Immune Profiles of Tumor Microenvironment and Clinical Prognosis among Women with Triple-Negative Breast Cancer

Metabolic reprogramming involves in transition of activated/resting CD4+ memory T cells and prognosis of gastric cancer

Background

Little is known on how metabolic reprogramming potentially prompts transition of activated and resting CD4+ memory T cells infiltration in tumor microenvironment of gastric cancer (GC). The study aimed to evaluate their interactions and develop a risk model for predicting prognosis in GC.

Methods

Expression profiles were obtained from TCGA and GEO databases. An immunotherapeutic IMvigor210 cohort was also enrolled. CIBERSORT algorithm was used to evaluate the infiltration of immune cells. The ssGSEA method was performed to assess levels of 114 metabolism pathways. Prognosis and correlation analysis were conducted to identify metabolism pathways and genes correlated with activated CD4+ memory T cells ratio (AR) and prognosis. An AR-related metabolism gene (ARMG) risk model was constructed and validated in different cohorts. Flow cytometry was applied to validate the effect of all-trans retinoic acid (ATRA) on CD4+ memory T cells.

Results

Since significantly inverse prognostic value and negative correlation of resting and activated CD4+ memory T cells, high AR level was associated with favorable overall survival (OS) in GC. Meanwhile, 15 metabolism pathways including retinoic acid metabolism pathway were significantly correlated with AR and prognosis. The ARMG risk model could classify GC patients with different outcomes, treatment responses, genomic and immune landscape. The prognostic value of the model was also confirmed in the additional validation, immunotherapy and pan-cancer cohorts. Functional analyses revealed that the ARMG model was positively correlated with pro-tumorigenic pathways. In vitro experiments showed that ATRA could inhibit levels of activated CD4+ memory T cells and AR.

Conclusion

Our study showed that metabolic reprogramming including retinoic acid metabolism could contribute to transition of activated and resting CD4+ memory T cells, and affect prognosis of GC patients. The ARMG risk model could serve as a new tool for GC patients by accurately predicting prognosis and response to treatment.

The prognostic impact of tumor-infiltrating B lymphocytes in patients with solid malignancies: A systematic review and meta-analysis

This study reviewed the prognostic effect of tumor-infiltrating B lymphocytes (TIBLs) on solid malignancies, to determine the potential role of TIBLs in predicting cancer patient's prognosis and their response to immunotherapy. A total of 45 original papers involving 11,099 individual patients were included in this meta-analysis covering 7 kinds of cancer. The pooled results suggested that high levels of TIBLs were correlated with favorable OS in lung, esophageal, gastric, colorectal, liver, and breast cancer; improved RFS in lung cancer; and improved DFS in gastrointestinal neoplasms. Additionally, TIBLs were significantly correlated with negative lymphatic invasion in gastric cancer, small tumor size in hepatocellular carcinoma, and negative distant metastasis in colorectal cancer. Additionally, TIBLs were reported as a discriminative feature of patients treated with immunotherapy with improved survival. We concluded that TIBLs play a favorable prognostic role among the common solid malignancie, providing theoretical evidence for further prognosis prediction for solid tumors.

Pan-Cancer Analysis of the Roles and Driving Forces of RAB42

RAB42 is a member of the RAS family. However, the roles and driving forces for RAB42 in tumors remain elusive. In this study, we performed a comprehensive pan-cancer analysis of the roles and regulatory mechanisms of RAB42 using bioinformatics and experiments. Online databases such as Sanger Box, ACLBI and TIDE were used to search for the expression levels, prognostic value and immune features of RAB42. We observed that RAB42 expression was upregulated in most tumors and was closely associated with poor prognosis. Enrichment analysis indicated that RAB42 was related to multiple biological functions, especially the immune process. RAB42 expression had a positive correlation with immune cell infiltration and immune checkpoint gene expression. RAB42 had a high predictive value for immunotherapy efficiency. Our study screened out susceptible drugs for the RAB42 protein by sensitivity analysis and virtual screening. Many key driver genes such as TP53 contributed to RAB42 expression. DNA methylation, super-enhancer and non-coding RNAs were the epigenetic factors responsible for RAB42 expression. In brief, RAB42 could serve as a diagnostic and prognostic biomarker in many tumor types. RAB42 might be a predictive biomarker and a new target for immunotherapy. Genetic and epigenetic factors were essential for RAB42 overexpression in tumors.

Hepatitis B virus pathogenesis relevant immunosignals uncovering amino acids utilization related risk factors guide artificial intelligence-based precision medicine

Background: Although immune microenvironment-related chemokines, extracellular matrix (ECM), and intrahepatic immune cells are reported to be highly involved in hepatitis B virus (HBV)-related diseases, their roles in diagnosis, prognosis, and drug sensitivity evaluation remain unclear. Here, we aimed to study their clinical use to provide a basis for precision medicine in hepatocellular carcinoma (HCC) via the amalgamation of artificial intelligence. Methods: High-throughput liver transcriptomes from Gene Expression Omnibus (GEO), NODE (https://www.bio.sino.org/node), the Cancer Genome Atlas (TCGA), and our in-house hepatocellular carcinoma patients were collected in this study. Core immunosignals that participated in the entire diseases course of hepatitis B were explored using the "Gene set variation analysis" R package. Using ROC curve analysis, the impact of core immunosignals and amino acid utilization related gene on hepatocellular carcinoma patient's clinical outcome were calculated. The utility of core immunosignals as a classifier for hepatocellular carcinoma tumor tissue was evaluated using explainable machine-learning methods. A novel deep residual neural network model based on immunosignals was constructed for the long-term overall survival (LS) analysis. In vivo drug sensitivity was calculated by the "oncoPredict" R package. Results: We identified nine genes comprising chemokines and ECM related to hepatitis B virus-induced inflammation and fibrosis as CLST signals. Moreover, CLST was co-enriched with activated CD4+ T cells bearing harmful factors (aCD4) during all stages of hepatitis B virus pathogenesis, which was also verified by our hepatocellular carcinoma data. Unexpectedly, we found that hepatitis B virus-hepatocellular carcinoma patients in the CLST^highaCD4^high subgroup had the shortest overall survival (OS) and were characterized by a risk gene signature associated with amino acids utilization. Importantly, characteristic genes specific to CLST/aCD4 showed promising clinical relevance in identifying patients with early-stage hepatocellular carcinoma via explainable machine learning. In addition, the 5-year long-term overall survival of hepatocellular carcinoma patients can be effectively classified by CLST/aCD4 based GeneSet-ResNet model. Subgroups defined by CLST and aCD4 were significantly involved in the sensitivity of hepatitis B virus-hepatocellular carcinoma patients to chemotherapy treatments. Conclusion: CLST and aCD4 are hepatitis B virus pathogenesis-relevant immunosignals that are highly involved in hepatitis B virus-induced inflammation, fibrosis, and hepatocellular carcinoma. Gene set variation analysis derived immunogenomic signatures enabled efficient diagnostic and prognostic model construction. The clinical application of CLST and aCD4 as indicators would be beneficial for the precision management of hepatocellular carcinoma.

'Why is survival with triple negative breast cancer so low? insights and talking points from preclinical and clinical research'

INTRODUCTION

Triple negative breast cancer is typically related to poor prognosis, early metastasis, and high recurrence rate. Intrinsic and extrinsic biological features of TNBC and resistance mechanisms to conventional therapies can support its aggressive behavior, characterizing TNBC how extremely heterogeneous. Novel combination strategies are under investigation, including immunotherapeutic agents, anti-drug conjugates, PARP inhibitors, and various targeting agents, exploring, in the meanwhile, possible predictive biomarkers to correctly select patients for the optimal treatment for their specific subtype.

AREAS COVERED

This article examines the main malignity characteristics across different subtype, both histological and molecular, and the resistance mechanisms, both primary and acquired, to different drugs explored in the landscape of TNBC treatment, that lead TNBC to still has high mortality rate.

EXPERT OPINION

The complexity of TNBC is not only the main reason of its aggressivity, but its heterogeneity should be exploited in terms of therapeutics opportunities, combining agents with different mechanism of action, after a correct selection by biologic or molecular biomarkers. The main goal is to understand what TNBC really is and to act selectively on its characteristics, with a personalized anticancer treatment.

HSP70 In triple negative breast cancer: Prognostic value and clinical significance

BACKGROUND

Triple-negative breast cancer (TNBC) has the worst prognosis and the highest immunogenic potential of all breast cancer subtypes. It is characterized by a lack of estrogen and progesterone receptors as well as HER2. A major component of the tumor microenvironment (TME) of TNBC is tumor-infiltrating lymphocytes (TILs). A chaperone heat shock protein 70 (HSP70) is involved in several pathways that enable tumour growth and progression, as well as in immune modulation.

METHODS

Immunohistochemical analysis of HSP70 expression in immune cells, as well as expression of immunosuppressive markers CTLA4 and PD-L1 and major TILs components: CD8, CD4 and Tregs were analyzed in the superficial and deep tumor layer of primary TNBC and compared with established clinicopathological parameters. Clinical data and surgical tissue samples from 68 TNBC patients who underwent initial surgery were included in the analysis and 36 control samples from benign breast tissue biopsies.

RESULTS

A higher expression of TILs, CD4, CD8 and PD-L1 was found in the invasive tumor front (ITF), as compared to the tumor center (TC) (p < 0001). HSP70 positive immune cells (HSP70(+) IC) in TC were associated with adverse clinical and pathological markers: higher stage of disease (P = 0.013), higher grade (P = 0.05) and a higher pN status (P < 0.001). In addition, higher expression of HSP70(+) IC from TC was correlated with the higher expression of FOXP3(+)T cells both in ITF (N = 61, rho=0.42, p < 0.001) and in metastatic tissue from the draining lymph nodes (N = 13, rho=0.61, P = 0.026).

CONCLUSION

Correlations between HSP70 immune cells expression and individual TILs components support the hypothesis of its active role in inducing immunosuppression and tumor progression. Routine determination of HSP70 expression, in immune cells of TC, may be of added value in the clinical decision-making process concerning axillary surgery.

PSMG2-controlled proteasome-autophagy balance mediates the tolerance for MEK-targeted therapy in triple-negative breast cancer

Although the MAPK pathway is aberrantly activated in triple-negative breast cancers (TNBCs), the clinical outcome of MEK-targeted therapy is still poor. Through a genome-wide CRISPR-Cas9 library screening, we find that inhibition of PSMG2 sensitizes TNBC cells BT549 and MB468 to the MEK inhibitor AZD6244. Mechanistically, PSMG2 knockdown impairs proteasome function, which in turn activates autophagy-mediated PDPK1 degradation. The PDPK1 degradation significantly enhances AZD6244-induced tumor cell growth inhibition by interrupting the negative feedback signals toward the AKT pathway. Consistently, co-targeting proteasomes and MEK with inhibitors synergistically suppresses tumor cell growth. The autophagy inhibitor chloroquine partially relieves the PDPK1 degradation and reverses the growth inhibition induced by combinatorial inhibition of MEK and proteasome. The combination regimen with the proteasome inhibitor MG132 plus AZD6244 synergistically inhibits tumor growth in a 4T1 xenograft mouse model. In summary, our study not only unravels the mechanism of MEK inhibitor resistance but also provides a combinatorial therapeutic strategy for TNBC in clinics.

Identification and validation of a novel necroptosis-related prognostic signature in cervical squamous cell carcinoma and endocervical adenocarcinoma

Background

The purpose of this study was to investigate the prognostic signature of necroptosis-related lncRNAs (NRLs) and explore their association with immune-related functions and sensitivity of the therapeutic drug in cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC).

Methods

UCSC Xena provided lncRNA sequencing and clinical data about CESC, and a necroptosis gene list was obtained from the KEGG database. NRLs were selected by structuring a co-expression network of lncRNAs and necroptosis-related genes. To further screen lncRNAs, we used the univariate Cox regression method, Lasso regression, and multivariate Cox regression. Afterward, an NRL signature was established. We used the xCell algorithm and single-sample gene set enrichment analysis (ssGSEA) to clarify the pertinence between immune infiltration and NRL expressions in CESC patients and explored the relationship between the target lncRNAs and immune-related genes. By leveraging the GDSC database, the therapy-sensitive response of the prognostic signature was forecasted and an experimental validation was performed. We performed GSEA with the aim of recognizing the potential pathway related to the individual prognostic signature.

Results

The two prognostic NRLs (AC009095.1 and AC005332.4) showed significant diversity and constituted the NRL signature. On the grounds of our signature, risk score was an independent element which was bound up with patient outcome (HR = 4.97 CI: 1.87–13.2, P = 0.001). The CESC patients were classified by the median risk score. Immune infiltration analysis revealed significant increases in CD4 + Tcm, eosinophils, epithelial cells, fibroblasts, NKT, plasma cells, platelets, and smooth muscle in the high-risk group (P&lt; 0.05). Target lncRNAs also showed some correlation with NRGs. The estimated IC50 values of bicalutamide, CHIR.99021, and imatinib were lower in the high-risk group. Through the subsequent experimental validation, both AC009095.1 and AC005332.4 were significantly more highly expressed in SiHa than in Hela. AC009095.1 was expressed more highly in SiHa than in HUCEC, but the expression of AC005332.4 was reversed.

Conclusions

This study elucidated that NRLs, as a novel signature, were indispensable factors which can significantly influence the prognosis of patients with CESC and could provide novel clinical evidence to serve as a potential molecular biomarker for future therapeutic targets.

Identification of hub genes related to CD4+ memory T cell infiltration with gene co-expression network predicts prognosis and immunotherapy effect in colon adenocarcinoma

Background: CD4+ memory T cells (CD4+ MTCs), as an important part of the microenvironment affecting tumorigenesis and progression, have rarely been systematically analyzed. Our purpose was to comprehensively analyze the effect of CD4+ MTC infiltration on the prognosis of colon adenocarcinoma (COAD). Methods: Based on RNA-Seq data, weighted gene co-expression network analysis (WGCNA) was used to screen the CD4+ MTC infiltration genes most associated with colon cancer and then identify hub genes and construct a prognostic model using the least absolute shrinkage and selection operator algorithm (LASSO). Finally, survival analysis, immune efficacy analysis, and drug sensitivity analysis were performed to evaluate the role of the prognostic model in COAD. Results: We identified 929 differentially expressed genes (DEGs) associated with CD4+ MTCs and constructed a prognosis model based on five hub genes (F2RL2, TGFB2, DTNA, S1PR5, and MPP2) to predict overall survival (OS) in COAD. Kaplan-Meier analysis showed poor prognosis in the high-risk group, and the analysis of the hub gene showed that overexpression of TGFB2, DTNA, S1PR5, or MPP2 was associated with poor prognosis. Clinical prediction nomograms combining CD4+ MTC-related DEGs and clinical features were constructed to accurately predict OS and had high clinical application value. Immune efficacy and drug sensitivity analysis provide new insights for individualized treatment. Conclusion: We constructed a prognostic risk model to predict OS in COAD and analyzed the effects of risk score on immunotherapy efficacy or drug sensitivity. These studies have important clinical significance for individualized targeted therapy and prognosis.

Triple negative breast cancer: Pitfalls and progress

Triple negative breast cancer (TNBC) is characterized by the lack of estrogen and progesterone receptor expression and lacks HER2 overexpression or gene amplification. It accounts for 10–15% of incident breast cancers and carries the worst prognosis. TNBC is overrepresented among Black and pre-menopausal women and is associated with significant psychological and treatment-related burdens, including financial toxicity. Like other breast cancers, TNBC is biologically heterogeneous, leading to diverse clinical and epidemiological behaviors, however, unlike the other clinical subtypes, in TNBC we still lack tumor-specific targeted therapy. Early TNBC outcomes have improved due to the intensification of therapies, including improvements in polychemotherapy and the addition of immunotherapy. Future efforts are needed to identify targetable aberrations for specific drug therapy, prevent immune evasion, and increase social-economic support. Given that the name TNBC illustrates its lack of specifically targeted and effective therapy, we look forward to being able to retire the name in favor of a group of targetable entities within what is now called “TNBC”.

ALKBH family members as novel biomarkers and prognostic factors in human breast cancer

Breast cancer is the most common lethal carcinoma worldwide and better targeted therapies are still worthy of exploration, having had some great successes already. Abnormal expression of ALKBH members were found in various cancers, and the roles played by it were the focus of attention. The ALKBH gene family encodes nine homologous enzymes (ALKBH1-8 and FTO) to repair DNA or RNA depending on Fe2+ and α-ketoglutarate (α-KG), which is related to carcinogenesis. In this study, we applied several databases to explore the roles of ALKBHs in breast cancer. We found that ALKBH members were abnormal expression in breast cancer and associated with tumor stage and subclasses. Higher alteration rates of ALKBH family were found in breast cancer. Function enrichment revealed that several cancer-associated signal pathways were related to ALKBH family such as PI3K-Akt signaling pathway and axon guidance. Infiltration of immune cells (Eosinophiles, NK CD56bright cells, mast cells, T helper cells and so on) were strongly related to ALKBHs. Moreover, we further found that there was strong correlation between ALKBH7 and higher age, later T stage, ER/PR positive and post-menopause of breast cancer patients, and patients with higher ALKBH7 expression had shorter overall survival (OS) and post progression survival (PPS). In conclusion, our findings may provide novel insights into ALKBH-targeted therapy for breast cancer patients, and ALKBH7 may be a potential prognostic biomarker.

The Relationship of Pyroptosis-Related Genes, Patient Outcomes, and Tumor-Infiltrating Cells in Bladder Urothelial Carcinoma (BLCA)

Introduction: The role of pyroptosis and its effects on tumor-infiltrating cells (TICs) in the pathogenesis and treatment outcomes of patients with bladder urothelial carcinoma (BLCA) remains unclear.

Methods: We conducted a bioinformatics analysis on the pyroptosis-related genes (PRGs) and TICs using data from public domains, and evaluated their impact on the pathogenesis and clinical outcomes of BLCA patients. A risk score based on PRGs and a prognostic risk model that incorporated patient demographics, tumor characteristics, and differentially expressed genes (DEGs) were developed.

Results: Twenty-three DEGs of 52 PRGs were identified in BLCA and normal samples from the TCGA database. Missense mutations and single nucleotide polymorphisms in PRGs are the most common genetic abnormalities. Patients with high PRG risk scores showed an inferior survival compared to those with low risk scores. The prognostic risk model based on patient demographics, tumor characteristics, and DEGs showed good predictive values for patient survival at 1, 3, and 5 years in BLCA patients. Caspase-8 (CASP8) was the only intersection gene of the prognostic genes, DEGs, and different genes expressed in tissue. Patients with a high CASP8 expression had improved survival, and an increased CASP8 expression level was observed in activated CD4 memory T cells, follicular T helper cells, resting NK cells, M0 macrophages, and activated dendritic cells. CASP8 expression also showed a positive correlation with the IL7R expression—a key cell marker of CD4 memory T cells. CASP8 expression also showed correlations with immune checkpoints (PDCD1, CD274, and CTLA4) and response to immune checkpoint inhibitors.

Conclusion: Our data suggest that PRGs, especially CASP8, showed strong associations with patient outcomes and TICs in BLCA. If validated, these results could potentially aid in the prognostication and guide treatment in BLCA patients.

Prognostic Model and Nomogram Construction and Validation With an Autophagy-Related Gene Signature in Low-Grade Gliomas

Background: Autophagy plays a vital role in cancer development. However, the prognostic value of autophagy-related genes (ARGs) in low-grade gliomas (LGG) is unclear. This research aimed to investigate whether ARGs correlated with overall survival (OS) in LGG patients.

Methods: RNA-sequencing data were obtained from The Cancer Genome Atlas (TCGA) TARGET GTEx database. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis of ARGs were performed by the “clusterprofile” R package. Cox regression with the wald χ² test was employed to identify prognostic significant ARGs. Next, the receiver operator characteristic curves were established to evaluate the feasibility of risk score (riskscore=h0(t)exp(∑j=1nCoefj×Xj)) and other clinical risk factors to predict prognosis. A nomogram was constructed. Correlations between clinical features and ARGs were further verified by a t-test or Kruskal–Wallis test. In addition, the correlations between autophagy and immune cells were assessed through the single-sample gene set enrichment analysis (ssGSEA) and tumor immune estimation resource database. Last, the prediction model was verified by LGG data downloaded from the Chinese Glioma Genome Atlas (CGGA) database.

Results: Overall, 35 DE-ARGs were identified. Functional enrichment analysis showed that these genes were mainly related to oxidative stress and regulation of autophagy. Nine ARGs (BAX, BIRC5, CFLAR, DIRAS3, GRID2, MAPK9, MYC, PTK6, and TP53) were significantly associated with OS. Age (Hazard ratio (HR) = 1.063, 95% CI: 1.046–1.080), grade (HR = 3.412, 95% CI: 2.164–5.379), histological type (HR = 0.556, 95% CI: 0.346–0.893), and risk score (HR = 1.135, 95% CI: 1.104–1.167) were independent prognostic risk factors (all p < 0.05). In addition, BIRC5, CFLAR, DIRAS3, TP53, and risk scores were found to correlate significantly with age and tumor grade (all p < 0.05). Immune cell enrichment analysis demonstrated that the types of immune cells and their expression levels in the high-risk group were significantly different from those in the low-risk group (all p < 0.05). A prognostic nomogram was constructed to predict 1-, 3-, and 5-year survival, and the prognostic value of sorted ARGs were verified in the CGGA database and clinical samples.

Conclusion: Our findings suggest that the 9 DE-ARGs’ risk score model could serve as diagnostic and prognostic biomarkers. The prognostic nomograms could be useful for individualized survival prediction and improved treatment strategies.

Potential Predictive and Prognostic Value of Biomarkers Related to Immune Checkpoint Inhibitor Therapy of Triple-Negative Breast Cancer

As an aggressive subtype of breast cancer, triple-negative breast cancer (TNBC) is associated with poor prognosis and lack of effective therapy, except chemotherapy. In recent years, immunotherapy based on immune checkpoint (IC) inhibition has emerged as a promising therapeutic strategy in TNBC. TNBC has more tumor-infiltrating lymphocytes (TILs) and higher rate of mutation and programmed cell death ligand-1 (PD-L1) expression than other subtypes of breast cancer have. However, previous studies have shown that monotherapy has little efficacy and only some TNBC patients can benefit from immunotherapy. Therefore, it is important to identify biomarkers that can predict the efficacy of IC inhibitors (ICIs) in TNBC. Recently, various biomarkers have been extensively explored, such as PD-L1, TILs and tumor mutational burden (TMB). Clinical trials have shown that PD-L1-positive patients with advanced TNBC benefit from ICIs plus chemotherapy. However, in patients with early TNBC receiving neoadjuvant therapy, PD-L1 cannot predict the efficacy of ICIs. These inconsistent conclusions suggest that PD-L1 is the best to date but an imperfect predictive biomarker for efficacy of ICIs. Other studies have shown that advanced TNBC patients with TMB ≥10 mutations/Mb can achieve clinical benefits from pembrolizumab. TILs also have potential predictive value in TNBC. Here, we select some biomarkers related to ICIs and discuss their potential predictive and prognostic value in TNBC. We hope these biomarkers could help to identify suitable patients and realize precision immunotherapy.

Establishment of a lncRNA-Based Prognostic Gene Signature Associated With Altered Immune Responses in HCC

Hepatocellular carcinoma (HCC) is a common malignancy with higher mortality, and means are urgently needed to improve the prognosis. T cell exclusion (TCE) plays a pivotal role in immune evasion, and lncRNAs represent a large group of tumor development and progression modulators. Using the TCGA HCC dataset (n=374), we identified 2752 differentially expressed and 702 TCE-associated lncRNAs, of which 336 were in both groups. As identified using the univariate Cox regression analysis, those associated with overall survival (OS) were subjected to the LASSO-COX regression analysis to develop a prognosis signature. The model, which consisted of 11 lncRNAs and was named 11LNCPS for 11-lncRNA prognosis signature, was validated and performed better than two previous models. In addition to OS and TCE, higher 11LNCPS scores had a significant correlation with reduced infiltrations of CD8+ T cells and dendritic cells (DCs) and decreased infiltrations of Th1, Th2, and pro B cells. As expected, these infiltration alterations were significantly associated with worse OS in HCC. Analysis of published data indicates that HCCs with higher 11LNCPS scores were transcriptomically similar to those that responded better to PDL1 inhibitor. Of the 11LNCPS lncRNAs, LINC01134 and AC116025.2 seem more crucial, as their upregulations affected more immune cell types' infiltrations and were significantly associated with TCE, worse OS, and compromised immune responses in HCC. LncRNAs in the 11LNCPS impacted many cancer-associated biological processes and signaling pathways, particularly those involved in immune function and metabolism. The 11LNCPS should be useful for predicting prognosis and immune responses in HCC.

Peripheral Blood Transcriptome in Breast Cancer Patients as a Source of Less Invasive Immune Biomarkers for Personalized Medicine, and Implications for Triple Negative Breast Cancer

Transcriptome studies of peripheral blood cells can advance our understanding of the systemic immune response to the presence of cancer and the mechanisms underlying cancer onset and progression. This enables the identification of novel minimally invasive immune biomarkers for early cancer detection and personalized cancer management and may bring forward new immunotherapy options. Recent blood gene expression analyses in breast cancer (BC) identified distinct patient subtypes that differed in the immune reaction to cancer and were distinct from the clinical BC subtypes, which are categorized based on expression of specific receptors on tumor cells. Introducing new BC subtypes based on peripheral blood gene expression profiles may be appropriate, since it may assist in BC prognosis, the identification of patients likely to benefit from immunotherapy, and treatment efficacy monitoring. Triple-negative breast cancer (TNBC) is an aggressive, heterogeneous, and difficult-to-treat disease, and identification of novel biomarkers for this BC is crucial for clinical decision-making. A few studies have reported TNBC-enriched blood transcriptional signatures, mostly related to strong inflammation and augmentation of altered immune signaling, that can differentiate TNBC from other classical BC subtypes and facilitate diagnosis. Future research is geared toward transitioning from expression signatures in unfractionated blood cells to those in immune cell subpopulations.

Cross-Platform in-silico Analyses Exploring Tumor Immune Microenvironment with Prognostic Value in Triple-Negative Breast Cancer

INTRODUCTION

Only a proportion of triple-negative breast cancer (TNBC) is immunotherapy-responsive. We hypothesized that the tumor microenvironment (TME) influences the outcomes of TNBC and investigated the relevant signaling pathways.

MATERIALS AND METHODS

Immune score (IS) and stromal score (SS) were calculated using the ESTIMATE and correlated with the overall survival (OS) in TNBC. RNA-seq data from 115 TNBC samples and 112 normal adjacent tissues were retrieved. Validations in the methylation levels in 10 TNBC and five non-TNBC cell lines were obtained. Cox model overall survival (OS) validated the derived transcription factor (TF) genes in cBioPortal breast cancer patients.

RESULTS

SS-low predicts a higher OS compared with SS-high patients (P = 0.0081 IS-high/SS-low patients had better OS (P = 0.045) than IS-low/SS-high patients. More macrophages were polarized to the M2 state in patients with IS-low/SS-high patients (P < 0.001). Moreover, CIBERSORTx showed more CD8+ cytotoxic T-cells in IS-high/SS-low patients (p = 0.0286) and more resting NK cells in the IS-low/SS-high TME (P = 0.0108). KEGG pathway analysis revealed that overexpressed genes were enriched in the IL-17 and cytokine-cytokine receptor interaction pathways. The lncRNA DRAIC, a tumor suppressor, was consistently deactivated in the 10 TNBC cell lines. On the cBioPortal platform, we validated that 13% of ER-negative, HER2-unamplified BC harbored IL17RA deep deletion and 25% harbored TRAF3IP2 amplification. On cBioPortal datasets, the nine altered TF genes derived from the X2K analysis showed significantly worse relapse-free survival in 2377 patients and OS in 4819 invasive BC patients than in the unaltered cohort.

CONCLUSION

Of note, the results of this integrated in silico study can only be generalized to approximately 17% of patients with TNBC, in which infiltrating stromal cells and immune cells play a determinant prognostic role.

Identifying the hub genes in non-small cell lung cancer by integrated bioinformatics methods and analyzing the prognostic values

BACKGROUND

Lung cancer, a malignant tumor, has the highest mortality and second most common morbidity worldwide. Non-small cell lung cancer (NSCLC) is the most common pathological subtype of lung cancer. This study aimed to identify the gene signature associated with the NSCLC prognosis using bioinformatics analysis.

MATERIALS AND METHODS

The dataset GSE103512 was utilized to construct co-expression networks using weighted gene co-expression network analysis (WGCNA). Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analyses were performed using Database for Annotation, Visualization, and Integrated Discovery. Gene set enrichment analysis was conducted to ascertain the function of the hub genes more accurately. The relationship between the hub genes and immune infiltration was investigated using a single sample gene set enrichment analysis. Hub genes were screened and validated by other datasets and online websites.

RESULTS

The results of WGCNA demonstrated that the blue module was most significantly related to tumor progression in NSCLC. Functional enrichment analysis showed that the blue module was associated with DNA replication, cell division, mitotic nuclear division, and cell cycle. A total of five hub genes (RFC5, UBE2S, CHAF1A, FANCI, and TMEM194A) were chosen to be identified and validated at transcriptional and translational levels. Receiver operating characteristic curve verified that the mRNA levels of these five genes can excellently discriminate between normal and tumor tissues. Survival analysis was also performed. Additionally, the protein levels of these five genes were also significantly different between tumor and normal tissues. Immune infiltration analysis showed that the expression levels of the hub genes had a negative correlation with the infiltration levels of many cells related to innate immune response, antigen-presenting process, humoral immune response, or T cell-mediated immune responses.

CONCLUSIONS

We identified five hub genes associated with the NSCLC tumorigenesis. NSCLC patients with higher expressions of each hub gene had a worse prognosis than those with lower expressions. Moreover, the hub genes might serve as biomarkers and therapeutic targets for precise diagnosis, target therapy, and immunotherapy of NSCLC in the future.

Characterization of Immune-Related Long Non-coding RNAs to Construct a Novel Signature and Predict the Prognosis and Immune Landscape of Soft Tissue Sarcoma

Background: Increasing evidence has demonstrated that immune-related long non-coding RNAs (irlncRNAs) are critically involved in tumor initiation and progression and associated with the prognosis of various cancers. However, their role in soft tissue sarcoma (STS) remains significantly uninvestigated.

Materials and Methods: Gene expression profiles were extracted from The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) for the identification of irlncRNAs. Univariate analysis and modified least absolute shrinkage and selection operator (LASSO) penalized regression analysis were employed to determine differently expressed irlncRNA (DEirlncRNA) pairs of prognostic value, and subsequently, a risk signature based on DEirlncRNA pairs was established. Furthermore, Kaplan–Meier analysis and the area under the receiver operating characteristic curve (AUC) were used to assess survival differences and the predictive accuracy of the risk signature, respectively. Lastly, the correlation of irlncRNAs with immune characteristics and chemosensitivity in patients with STS were further investigated.

Results: A total of 1088 irlncRNAs were identified, and 311 irlncRNAs were distinguished as DEirlncRNAs. A total of 130 DEirlncRNA pairs were further identified as prognostic markers, and 14 pairs were selected for establishing a risk signature. The irlncRNA-based risk signature functioned as an independent prognostic marker for STS. Compared with the patients in the high-risk group, those in the low-risk group exhibited a better prognosis and were more sensitive to several chemotherapeutic agents. In addition, the irlncRNA-based risk signature was significantly associated with immune scores, infiltrating immune cells, and the expression of several immune checkpoints.

Conclusion: In conclusion, our data revealed that the irlncRNA-based risk signature resulted in reliable prognosis, effectively predicted the immune landscape of patients with STS and was significantly correlated with chemosensitivity, thus providing insights into the potential role of irlncRNAs as prognostic biomarkers and novel therapeutic targets for STS.

Development and validation of a ferroptosis-related model for three digestive tract tumors based on a pan-cancer analysis

Aims: To develop a ferroptosis gene-based survival-predictor model for predicting the prognosis of patients with digestive tract tumors, a pan-caner analysis was performed. Materials & methods: Based on unsupervised clustering and the expression levels of ferroptosis genes, patients with cancer were divided into two clusters. The least absolute shrinkage and selection operator method Cox regression analysis was used to establish the survival-predictor model. Results: Based on the pan-cancer analysis, a 20 gene-based survival-predictor model for predicting survival rates was developed, which was validated in patients with hepatocellular carcinoma. Conclusion: The survival-predictor model accurately predicted the prognosis of patients with digestive tract tumors.

Identification of MAP3K15 as a potential prognostic biomarker and correlation with immune infiltrates in osteosarcoma

Background

Osteosarcoma (OS) is a type of primary malignant tumor, and increasing evidence shows the clinical benefits of immunotherapy in treating OS. However, the lack of comprehensive studies on the complex OS immune microenvironment hinders the application of immunotherapy. Thus, this study aimed to systematically explore the immune characteristics of OS and identify novel biomarkers for OS treatment.

Methods

We systematically studied the immune score and proportions of infiltrating immune cells in OS in the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) and Gene Expression Omnibus (GEO) databases using the ESTIMATE and CIBERSORT algorithms. Differential expression and functional analyses were used to identify dysregulated genes and explore their functions. Survival and Cox regression analyses were applied to establish an immune-related prognostic signature. Additionally, qPCR and immunohistochemistry were performed to validate the results.

Results

A total of 103 differentially expressed immune genes (DEIGs) were found in the TARGET-OS and GSE39058 databases, and these DEIGs were mainly enriched in leukocyte proliferation, leukocyte differentiation, osteoclast differentiation, natural killer (NK) cell-mediated cytotoxicity, and the adaptive immune system. A predictive signature was constructed based on the survival analysis, with an area under the receiver operating characteristic (ROC) curve (AUC) of 0.65. Moreover, we found that mitogen-activated protein kinase kinase kinase 15 (MAP3K15) can predict the prognosis of patients with OS and is closely related to CD4⁺ T cells and macrophages. The OS patients with high MAP3K15 expression had a significantly poorer prognosis.

Conclusions

Our study found that MAP3K15, whose expression level is closely related to immune activity in tumors, is a critical immune-related biomarker, and our findings may provide a basis for OS immunotherapy.

Identification of Somatic Mutation-Driven Immune Cells by Integrating Genomic and Transcriptome Data

Tumor somatic mutations in protein-coding regions may generate neoantigens which may trigger antitumor immune cell response. Increasing evidence supports that immune cell response may profoundly influence tumor progression. However, there are no calculated tools to systematically identify immune cells driven by specific somatic mutations. It is urgent to develop a calculated method to comprehensively detect tumor-infiltrating immune cells driven by the specific somatic mutations in cancer. We developed a novel software package (SMDIC) that enables the automated identification of somatic mutation-driven immune cell. SMDIC provides a novel pipeline to discover mutation-specific immune cells by integrating genomic and transcriptome data. The operation modes include inference of the relative abundance matrix of tumor-infiltrating immune cells, detection of differential abundance immune cells with respect to the gene mutation status, conversion of the abundance matrix of significantly dysregulated cells into two binary matrices (one for upregulated and one for downregulated cells), identification of somatic mutation-driven immune cells by comparing the gene mutation status with each immune cell in the binary matrices across all samples, and visualization of immune cell abundance of samples in different mutation status for each gene. SMDIC provides a user-friendly tool to identify somatic mutation-specific immune cell response. SMDIC may contribute to understand the mechanisms underlying anticancer immune response and find targets for cancer immunotherapy. The SMDIC was implemented as an R-based tool which was freely available from the CRAN website https://CRAN.R-project.org/package=SMDIC.

Multiplexed imaging analysis of the tumor-immune microenvironment reveals predictors of outcome in triple-negative breast cancer

Triple-negative breast cancer, the poorest-prognosis breast cancer subtype, lacks clinically approved biomarkers for patient risk stratification and treatment management. Prior literature has shown that interrogation of the tumor-immune microenvironment may be a promising approach to fill these gaps. Recently developed high-dimensional tissue imaging technology, such as multiplexed ion beam imaging, provide spatial context to protein expression in the microenvironment, allowing in-depth characterization of cellular processes. We demonstrate that profiling the functional proteins involved in cell-to-cell interactions in the microenvironment can predict recurrence and overall survival. We highlight the immunological relevance of the immunoregulatory proteins PD-1, PD-L1, IDO, and Lag3 by tying interactions involving them to recurrence and survival. Multivariate analysis reveals that our methods provide additional prognostic information compared to clinical variables. In this work, we present a computational pipeline for the examination of the tumor-immune microenvironment using multiplexed ion beam imaging that produces interpretable results, and is generalizable to other cancer types.

Immune-related biomarkers in triple-negative breast cancer

Breast cancer is a commonly diagnosed female cancer in the world. Triple-negative breast cancer (TNBC) is the most dangerous and biologically aggressive subtype in breast cancer which has a high mortality, high rates of relapse and poor prognosis, representing approximately 15-20% of breast cancers. TNBC has unique and special biological molecular characteristics and higher immunogenicity than other breast cancer types. On the basis of molecular features, TNBC is divided into different subtypes and gets various treatments. Especially, immunotherapy becomes a promising and effective treatment to TNBC. However, not all of the TNBC patients are sensitive to immunotherapy, the need of selecting the patients suitable for immunotherapy is imperative. In this review, we discussed recent discoveries about the immune-related factors of TNBC, including tumor-infiltrating lymphocytes (TILs), programmed death-ligand protein-1 (PD-L1), immune gene signatures, some other emerging biomarkers for immunotherapy effectivity and promising biomarkers for immunotherapy resistance. In addition, we summarized the features of these biomarkers contributing to predict the prognosis and effect of immunotherapy. We hope we can provide some helps or evidences to clinical immunotherapy and combined treatment for TNBC patients.

Tumor-derived exosomal circPSMA1 facilitates the tumorigenesis, metastasis, and migration in triple-negative breast cancer (TNBC) through miR-637/Akt1/β-catenin (cyclin D1) axis

Circular RNAs (circRNAs) are increasingly gaining importance and attention due to their diverse potential functions and their value as diagnostic biomarkers (disease specific). This study aims to explore the novel mechanisms by which exosome-contained circRNAs promote tumor development and metastasis in TNBC. We identified increased circRNA circPSMA1 in TNBC cells, their exosomes, and serum exosomes samples from TNBC patients. The overexpression of circPSMA1 promoted TNBC cell proliferation, migration, and metastasis both in vitro and in vivo. Moreover, we investigated the tumor-infiltrating immune cells (TICs) or stromal components in immune microenvironment (IME), and identified the significant differences in the immune cells between TNBC and non-TNBC samples. Mechanistically, circPSMA1 acted as a "miRNAs sponge" to absorb miR-637; miR-637 inhibited TNBC cell migration and metastasis by directly targeted Akt1, which recognized as a key immune-related gene and affected downstream genes β-catenin and cyclin D1. Subsequent co-culture experiments also demonstrated that exosomes from TNBC carrying large amounts of circPSMA1 could transmit migration and proliferation capacity to recipient cells. Kaplan-Meier plots showed that high expression of Akt1 and low expression of mir-637 are highly correlated with poor prognosis in patients with lymph node metastasis of TNBC. Collectively, all these results reveal that circPSMA1 functions as a tumor promoter through the circPSMA1/miR-637/Akt1-β-catenin (cyclin D1) regulatory axis, which can facilitate the tumorigenesis, metastasis, and immunosuppression of TNBC. Our research proposes a fresh perspective on novel potential biomarkers and immune treatment strategies for TNBC.

Molecular Subtypes and CD4+ Memory T Cell-Based Signature Associated With Clinical Outcomes in Gastric Cancer

Background

CD4⁺ memory T cells are an important component of the tumor microenvironment (TME) and affect tumor occurrence and progression. Nevertheless, there has been no systematic analysis of the effect of CD4⁺ memory T cells in gastric cancer (GC).

Methods

Three datasets obtained from microarray and the corresponding clinical data of GC patients were retrieved and downloaded from the Gene Expression Omnibus (GEO) database. We uploaded the normalize gene expression data with standard annotation to the CIBERSORT web portal for evaluating the proportion of immune cells in the GC samples. The WGCNA was performed to identify the modules the CD4⁺ memory T cell related module (CD4⁺ MTRM) which was most significantly associated with CD4⁺ memory T cell. Univariate Cox analysis was used to screen prognostic CD4⁺ memory T cell-related genes (CD4⁺ MTRGs) in CD4⁺ MTRM. LASSO analysis and multivariate Cox analysis were then performed to construct a prognostic gene signature whose effect was evaluated by Kaplan-Meier curves and receiver operating characteristic (ROC), Harrell’s concordance index (C-index), and decision curve analyses (DCA). A prognostic nomogram was finally established based on the CD4⁺ MTRGs.

Result

We observed that a high abundance of CD4⁺ memory T cells was associated with better survival in GC patients. CD4⁺ MTRM was used to stratify GC patients into three clusters by unsupervised clustering analysis and ten CD4⁺ MTRGs were identified. Overall survival, five immune checkpoint genes and 17 types of immunocytes were observed to be significantly different among the three clusters. A ten-CD4⁺ MTRG signature was constructed to predict GC patient prognosis. The ten-CD4⁺ MTRG signature could divide GC patients into high- and low-risk groups with distinct OS rates. Multivariate Cox analysis suggested that the ten-CD4⁺ MTRG signature was an independent risk factor in GC. A nomogram incorporating this signature and clinical variables was established, and the C-index was 0.73 (95% CI: 0.697–0.763). Calibration curves and DCA presented high credibility for the OS nomogram.

Conclusion

We identified three molecule subtypes, ten CD4⁺ MTRGs, and generated a prognostic nomogram that reliably predicts OS in GC. These findings have implications for precise prognosis prediction and individualized targeted therapy.

Long Non-coding RNA BTG3-7:1 and JUND Co-regulate C21ORF91 to Promote Triple-Negative Breast Cancer Progress

BACKGROUND

Triple-negative breast cancer (TNBC) is a type of highly invasive breast cancer with poor prognosis. Recently, massive data reveal that long non-coding RNAs (lncRNAs) play important roles in cancer progress. Recently, although the role of lncRNAs in breast cancer has been well documented, few focused on TNBC. In this study, we aimed to systematically identify functional lncRNAs and to explore its molecular mechanism on TNBC progress.

METHODS

The recurrence of lncRNAs and their target genes were validated with TNBC biopsies and cell lines. Total one hundred and thirteen TNBC biopsies, including nineteen patient-matched samples, were collected. The profile of TNBC-related lncRNAs and their target genes were characterized by RNA sequencing (RNA-seq) and bioinformatic analysis. Tumor specific lncRNAs, which also showed biological function correlated with TNBC, were identified as potential candidates; and the target genes, which regulated by the identified lncRNAs, were predicted by the analysis of expression correlation and chromosome colocalization. Cross bioinformatic validation was performed with TNBC independent datasets from the cancer genome atlas (TCGA). The biological functions and molecular mechanism were investigated in TNBC model cell lines by cell colony forming assay, flow cytometry assay, western-blot, RNA Fluorescence in situ Hybridization assay (RNA FISH) and chromatin immunoprecipitation-qPCR (ChIP-qPCR).

RESULTS

Abundant Lnc-BTG3-7:1, which targets gene C21ORF91, was specifically observed in TNBC biopsies and cell lines. Knockdown of Lnc-BTG3-7:1 or C21ORF91 strongly inhibited cell proliferation, promoted cell apoptosis and cell cycle G1-arrested. Meanwhile, investigation of molecular mechanism indicated that Lnc-BTG3-7:1, cooperated with transcription factor JUND, cis-regulated the transcription of C21ORF91 gene, and down-regulation of Lnc-BTG3-7:1/C21ORF91 suppressed GRB2-RAS-RAF-MEK-ERK and GRB2-PI3K-AKT-GSK3β-β-catenin pathways.

CONCLUSIONS

In this study, we identified a TNBC specific lncRNA Lnc-BTG3-7:1, which sustained tumor progress. Up-regulation of Lnc-BTG3-7:1 promoted the transcription of oncogene C21ORF91 and activated PI3K-AKT-GSK3β-β-catenin and MAPK pathways. Taken together, our results not only identified a biomarker for diagnosis but also provided a potential therapeutic target against TNBC.

Development of a prognostic model based on an immunogenomic landscape analysis of medulloblastoma

Medulloblastoma (MB) is one of the most common central nervous system tumors in children. At present, the vital role of immune abnormalities has been proved in tumorigenesis and progression. However, the immune mechanism in MB is still poorly understood. In the present study, 51 differentially expressed immune-related genes (DE-IRGs) and 226 survival associated immune-related genes (Sur-IRGs) were screened by an integrated analysis of multi-array. Moreover, the potential pathways were enriched by functional analysis, such as ‘cytokine–cytokine receptor interaction’, ‘Ras signaling pathway’, ‘PI3K-Akt signaling pathway’ and ‘pathways in cancer’. Furthermore, 10 core IRGs were identified from DE-IRGs and Sur-IRGs. And the potential regulatory mechanisms of core IRGs were also explored. Additionally, a new prognostic model, including 7 genes (HDGF, CSK, PNOC, S100A13, RORB, FPR1, and ICAM2) based on IRGs, was established by multivariable COX analysis. In summary, our study revealed the underlying immune mechanism of MB. Moreover, we developed a prognostic model associated with clinical characteristics and could reflect the infiltration of immune cells.

Identification of Tumor Microenvironment-Related Prognostic Biomarkers in Luminal Breast Cancer

Background: The tumor microenvironment (TME) has been reported to have significant value in the diagnosis and prognosis of cancers. This study aimed to identify key biomarkers in the TME of luminal breast cancer (BC).

Methods: We obtained immune scores (ISs) and stromal scores (SSs) for The Cancer Genome Atlas (TCGA) luminal BC cohort from the online ESTIMATE (Estimation of STromal and Immune cells in MAlignant Tumor tissues using Expression data) portal. The relationships between ISs and SSs and the overall survival of luminal BC patients were assessed by the Kaplan-Meier method. The differentially expressed messenger RNAs (DEmRNAs) related to the ISs and SSs were subjected to functional enrichment analysis. Additionally, a competing endogenous RNA (ceRNA) network was constructed with differentially expressed microRNAs (DEmiRNAs) and long noncoding RNAs (DElncRNAs). Furthermore, a protein–protein interaction (PPI) network was established to analyze the DEmRNAs in the ceRNA network. Then, survival analysis of biomarkers involved in the ceRNA network was carried out to explore their prognostic value. Finally, these biomarkers were validated using the luminal BC dataset from the Gene Expression Omnibus (GEO) database.

Results: The results showed that ISs were significantly associated with longer survival times of luminal BC patients. Functional enrichment analysis showed that the DEmRNAs were mainly associated with immune response, antigen binding, and the extracellular region. In the PPI network, the top 10 DEmRNAs were identified as hub genes that affected the TME of luminal BC. Finally, two DEmiRNAs, two DElncRNAs, and 17 DEmRNAs of the ceRNA network associated with the TME were shown to have prognostic value. Subsequently, the expression of 15 prognostic biomarkers was validated in one additional dataset (GSE81002). In particular, one lncRNA (GVINP1) and five mRNAs (CCDC69, DOCK2, IKZF1, JCHAIN, and NCKAP1L) were novel biomarkers.

Conclusions: Our studies demonstrated that ISs were associated with the survival of luminal BC patients, and a set of novel biomarkers that might play a prognostic role in the TME of luminal BC was identified.

M1 Macrophage and M1/M2 ratio defined by transcriptomic signatures resemble only part of their conventional clinical characteristics in breast cancer

Tumor associated macrophages (TAMs) play a critical role in biology of various cancers, including breast cancer. In the current study, we defined "M1" macrophage and "M1"/"M2" ratio by transcriptomic signatures using xCell. We investigated the association between high level of "M1" macrophage or "M1"/"M2" ratio and the tumor immune microenvironment by analyzing the transcriptome of publicly available cohorts, TCGA and METABRIC. We found that "M1" high tumors were not associated with prolonged survival compared with "M1" low tumors, or with the response to neoadjuvant chemotherapy. "M1" high tumors were associated with clinically aggressive features and "M1" high tumors enriched the cell proliferation and cell cycle related gene sets in GSEA. At the same time, "M1" high tumors were associated with high immune activity and favorable tumor immune microenvironment, as well as high expression of immune check point molecules. Strikingly, all these results were mirrored in "M1"/"M2" ratio high tumors. In conclusion, transcriptomically defined "M1" or "M1"/"M2" high tumors were associated with aggressive cancer biology and favorable tumor immune microenvironment but not with survival benefit, which resembled only part of their conventional clinical characteristics.

Transcriptome-Based Network Analysis Unveils Eight Immune-Related Genes as Molecular Signatures in the Immunomodulatory Subtype of Triple-Negative Breast Cancer

Objective: Triple-negative breast cancer (TNBC) is a high heterogeneity cancer. The identification of genomic aberrations that drive each of the TNBC subtypes may predict the prognosis of patients with TNBC and provide novel therapeutic strategies in clinical practice. This study focuses on the transcriptome-based gene expression of TNBC and aims to generate comprehensive gene co-expression networks correlated with the immune-related subtype of TNBC.

Methods: The transcriptome profiles of 107 female patients with TNBC were analyzed. Weighted gene co-expression network analysis (WGCNA) was applied to construct related networks and to sort hub-genes associated with the survival of TNBC patients. The data of the transcriptional expression, genomic alteration, survival status, and tumor immune microenvironment, which associated with hub-genes, were extracted, retrieved, and analyzed from Oncomine, UALCAN, TCGA, starBase, Kaplan–Meier Plotter, cBioPortal, and TIMER databases.

Results: Immune-related hub-genes, including BIRC3, BTN3A1, CSF2RB, GIMAP7, GZMB, HCLS1, LCP2, and SELL, were found to be associated with clinical features of TNBC evaluated by WGCNA. These hub-genes belonged to the immunomodulatory subtype of TNBC and were upregulated in the TNBC cells. The protein expression of eight immune-related hub-genes was further confirmed to be upregulated in TNBC/CD8+ tissues detected by immunohistochemical staining. Survival analysis revealed that overexpression of eight immune-related hub-genes was in favor of the survival of patients with TNBC. Moreover, a positive correlation between eight immune-related hub-genes and immune cell infiltration was observed in TNBC patients. Furthermore, checkpoint inhibitor genes such as PD-L1, PD-1, and CTLA4 were more enrichment in the immunomodulatory subtype of TNBC and the expression of PD-L1, PD-1, and CTLA4 was positively correlated with eight immune-related hub-genes in the breast cancer dataset of TCGA.

Conclusions: Eight immune-related hub-genes were identified to be molecular signatures in the immunomodulatory subtype of TNBC, which may provide therapeutic targets for the treatment of patients with breast cancer.

CTLA-4 immunotherapy exposes differences in immune response along with different tumor progression in colorectal cancer

Tumor growth is accompanied by a changing tumor microenvironment and mutations that increase the resistance to therapy. Here, we used syngeneic models to evaluate the drug response of tumors of the same type of different sizes. We used the in vivo efficacy and Ki-67 immunohistochemistry (IHC) assay to assess the difference in responses in response to treatment with the same concentration of anti-CTLA-4. Flow cytometry analysis revealed changes in the immune subpopulations changes the spleen, peripheral blood, lymph node, and tumor tissue across different tumor growth phases. For example, naive CD4+T, CD4+TCM, CD8+TEM, T, B, Treg, CD8+TCM exhibited different percentages depending on the specific immune organ. To further expose the changes in the immune microenvironment, the level of expression of PD-1 and CTLA-4 showed statistically significant difference in related subsets for each four immune tissues in different tumor sizes. In addition, the ratios of CD4 + Teff/ CD4 + Treg and CD8 + T/Treg in corresponding immune tissue were also associated with statistically significant differences alongside tumor growth in different animal models. These results reveal the ongoing changes in the immune microenvironment during tumor progression and anti-CTLA-4 antibody immunotherapy effect depends on the expression level of immune factors.

Cellular heterogeneity map of diverse immune and stromal phenotypes within breast tumor microenvironment

Background

Cellular heterogeneity within the tumor microenvironment is essential to tumorigenesis and tumor development. A high-resolution global view of the tumor-infiltrating immune and stromal cells in breast tumors is needed.

Methods

xCell was used to create a cellular heterogeneity map of 64 cell types in 1,092 breast tumor and adjacent normal tissues. xCell digitally dissects tissue cellular heterogeneity based on gene expression. Integrated statistical analyses were then performed.

Results

There were noticeable differences between the cell fractions in tumor tissues and normal tissues. Tumors displayed higher proportions of immune cells, including CD4+ Tem, CD8+ naïve T cells, and CD8+ Tcm compared with normal tissues. Immune inhibitory receptors (PD1, CTLA4, LAG3 and TIM3) were co-expressed on certain subtypes of T cells in breast tumors, and PD1 and CTLA4 were both positively correlated with CD8+ Tcm and CD8+ T cells. 28 cell types were significantly associated with overall survival in univariate analysis. CD4+ Tem, CD8+ Tcm, CD8+ T-cells, CD8+ naive T-cells, and B cells were positive prognostic factors but CD4+ naive T-cells were negative prognostic factors for breast cancer patients. TDRD6 and TTK are promising T cell and B cell targets for tumor vaccines. Endothelial cells and fibroblasts were significantly less prevalent in tumor tissues; astrocytes and mesangial cells were negatively correlated with the T stage. Mesangial cells and keratinocytes were found to be favorable prognostic factors and myocytes were negative prognostic factors. Five cell types were found to be independent prognostic factors and we used these to create a reliable prognostic model for breast cancer patients. Cellular heterogeneity was discovered among different breast cancer subtypes by Her2, ER, and PR status. Tri-negative patients had the highest fraction of immune cells while luminal type patients had the lowest. The various cells may have diverse or opposing roles in the prognosis of breast cancer patients.

Conclusions

We created a uniquecellular map for the diverse heterogeneity of immune and stromal phenotypes within the breast tumor microenvironment. This map may lead to potential therapeutic targets and biomarkers with prognostic utility.

Immune profiling reveals prognostic genes in high-grade serous ovarian cancer

High-grade serous ovarian cancer (HGSOC) is a heterogeneous disease with diverse clinical outcomes, highlighting a need for prognostic biomarker identification. Here, we combined tumor microenvironment (TME) scores with HGSOC characteristics to identify immune-related prognostic genes through analysis of gene expression profiles and clinical patient data from The Cancer Genome Atlas and the International Cancer Genome Consortium public cohorts. We found that high TME scores (TMEscores) based on the fractions of immune cell types correlated with better overall survival. Furthermore, differential expression analysis revealed 329 differentially expressed genes between patients with high vs. low TMEscores. Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analyses showed that these genes participated mainly in immune-related functions and, among them, 48 TME-related genes predicted overall survival in HGSOC. Seven of those genes were associated with prognosis in an independent HGSOC database. Finally, the two genes with the lowest p-values in the prognostic analysis (GBP1, ETV7) were verified through in vitro experiments. These findings reveal specific TME-related genes that could serve as effective prognostic biomarkers for HGSOC.

Loss of ELF5-FBXW7 stabilizes IFNGR1 to promote the growth and metastasis of triple-negative breast cancer through interferon-γ signalling

Triple-negative breast cancer (TNBC) is characterized by a high degree of immune infiltrate in the tumour microenvironment, which may influence the fate of TNBC cells. We reveal that loss of the tumour suppressive transcription factor Elf5 in TNBC cells activates intrinsic interferon-γ (IFN-γ) signalling, promoting tumour progression and metastasis. Mechanistically, we find that loss of the Elf5-regulated ubiquitin ligase FBXW7 ensures stabilization of its putative protein substrate IFN-γ receptor 1 (IFNGR1) at the protein level in TNBC. Elf5low tumours show enhanced IFN-γ signalling accompanied by an increase of immunosuppressive neutrophils within the tumour microenvironment and increased programmed death ligand 1 expression. Inactivation of either programmed death ligand 1 or IFNGR1 elicited a robust anti-tumour and/or anti-metastatic effect. A positive correlation between ELF5 and FBXW7 expression and a negative correlation between ELF5, FBXW7 and IFNGR1 expression in the tumours of patients with TNBC strongly suggest that this signalling axis could be exploited for patient stratification and immunotherapeutic treatment strategies for Elf5low patients with TNBC.

Activating miRNA-mRNA network in gemcitabine-resistant pancreatic cancer cell associates with alteration of memory CD4+ T cells

Background

To identify key microRNAs (miRNAs) and their target mRNAs related to gemcitabine-resistant pancreatic cancer (PC) and investigate the association between gemcitabine-resistant-related miRNAs and mRNAs and immune infiltration.

Methods

Expression profiles of miRNAs and mRNAs were obtained from the Gene Expression Omnibus (GEO) database. The differentially expressed miRNAs and mRNAs (referred to as "DEmiRNAs" and "DEmRNAs", respectively) were distinguished between gemcitabine-resistant PC cells and its parental cells. The DEmRNAs targeted by the DEmiRNAs were retrieved using miRDB, microT, and Targetscan. Furthermore, GO and KEGG pathway enrichment analysis and GSEA were performed. The Kaplan-Meier plotter was used to analyze the prognosis of key DEmiRNAs and DEmRNAs on PC patients. The relationship between the key DEmRNAs and tumor-infiltrating immune cells in PC was investigated using CIBERSORT method using the LM22 signature as reference. Key infiltrating immune cells were further analyzed for the associations with prognosis of TCGA PAAD patients.

Results

Four DEmiRNAs, including hsa-miR-3178, hsa-miR-485-3p, hsa-miR-574-5p, and hsa-miR-584-5p, were identified to target seven DEmRNAs, including MSI2, TEAD1, GNPDA1, RND3, PRKACB, TRIM68, and YKT6, individually, in gemcitabine-resistant PC cells versus parental cells. Gemcitabine-resistant PC cells were enriched in proteasome-related, immune-related, and memory CD4⁺ T cell-related pathways, indicating a gemcitabine therapeutic effect on PC cells. All four DEmiRNAs and almost all DEmRNAs had an impact on the prognosis of PC patients. All seven DEmRNAs had remarkable effects on CD4⁺ memory T cells, which were affected by the gemcitabine therapeutic effect. Effector memory CD4⁺ T cells rather than central memory CD4⁺ T cells predicted a good prognosis according to the TCGA PAAD dataset.

Conclusions

Gemcitabine resistance can alter the fraction of memory CD4⁺ T cells via hsa-miR-3178, hsa-miR-485-3p, hsa-miR-574-5p and hsa-miR-584-5p targeted MSI2, TEAD1, GNPDA1, RND3, PRKACB, TRIM68, and YKT6 network in PC.