controllable stiffness varying from 0.2kPa (soft tissues) to 64kPa (bone tissues). We identified 3 distinct morphological cell types (morphs) varying from compact round-shaped to flattened irregular-shaped cells with lamellipodia, predominantly populating 2-kPa and >16kPa substrates, respectively. These observations were accompanied by significant changes in E-cadherin and vimentin expression. Furthermore, we demonstrate that the bone-mimicking substrate (64kPa) induced multicellular cluster formation accompanied by E-cadherin cell surface localisation. MDA-MB-231 cells responded to different substrate stiffness by morphological adaptation, changes in proliferation rate and cytoskeleton markers, and cluster formation on bone-mimicking substrate. Our results suggest that the stiffest microenvironment can induce MET.

TGF-β-dependent reprogramming of amino acid metabolism induces epithelial-mesenchymal transition in non-small cell lung cancers

Epithelial–mesenchymal transition (EMT)—a fundamental process in embryogenesis and wound healing—promotes tumor metastasis and resistance to chemotherapy. While studies have identified signaling components and transcriptional factors responsible in the TGF-β-dependent EMT, whether and how intracellular metabolism is integrated with EMT remains to be fully elucidated. Here, we showed that TGF-β induces reprogramming of intracellular amino acid metabolism, which is necessary to promote EMT in non-small cell lung cancer cells. Combined metabolome and transcriptome analysis identified prolyl 4-hydroxylase α3 (P4HA3), an enzyme implicated in cancer metabolism, to be upregulated during TGF-β stimulation. Further, knockdown of P4HA3 diminished TGF-β-dependent changes in amino acids, EMT, and tumor metastasis. Conversely, manipulation of extracellular amino acids induced EMT-like responses without TGF-β stimulation. These results suggest a previously unappreciated requirement for the reprogramming of amino acid metabolism via P4HA3 for TGF-β-dependent EMT and implicate a P4HA3 inhibitor as a potential therapeutic agent for cancer.

Through metabolome and transcriptome analyses, Nakasuka et al find that TGF-β-induced epithelial–mesenchymal transition (EMT) in non-small cell lung cancer cells is associated with reprogramming of amino acid metabolism. They also identify P4HA3 as a key enzyme involved in these changes altogether providing insights into potential mechanisms of metastasis.

Immune-related genes LAMA2 and IL1R1 correlate with tumor sites and predict poor survival in pancreatic adenocarcinoma

Aims: The aim of this study was to identify the immune- and locus-associated genes in pancreatic ductal adenocarcinoma and evaluate their value in prognosis. Methods: The pancreatic ductal adenocarcinoma stromal and immune scores were calculated with the estimation of stromal and immune cells in malignant tumor tissues using expression data algorithm. The authors screened the differentially expressed genes to generate immune- and stromal-related differentially expressed genes. Next, the authors conducted weighted correlation network analysis to find the gene sets related to tumor sites. Results: IL1R1 and LAMA2 were identified as the site- and immune-related genes in pancreatic ductal adenocarcinoma, and their high expression in pancreatic head cancer exhibited high immune scores and predicted unfavorable prognosis. Conclusion: The authors identified IL1R1 and LAMA2 as immune- and locus-associated genes, and their high expression predicted a poor prognosis.

ADAMTS12 acts as a tumor microenvironment related cancer promoter in gastric cancer

The infiltration degree of immune and stromal cells has been shown clinically significant in tumor microenvironment (TME). However, the utility of stromal and immune components in Gastric cancer (GC) has not been investigated in detail. In the present study, ESTIMATE and CIBERSORT algorithms were applied to calculate the immune/stromal scores and the proportion of tumor-infiltrating immune cell (TIC) in GC cohort, including 415 cases from The Cancer Genome Atlas (TCGA) database. The differentially expressed genes (DEGs) were screened by Cox proportional hazard regression analysis and protein-protein interaction (PPI) network construction. Then ADAMTS12 was regarded as one of the most predictive factors. Further analysis showed that ADAMTS12 expression was significantly higher in tumor samples and correlated with poor prognosis. Gene Set Enrichment Analysis (GSEA) indicated that in high ADAMTS12 expression group gene sets were mainly enriched in cancer and immune-related activities. In the low ADAMTS12 expression group, the genes were enriched in the oxidative phosphorylation pathway. CIBERSORT analysis for the proportion of TICs revealed that ADAMTS12 expression was positively correlated with Macrophages M0/M1/M2 and negatively correlated with T cells follicular helper. Therefore, ADAMTS12 might be a tumor promoter and responsible for TME status and tumor energy metabolic conversion.

Development of an Immune-Related Prognostic Index Associated With Glioblastoma

Background: Although the tumor microenvironment (TME) is known to influence the prognosis of glioblastoma (GBM), the underlying mechanisms are not clear. This study aims to identify hub genes in the TME that affect the prognosis of GBM. Methods: The transcriptome profiles of the central nervous systems of GBM patients were downloaded from The Cancer Genome Atlas (TCGA). The ESTIMATE scoring algorithm was used to calculate immune and stromal scores. The application of these scores in histology classification was tested. Univariate Cox regression analysis was conducted to identify genes with prognostic value. Subsequently, functional enrichment analysis and protein-protein interaction (PPI) network analysis were performed to reveal the pathways and biological functions associated with the genes. Next, these prognosis genes were validated in an independent GBM cohort from the Chinese Glioma Genome Atlas (CGGA). Finally, the efficacy of current antitumor drugs targeting these genes against glioma was evaluated. Results: Gene expression profiles and clinical data of 309 GBM samples were obtained from TCGA database. Higher immune and stromal scores were found to be significantly correlated with tissue type and poor overall survival (OS) (p = 0.15 and 0.77, respectively). Functional enrichment analysis identified 860 upregulated and 162 downregulated cross genes, which were mainly linked to immune response, inflammatory response, cell membrane, and receptor activity. Survival analysis identified 228 differentially expressed genes associated with the prognosis of GBM (p ≤ 0.05). A total of 48 hub genes were identified by the Cytoscape tool, and pathway enrichment analysis of the genes was performed using Database for Annotation, Visualization and Integrated Discovery (DAVID). The 228 genes were validated in an independent GBM cohort from the CGGA. In total, 10 genes were found to be significantly associated with prognosis of GBM. Finally, 14 antitumor drugs were identified by drug-gene interaction analysis. Conclusions: Here, 10 TME-related genes and 14 corresponding antitumor agents were found to be associated with the prognosis and OS of GBM.

Identification of a Prognostic Signature for Ovarian Cancer Based on the Microenvironment Genes

Background: Ovarian cancer is highly malignant and has a poor prognosis in the advanced stage. Studies have shown that infiltration of tumor microenvironment cells, immune cells and stromal cells has an important impact on the prognosis of cancers. However, the relationship between tumor microenvironment genes and the prognosis of ovarian cancer has not been studied.

Methods: Gene expression profiles and SNP data of ovarian cancer were downloaded from the TCGA database. Cluster analysis, WGCNA analysis and univariate survival analysis were used to identify immune microenvironment genes as prognostic signatures for predicting the survival of ovarian cancer patients. External data were used to evaluate the signature. Moreover, the top five significantly correlated genes were evaluated by immunohistochemical staining of ovarian cancer tissues.

Results: We systematically analyzed the relationship between ovarian cancer and immune metagenes. Immune metagenes expression were associated with prognosis. In total, we identified 10 genes related to both immunity and prognosis in ovarian cancer according to the expression of immune metagenes. These data reveal that high expression of ETV7 (OS, HR = 1.540, 95% CI 1.023–2.390, p = 0.041), GBP4 (OS, HR = 1.834, 95% CI 1.242–3.055, p = 0.004), CXCL9 (OS, HR = 1.613, 95% CI 1.080 –2.471, p = 0.021), CD3E (OS, HR = 1.590, 95% CI 1.049 –2.459, p = 0.031), and TAP1 (OS, HR = 1.766, 95% CI 1.163 –2.723, p = 0.009) are associated with better prognosis in patients with ovarian cancer.

Conclusion: Our study identified 10 immune microenvironment genes related to the prognosis of ovarian cancer. The list of tumor microenvironment-related genes provides new insights into the underlying biological mechanisms driving the tumorigenesis of ovarian cancer.

Comprehensive description of the current breast cancer microenvironment advancements via single-cell analysis

Breast cancer is a heterogeneous disease with a complex microenvironment consisting of tumor cells, immune cells, fibroblasts and vascular cells. These cancer-associated cells shape the tumor microenvironment (TME) and influence the progression of breast cancer and the therapeutic responses in patients. The exact composition of the intra-tumoral cells is mixed as the highly heterogeneous and dynamic nature of the TME. Recent advances in single-cell technologies such as single-cell DNA sequencing (scDNA-seq), single-cell RNA sequencing (scRNA-seq) and mass cytometry have provided new insights into the phenotypic and functional diversity of tumor-infiltrating cells in breast cancer. In this review, we have outlined the recent progress in single-cell characterization of breast tumor ecosystems, and summarized the phenotypic diversity of intra-tumoral cells and their potential prognostic relevance.

Seven-gene signature on tumor microenvironment for predicting the prognosis of patients with pancreatic cancer

Background

The aim of the present study was to construct a novel gene signature on the tumor microenvironment (TME) to predict the prognosis of patients with pancreatic ductal adenocarcinoma (PDAC).

Methods

We downloaded gene expression profiles and clinical information of PDAC from The Cancer Genome Atlas (TCGA) datasets, as well as Gene Expression Omnibus (GEO) datasets (GSE78229, GSE62452, and GSE28735). Differentially expressed genes were generated by comparing high versus low score groups of immune/stromal subgroups based on the Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data algorithm. Subsequently, a prognostic risk score model was constructed and validated through univariate and multivariate Cox regression analyses. Finally, functional enrichment analysis and protein-protein interactions were performed to predict the functional implication of the prognostic model.

Results

We picked out 1,797 upregulated genes in immune groups and stromal groups. Through further analysis, we constructed a 7-gene signature on the TME. The risk score from the model effectively differentiated patients into high-risk and low-risk groups with different overall survival and was validated by GEO datasets. A functional analysis suggested that 7 selected genes and their co-expressed genes were mainly enriched in immune response, extracellular structure organization, and cell adhesion molecule binding.

Conclusions

Our results showed that the 7-gene model on the TME can be used to assess the prognosis of patients with PDAC.

PDIA5 is Correlated With Immune Infiltration and Predicts Poor Prognosis in Gliomas

Gliomas are the most common and lethal primary malignant tumor of the brain. Routine treatment including surgical resection, chemotherapy, and radiotherapy produced limited therapeutic effect, while immunotherapy targeting the glioma microenvironment has offered a novel therapeutic option. PDIA5 protein is the member of PDI family, which is highly expressed in glioma and participates in glioma progression. Based on large-scale bioinformatics analysis, we discovered that PDIA5 expression level is upregulated in aggressive gliomas, with high PDIA5 expression predicting poor clinical outcomes. We also observed positive correlation between PDIA5 and immune infiltrating cells, immune related pathways, inflammatory activities, and other immune checkpoint members. Patients with high PDIA5 high-expression benefited from immunotherapies. Additionally, immunohistochemistry revealed that PDIA5 and macrophage biomarker CD68 were upregulated in high-grade gliomas, and patients with low PDIA5 level experienced favorable outcomes among 33 glioma patients. Single cell RNA sequencing exhibited that PDIA5 was in high level presenting in neoplastic cells and macrophages. Cell transfection and co-culture of glioma cells and macrophages revealed that PDIA5 in tumor cells mediated macrophages exhausting. Altogether, our findings indicate that PDIA5 overexpression is associated with immune infiltration in gliomas, and may be a promising therapeutic target for glioma immunotherapy.

Identification and Verification of Immune-Related Gene Prognostic Signature Based on ssGSEA for Osteosarcoma

Osteosarcoma is the most common malignant bone tumor in children and adolescence. Multiple immune-related genes have been reported in different cancers. The aim is to identify an immune-related gene signature for the prospective evaluation of prognosis for osteosarcoma patients. In this study, we evaluated the infiltration of immune cells in 101 osteosarcoma patients downloaded from TARGET using the ssGSEA to the RNA-sequencing of these patients, thus, high immune cell infiltration cluster, middle immune cell infiltration cluster and low immune cell infiltration cluster were generated. On the foundation of high immune cell infiltration cluster vs. low immune cell infiltration cluster and normal vs. osteosarcoma, we found 108 common differentially expressed genes which were sequentially submitted to univariate Cox and LASSO regression analysis. Furthermore, GSEA indicated some pathways with notable enrichment in the high- and low-immune cell infiltration cluster that may be helpful in understanding the potential mechanisms. Finally, we identified seven immune-related genes as prognostic signature for osteosarcoma. Kaplan-Meier analysis, ROC curve, univariate and multivariate Cox regression further confirmed that the seven immune-related genes signature was an innovative and significant prognostic factor independent of clinical features. These results of this study offer a means to predict the prognosis and survival of osteosarcoma patients with uncovered seven-gene signature as potential biomarkers.

Prognostic Hub Genes in the Immune Microenvironment of Lung Adenocarcinoma by Estimation

BACKGROUND

The mortality of lung adenocarcinoma(LUAD) is high. Recent studies have found that the degree of immune infiltration and stromal cells in the tumour microenvironment or tumours makes a significant contribution to prognosis.

METHODS

During study, we screened differentially expressed genes (DEGs) of TCGA database for prognostic genes in LUAD immune microenvironment. Further, immune and stromal cells were quantified using ESTIMATE algorithm. To study the effects of immune and stromal cell-associated genes on the prognosis of LUAD, LUAD patients were divided into high and low groups according to their immune/ stromal scores. The obtained scores were found to be related to the phenotype and survival rate of LUAD patients. By selecting DEGs with high expression in immune and stromal cells, we performed functional enrichment analysis and found that most genes are associated with pathways of cancer, stimulus response and the MAPK signaling. The functions and enriched pathways of LUAD prognostic genes were shown by a protein-protein interaction (PPI) network. Nonetheless, an external database was used to validate the prognostic genes from the TCGA.

RESULTS

Prognostic genes were listed according to their expression position and protein function.

CONCLUSION

We provided a new targets for immunotherapy of LUAD, which further provides basic knowledge for future clinical research.

A Molecular Subtype Model for Liver HBV-Related Hepatocellular Carcinoma Patients Based on Immune-Related Genes

Hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC) is one of the most common malignant tumors in the world with a very poor prognosis. Immunotyping is of great significance for predicting HCC outcomes and guiding immunotherapy. Therefore, we sought to establish a reliable prognostic model for HBV-related HCC based on immune scores. We identified immune-related modules of The Cancer Genome Atlas LIHC and GSE14520 data sets through weighted gene co-expression network analysis and evaluated HCC through a non-negative matrix factorization algorithm. Through further bioinformatics analyses, we identified causes for prognostic differences between subtypes. The results illustrate a significant difference in prognosis based on immunotypes, which may stem from metabolic disorders and increased tumor invasion associated with the high expression of genes related to stem cell characteristics. In conclusion, we identified a novel HBV-related HCC immune subtype and determined its immunological characteristics, which provides ideas for further individualized immunotherapy research.

miR-9-Mediated Inhibition of EFEMP1 Contributes to the Acquisition of Pro-Tumoral Properties in Normal Fibroblasts

Tumor growth and invasion occurs through a dynamic interaction between cancer and stromal cells, which support an aggressive niche. MicroRNAs are thought to act as tumor messengers to “corrupt” stromal cells. We previously demonstrated that miR-9, a known metastamiR, is released by triple negative breast cancer (TNBC) cells to enhance the transition of normal fibroblasts (NFs) into cancer-associated fibroblast (CAF)-like cells. EGF containing fibulin extracellular matrix protein 1 (EFEMP1), which encodes for the ECM glycoprotein fibulin-3, emerged as a miR-9 putative target upon miRNA’s exogenous upmodulation in NFs. Here we explored the impact of EFEMP1 downmodulation on fibroblast’s acquisition of CAF-like features, and how this phenotype influences neoplastic cells to gain chemoresistance. Indeed, upon miR-9 overexpression in NFs, EFEMP1 resulted downmodulated, both at RNA and protein levels. The luciferase reporter assay showed that miR-9 directly targets EFEMP1 and its silencing recapitulates miR-9-induced pro-tumoral phenotype in fibroblasts. In particular, EFEMP1 siRNA-transfected (si-EFEMP1) fibroblasts have an increased ability to migrate and invade. Moreover, TNBC cells conditioned with the supernatant of NFs transfected with miR-9 or si-EFEMP1 became more resistant to cisplatin. Overall, our results demonstrate that miR-9/EFEMP1 axis is crucial for the conversion of NFs to CAF-like cells under TNBC signaling.

Identification of Key Genes of Prognostic Value in Clear Cell Renal Cell Carcinoma Microenvironment and a Risk Score Prognostic Model

Objective

We aimed at identifying the key genes of prognostic value in clear cell renal cell carcinoma (ccRCC) microenvironment and construct a risk score prognostic model.

Materials and Methods

Immune and stromal scores were calculated using the ESTIMATE algorithm. A total of 539 ccRCC cases were divided into high- and low-score groups. The differentially expressed genes in immune and stromal cells for the prognosis of ccRCC were screened. The relationship between survival outcome and gene expression was evaluated using univariate and multivariate Cox proportional hazard regression analyses. A risk score prognostic model was constructed based on the immune/stromal scores.

Results

The median survival time of the low immune score group was longer than that of the high immune score group (p = 0.044). Ten tumor microenvironment-related genes were selected by screening, and a predictive model was established, based on which patients were divided into high- and low-risk groups with markedly different overall survival (p < 0.0001). Multivariate Cox analyses showed that the risk score prognostic model was independently associated with overall survival, with a hazard ratio of 1.0437 (confidence interval: 1.0237-1.0641, p < 0.0001).

Conclusions

Low immune scores were associated with extended survival time compared to high immune scores. The novel risk predictive model based on tumor microenvironment-related genes may be an independent prognostic biomarker in ccRCC.

Zonal regulation of collagen-type proteins and posttranslational modifications in prostatic benign and cancer tissues by imaging mass spectrometry

BACKGROUND

The emergence of reactive stroma is a hallmark of prostate cancer (PCa) progression and a potential source for prognostic and diagnostic markers of PCa. Collagen is a main component of reactive stroma and changes systematically and quantitatively to reflect the course of PCa, yet has remained undefined due to a lack of tools that can define collagen protein structure. Here we use a novel collagen-targeting proteomics approach to investigate zonal regulation of collagen-type proteins in PCa prostatectomies.

METHODS

Prostatectomies from nine patients were divided into zones containing 0%, 5%, 20%, 70% to 80% glandular tissue and 0%, 5%, 25%, 70% by mass of PCa tumor following the McNeal model. Tissue sections from zones were graded by a pathologist for Gleason score, percent tumor present, percent prostatic intraepithelial neoplasia and/or inflammation (INF). High-resolution accurate mass collagen targeting proteomics was done on a select subset of tissue sections from patient-matched tumor or nontumor zones. Imaging mass spectrometry was used to investigate collagen-type regulation corresponding to pathologist-defined regions.

RESULTS

Complex collagen proteomes were detected from all zones. COL17A and COL27A increased in zones of INF compared with zones with tumor present. COL3A1, COL4A5, and COL8A2 consistently increased in zones with tumor content, independent of tumor size. Collagen hydroxylation of proline (HYP) was altered in tumor zones compared with zones with INF and no tumor. COL3A1 and COL5A1 showed significant changes in HYP peptide ratios within tumor compared with zones of INF (2.59 ± 0.29, P value: .015; 3.75 ± 0.96 P value .036, respectively). By imaging mass spectrometry COL3A1 showed defined localization and regulation to tumor pathology. COL1A1 and COL1A2 showed gradient regulation corresponding to PCa pathology across zones. Pathologist-defined tumor regions showed significant increases in COL1A1 HYP modifications compared with COL1A2 HYP modifications. Certain COL1A1 and COL1A2 peptides could discriminate between pathologist-defined tumor and inflammatory regions.

CONCLUSIONS

Site-specific posttranslational regulation of collagen structure by proline hydroxylation may be involved in reactive stroma associated with PCa progression. Translational and posttranslational regulation of collagen protein structure has potential for new markers to understand PCa progression and outcomes.

Analysis of prognostic genes in the tumor microenvironment of lung adenocarcinoma

Background

Prognostic genes in the tumor microenvironment play an important role in immune biological processes and the response of cancer to immunotherapy. Thus, we aimed to assess new biomarkers that are associated with immune/stromal cells in lung adenocarcinomas (LUAD) using the ESTIMATE algorithm, which also significantly affects the prognosis of cancer.

Methods

The RNA sequencing (RNA-Seq) and clinical data of LUAD were downloaded from the the Cancer Genome Atlas (TCGA ). The immune and stromal scores were calculated for each sample using the ESTIMATE algorithm. The LUAD gene chip expression profile data and the clinical data (GSE37745, GSE11969, and GSE50081) were downloaded from the Gene Expression Omnibus (GEO) for subsequent validation analysis. Differentially expressed genes were calculated between high and low score groups. Univariate Cox regression analysis was performed on differentially expressed genes (DEGs) between the two groups to obtain initial prognosis genes. These were verified by three independent LUAD cohorts from the GEO database. Multivariate Cox regression was used to identify overall survival-related DEGs. UALCAN and the Human Protein Atlas were used to analyze the mRNA /protein expression levels of the target genes. Immune cell infiltration was evaluated using the Tumor Immune Estimation Resource (TIMER) and CIBERSORT methods, and stromal cell infiltration was assessed using xCell.

Results

In this study, immune scores and stromal scores are significantly associated with the clinical characteristics of LUAD, including T stage, M stage, pathological stage, and overall survival time. 530 DEGs (18 upregulated and 512 downregulated) were found to coexist in the difference analysis with the immune scores and stromal scores subgroup. Univariate Cox regression analysis showed that 286 of the 530 DEGs were survival-related genes (p < 0.05). Of the 286 genes initially identified, nine prognosis-related genes (CSF2RB, ITK, FLT3, CD79A, CCR4, CCR6, DOK2, AMPD1, and IGJ) were validated from three separate LUAD cohorts. In addition, functional analysis of DEGs also showed that various immunoregulatory molecular pathways, including regulation of immune response and the chemokine signaling pathways, were involved. Five genes (CCR6, ITK, CCR4, DOK2, and AMPD1) were identified as independent prognostic indicators of LUAD in specific data sets. The relationship between the expression levels of these genes and immune genes was assessed. We found that CCR6 mRNA and protein expression levels of LUAD were greater than in normal tissues. We evaluated the infiltration of immune cells and stromal cells in groups with high and low levels of expression of CCR6 in the TCGA LUAD cohort. In summary, we found a series of prognosis-related genes that were associated with the LUAD tumor microenvironment.

Exploring immune-related genes with prognostic value in microenvironment of breast cancer from TCGA database

Breast cancer is one of the most common malignancies in women worldwide. Many studies have shown that tumor microenvironment cells, immune cells, and stromal cell infiltration have an important impact on prognosis, so it is important to identify biomarkers for achieving better treatment and prognosis.To better understand the relationship between immune and stromal cell-related genes and prognosis, we screened patients with breast cancer in The Cancer Genome Atlas (TCGA) database and divided them into high and low groups based on immune/stromal scores. We next identified differentially expressed immune-related genes that are significantly associated with the prognosis of patients with breast cancer for functional enrichment analysis and protein-protein interaction networks, respectively. Finally, we selected a separate breast cancer cohort in gene expression synthesis (GEO) for validation.Both immune scores and stromal scores are meaningful in the correlation of subtype classification. Disease-free survival of cases with the high score group of immune scores is statistically longer than the cases in the low score group. Differentially expressed immune-related genes extracted from the comparison can effectively evaluate the prognosis of patients with breast cancer and these genes are primarily involved in immune responses, extracellular matrix, and chemokine activity. At last, we obtained a series of verified tumor immune-related genes that predict the prognosis of patients with breast cancer.Combining the Estimation of Stromal and Immune Cells in Malignant Tumor Tissues using Expression database and the TCGA database to extract the list of tumor microenvironment related genes which may help to outline the prognosis of patients with breast cancer. Some previously overlooked genes have the potential to become additional biomarkers for breast cancer. Further research on these genes can reveal a new understanding of the potential relationship between tumor microenvironment and breast cancer prognosis.

Prognostic value of immune-related genes in the tumor microenvironment of lung adenocarcinoma and lung squamous cell carcinoma

Non-small cell lung cancer (NSCLC), which consists mainly of lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC), are the leading cause of cancer deaths worldwide. In this study, we performed a comprehensive analysis of the tumor microenvironmental and genetic factors to identify prognostic biomarkers for NSCLC. We evaluated the immune and stromal scores of patients with LUAD and LUSC using data from The Cancer Genome Atlas database with the ESTIMATE algorithm. Based on these scores, the differentially expressed genes were obtained and immune-related prognostic genes were identified. Functional analysis and protein-protein interaction network further revealed the immune-related biological processes in which these genes participated. Additionally, 22 subsets of tumor-infiltrating immune cells (TIICs) in the tumor microenvironment were analyzed with the CIBERSORT algorithm. Finally, we validated these valuable genes using an independent cohort from the Gene Expression Omnibus database. The associations of the immune and stromal scores with patients’ clinical characteristics and prognosis were positive in LUAD but negative in LUSC and the correlations of TIICs with clinical characteristics were clarified. Several differentially expressed genes were identified to be potential immune-related prognostic genes. This study comprehensively analyzed the tumor microenvironment and presented immune-related prognostic biomarkers for NSCLC.

Screening TCGA database for prognostic genes in lower grade glioma microenvironment

Background

To identify prognostic hub genes which associated with tumor microenvironment (TME) in lower grade glioma (LGG) of central nervous system.

Methods

We downloaded LGG patients gene transcriptome profiles of the central nervous system in The Cancer Genome Atlas (TCGA) database. Clinical characteristics and survival data through the Genomic Data Commons (GDC) tool were extracted. We used limma package for normalization processing. Scores of immune, stromal and ESTIMATE were calculated using ESTIMATE algorithm. Then, box plots were applied to explore the association between immune scores, stromal scores, ESTIMATE scores and histological type, tumor grade. Kaplan-Meier (K-M) analysis was utilized to explore the prognostic value of scores. Furthermore, heatmaps and volcano plots were applied for visualizing expression of differential expressed-gene screening and cluster analysis. Venn plots were constructed to screen the intersected differentially expressed genes (DEGs). In addition, enrichment of functions and signaling pathways and Gene Set Enrichment Analysis (GESA) of the DEGs were performed. Then we used protein-protein interaction (PPI) network and Cytoscape software to identify hub genes. We evaluated the prognostic value of hub genes and risk score (RS) calculated based on multivariate cox regression analysis. Finally, relationships of hub genes with the TME of LGG patients were evaluated based on tumor immune estimation resource (TIMER) database.

Results

Gene expression profiles and clinical data of 514 LGG samples were extracted and the results revealed that higher scores were significantly related with histological types and higher tumor grade (P<0.0001, respectively). Besides, higher scores were associated with worse survival outcomes in immune scores (P=0.0167), stromal scores (P=0.0035) and ESTIMATE scores (P=0.0190). Then, 785 up-regulated intersected genes and 357 down-regulated intersected genes were revealed. Functional enrichment analysis revealed that intersected genes were associated with immune response, inflammatory response, plasma membrane and receptor activity. After PPI network construction and cytoHubba analysis, 25 tumor immune-related hub genes were identified and enriched pathways were identified by GSEA. Besides, receiver operating characteristic (ROC) curves showed significantly predictive accuracy [area under curve (AUC) =0.771] of RS. Furthermore, significant prognostic values of hub genes were observed, and the relationships between hub genes and LGG TME were demonstrated.

Conclusions

We identified 25 TME-related genes which significantly associated with overall survival in patients with central nervous system LGG from TCGA database.

Screening the Cancer Genome Atlas Database for Genes of Prognostic Value in Acute Myeloid Leukemia

Object: To identify genes of prognostic value which associated with tumor microenvironment (TME) in acute myeloid leukemia (AML).

Methods and Materials: Level 3 AML patients gene transcriptome profiles were downloaded from The Cancer Genome Atlas (TCGA) database. Clinical characteristics and survival data were extracted from the Genomic Data Commons (GDC) tool. Then, limma package was utilized for normalization processing. ESTIMATE algorithm was used for calculating immune, stromal and ESTIMATE scores. We examined the distribution of these scores in Cancer and Acute Leukemia Group B (CALGB) cytogenetics risk category. Kaplan-Meier (K-M) curves were used to evaluate the relationship between immune scores, stromal scores, ESTIMATE scores and overall survival. We performed clustering analysis and screened differential expressed genes (DEGs) by using heatmaps, volcano plots and Venn plots. After pathway enrichment analysis and gene set enrichment analysis (GESA), protein-protein interaction (PPI) network was constructed and hub genes were screened. We explore the prognostic value of hub genes by calculating risk scores (RS) and processing survival analysis. Finally, we verified the expression level, association of overall survival and gene interactions of hub genes in the Vizome database.

Results: We enrolled 173 AML samples from TCGA database in our study. Higher immune score was associated with higher risk rating in CALGB cytogenetics risk category (P = 0.0396) and worse overall survival outcomes (P = 0.0224). In Venn plots, 827 intersect genes were screened with differential analysis. Functional enrichment clustering analysis revealed a significant association between intersect genes and the immune response. After PPI network, 18 TME-related hub genes were identified. RS was calculated and the survival analysis results revealed that high RS was related with poor overall survival (P < 0.0001). Besides, the survival receiver operating characteristic curve (ROC) showed superior predictive accuracy (area under the curve = 0.725). Finally, the heatmap from Vizome database demonstrated that 18 hub genes showed high expression in patient samples.

Conclusion: We identified 18 TME-related genes which significantly associated with overall survival in AML patients from TCGA database.

Immune-related prognosis biomarkers associated with osteosarcoma microenvironment

BACKGROUND

Osteosarcoma is a highly aggressive bone tumor that most commonly affects children and adolescents. Treatment and outcomes for osteosarcoma have remained unchanged over the past 30 years. The relationship between osteosarcoma and the immune microenvironment may represent a key to its undoing.

METHODS

We calculated the immune and stromal scores of osteosarcoma cases from the Target database using the ESTIMATE algorithm. Then we used the CIBERSORT algorithm to explore the tumor microenvironment and analyze immune infiltration of osteosarcoma. Differentially expressed genes (DEGs) were identified based on immune scores and stromal scores. Search Tool for the Retrieval of Interacting Genes Database (STRING) was utilized to assess protein-protein interaction (PPI) information, and Molecular Complex Detection (MCODE) plugin was used to screen hub modules of PPI network in Cytoscape. The prognostic value of the gene signature was validated in an independent GSE39058 cohort. Gene set enrichment analysis (GSEA) was performed to study the hub genes in signaling pathways.

RESULTS

From 83 samples of osteosarcoma obtained from the Target dataset, 137 DEGs were identified, including 134 upregulated genes and three downregulated genes. Functional enrichment analysis and PPI networks demonstrated that these genes were mainly involved in neutrophil degranulation and neutrophil activation involved in immune response, and participated in neuroactive ligand-receptor interaction and staphylococcus aureus infection.

CONCLUSIONS

Our study established an immune-related gene signature to predict outcomes of osteosarcoma, which may be important targets for individual treatment.

Prognostic genes in the tumor microenvironment in cervical squamous cell carcinoma

Cervical squamous cell carcinoma (CSCC) is one of the most commonly occurring gynecological malignancies. Because CSCC is a biologically heterogeneous disease, its prognosis varies. Therefore, identifying prognostic biomarkers that reflect its biological heterogeneity could lead to better interventions for patients with a poor prognosis. This study used the ESTIMATE algorithm to identify immune related prognostic genes within the tumor microenvironment of CSCC. The results revealed that high immune scores were associated with better overall survival (P = 0.029). Differential expression analysis revealed 384 intersection genes influencing both the immune and stromal scores. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed the 384 intersection genes to be mainly enriched for T cell activation, the region of the membrane, carbohydrate binding, and cytokine-cytokine receptor interaction. Among them, 149 immune genes were predictive of overall survival in CSCC. These findings provide a more comprehensive understanding of immune genes within the tumor microenvironment as well as a list of immune genes prognostic in CSCC.

Emerging immune gene signatures as prognostic or predictive biomarkers in breast cancer

Several multigene assays have been developed to predict the risk of distant recurrence and response to adjuvant therapy in early breast cancer. However, the prognostic or predictive value of current proliferation gene signature-based assays are limited to hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR+/HER2-) early breast cancer. Considerable discordance between the different assays in classifying patients into risk groups has also been reported, thus raising questions about the clinical utility of these assays for individual patients. Therefore, there still remains a need for better prognostic or predictive biomarkers for breast cancer. The role of immune cells comprising tumor microenvironment in tumor progression has been recognized. Accumulating evidences have shown that immune gene signatures and tumor-infiltrating lymphocytes (TILs) can be prognostic or predictive factors in breast cancer, particularly with regard to HER2+ and triple-negative breast cancer. In this review, I summarize current multigene assays for breast cancer and discuss recent progress in identifying novel breast cancer biomarkers, focusing on the emerging importance of immune gene signatures and TILs as prognostic or predictive biomarkers.

COL6A6 interacted with P4HA3 to suppress the growth and metastasis of pituitary adenoma via blocking PI3K-Akt pathway

The role and mechanism of collagen type VI alpha 6 (COL6A6) on tumor growth and metastasis in pituitary adenoma (PA) was determined. COL6A6 was downregulated in PA tissues and cell lines, which was negatively associated with the expression of prolyl-4-hydroxylase alpha polypeptide III (P4HA3) in the progression of PA. Overexpression of COL6A6 significantly suppressed tumor growth and metastasis capacity in PA. In addition, P4HA3 worked as the upstream of the PI3K-Akt pathway to alleviate the antitumor activity of COL6A6 on the growth and metastasis of both AtT-20 and HP75 cells. Furthermore, the inhibitory effect of COL6A6 on cell proliferation, migration and invasion, and epithelial-mesenchymal transition (EMT) was reversed by P4HA3 overexpression or activation of the PI3K-Akt pathway induced by IGF-1 addition, which provided a new biomarker for clinical PA treatment.

Mining TCGA database for genes of prognostic value in glioblastoma microenvironment

Glioblastoma (GBM) is one of the most deadly brain tumors. The convenient access to The Cancer Genome Atlas (TCGA) database allows for large-scale global gene expression profiling and database mining for potential correlation between genes and overall survival of a variety of malignancies including GBM. Previous reports have shown that tumor microenvironment cells and the extent of infiltrating immune and stromal cells in tumors contribute significantly to prognosis. Immune scores and stromal scores calculated based on the ESTIMATE algorithm could facilitate the quantification of the immune and stromal components in a tumor. To better understand the effects of genes involved in immune and stromal cells on prognosis, we categorized GBM cases in the TCGA database according to their immune/stromal scores into high and low score groups, and identified differentially expressed genes whose expression was significantly associated with prognosis in GBM patients. Functional enrichment analysis and protein-protein interaction networks further showed that these genes mainly participated in immune response, extracellular matrix, and cell adhesion. Finally, we validated these genes in an independent GBM cohort from the Chinese Glioma Genome Atlas (CGGA). Thus, we obtained a list of tumor microenvironment-related genes that predict poor outcomes in GBM patients.