A 4-gene-based hypoxia signature is associated with tumor immune microenvironment and predicts the prognosis of pancreatic cancer patients

A Novel Pancreatic Cancer Hypoxia Status Related Gene Signature for Prognosis and Therapeutic Responses

Pancreatic cancer (PAC) is a highly fatal and aggressive type of cancer. Hypoxia is a common feature of PAC. The aim of this study was to develop a hypoxia status-related prognostic model for predicting the survival outcomes in PAC. The data sets of PAC from The Cancer Genome Atlas and the International Cancer Genome Consortium were used to construct and validate the signature. A 6 hypoxia status-related differential expression genes prognostic model for predicting the survival outcomes was established. The Kaplan-Meier analysis and Received operating characteristic curve indicated the good performance of the signature at predicting overall survival. Univariate and Multivariate Cox regression revealed that the signature was an independent prognostic factor in PAC. Weighted Gene Co-expression Network Analysis and immune infiltration analysis indicated that Immune-related pathways and immune cell infiltration was mostly enriched in the low-risk group, which presented a better prognosis. We also evaluated the predictive of the signature for immunotherapy and chemoradiotherapy. Risk gene LY6D may be a potential prognostic predictor of PAC. This model can be used as an independent prognostic factor for predicting clinical outcomes and a possible classifier for response to chemotherapy.

High hypoxia status in pancreatic cancer is associated with multiple hallmarks of an immunosuppressive tumor microenvironment

Introduction

Pancreatic ductal adenocarcinoma (PDAC), the most common form of pancreatic cancer, is a particularly lethal disease that is often diagnosed late and is refractory to most forms of treatment. Tumour hypoxia is a key hallmark of PDAC and is purported to contribute to multiple facets of disease progression such as treatment resistance, increased invasiveness, metabolic reprogramming, and immunosuppression.

Methods

We used the Buffa gene signature as a hypoxia score to profile transcriptomics datasets from PDAC cases. We performed cell-type deconvolution and gene expression profiling approaches to compare the immunological phenotypes of cases with low and high hypoxia scores. We further supported our findings by qPCR analyses in PDAC cell lines cultured in hypoxic conditions.

Results

First, we demonstrated that this hypoxia score is associated with increased tumour grade and reduced survival suggesting that this score is correlated to disease progression. Subsequently, we compared the immune phenotypes of cases with high versus low hypoxia score expression (HypoxiaHI vs. HypoxiaLOW) to show that high hypoxia is associated with reduced levels of T cells, NK cells and dendritic cells (DC), including the crucial cDC1 subset. Concomitantly, immune-related gene expression profiling revealed that compared to HypoxiaLOW tumours, mRNA levels for multiple immunosuppressive molecules were notably elevated in HypoxiaHI cases. Using a Random Forest machine learning approach for variable selection, we identified LGALS3 (Galectin-3) as the top gene associated with high hypoxia status and confirmed its expression in hypoxic PDAC cell lines.

Discussion

In summary, we demonstrated novel associations between hypoxia and multiple immunosuppressive mediators in PDAC, highlighting avenues for improving PDAC immunotherapy by targeting these immune molecules in combination with hypoxia-targeted drugs.

Hypoxia induces immunosuppression, metastasis and drug resistance in pancreatic cancers

Pancreatic cancer is one of the common malignant tumors of the digestive system and is known as the "king of cancers". It is extremely difficult to diagnose at an early stage, the disease progresses rapidly, and the effect of chemotherapy and radiotherapy is poor, so the prognosis of pancreatic cancer patients is very poor. Numerous studies have suggested that hypoxia is closely related to the development and progression of pancreatic cancer. Inadequate blood supply and desmoplasia in the microenvironment of pancreatic cancer can result in its extreme hypoxia. This hypoxic microenvironment can further contribute to angiogenesis and desmoplasia. Hypoxia is mediated by the complex hypoxia inducible factor (HIF) signaling pathway and plays an important role in the formation of a highly immunosuppressive microenvironment and the metastasis of pancreatic cancer. Further work on the hypoxic microenvironment will help clarify the specific mechanisms of the role of hypoxia in pancreatic cancer and provide a basis for the realization of hypoxia-targeted therapeutic and diagnostic strategies.

Novel prognostic gene signature for pancreatic ductal adenocarcinoma based on hypoxia

BACKGROUND

Currently, there is lack of marker to accurately assess the prognosis of patients diagnosed with pancreatic ductal adenocarcinoma (PDAC). This study aims to establish a hypoxia-related risk scoring model that can effectively predict the prognosis and chemotherapy outcomes of PDAC patients.

METHODS

Using unsupervised consensus clustering algorithms, we comprehensively analyzed The Cancer Genome Atlas (TCGA) data to identify two distinct hypoxia clusters and used the weighted gene co-expression network analysis (WGCNA) to examine gene sets significantly associated with these hypoxia clusters. Then univariate Cox regression, the least absolute shrinkage and selection operator (LASSO) Cox regression and multivariate Cox regression were used to construct a signature and its efficacy was evaluated using the International Cancer Genome Consortium (ICGC) PDAC cohort. Further, the correlation between the risk scores obtained from the signature and carious clinical, pathological, immunophenotype, and immunoinfiltration factors as well as the differences in immunotherapy potential and response to common chemotherapy drugs between high-risk and low-risk groups were evaluated.

RESULTS

From a total of 8 significantly related modules and 4423 genes, 5 hypoxia-related signature genes were identified to construct a risk model. Further analysis revealed that the overall survival rate (OS) of patients in the low-risk group was significantly higher than the high-risk group. Univariate and multivariate Cox regression analysis showed that the risk scoring signature was an independent factor for prognosis prediction. Analysis of immunocyte infiltration and immunophenotype showed that the immune score and the anticancer immune response in the high-risk were significantly lower than that in the low-risk group.

CONCLUSION

The constructed hypoxia-associated prognostic signature demonstrated could be used as a potential risk classifier for PDAC.

Polarization of Cancer-Associated Macrophages Maneuver Neoplastic Attributes of Pancreatic Ductal Adenocarcinoma

Mounting evidence links the phenomenon of enhanced recruitment of tumor-associated macrophages towards cancer bulks to neoplastic growth, invasion, metastasis, immune escape, matrix remodeling, and therapeutic resistance. In the context of cancer progression, naïve macrophages are polarized into M1 or M2 subtypes according to their differentiation status, gene signatures, and functional roles. While the former render proinflammatory and anticancer effects, the latter subpopulation elicits an opposite impact on pancreatic ductal adenocarcinoma. M2 macrophages have gained increasing attention as they are largely responsible for molding an immune-suppressive landscape. Through positive feedback circuits involving a paracrine manner, M2 macrophages can be amplified by and synergized with neighboring neoplastic cells, fibroblasts, endothelial cells, and non-cell autonomous constituents in the microenvironmental niche to promote an advanced disease state. This review delineates the molecular cues expanding M2 populations that subsequently convey notorious clinical outcomes. Future therapeutic regimens shall comprise protocols attempting to abolish environmental niches favoring M2 polarization; weaken cancer growth typically assisted by M2; promote the recruitment of tumoricidal CD8⁺ T lymphocytes and dendritic cells; and boost susceptibility towards gemcitabine as well as other chemotherapeutic agents.

Glioblastoma glycolytic signature predicts unfavorable prognosis, immunological heterogeneity, and ENO1 promotes microglia M2 polarization and cancer cell malignancy

Glioblastomas are the most malignant brain tumors, whose progress was promoted by aberrate aerobic glycolysis. The immune environment was highly engaged in glioblastoma formation, while its interaction with aerobic glycolysis remained unclear. Herein, we build a 7-gene Glycolytic Score (GS) by Elastic Net in the training set and two independent validating sets. The GS predicted malignant features and poor survival with good performances. Immune functional analyses and Cibersort calculation identified depressed T cells, B cells, natural killer cells immunity, and high immunosuppressive cell infiltration in the high-GS group. Also, high expressions of the immune-escape genes were discovered. Subsequently, the single-cell analyses validated the glycolysis-related immunosuppression. The functional results manifested the high-GS neoplastic cells' association with T cells, NK cells, and macrophage function regulation. The intercellular cross-talk showed strong associations between high-GS neoplastic cells and M2 macrophages/microglia in several immunological pathways. We finally confirmed that ENO1, the key gene of the GS, promoted M2 microglia polarization and glioblastoma cell malignant behaviors via immunofluorescence, clone formation, CCK8, and transwell rescue experiments. These results indicated the interactions between cancerous glycolysis and immunosuppression and glycolysis' role in promoting glioblastoma progression. Conclusively, we built a robust model and discovered strong interaction between GS and immune, shedding light on prognosis management improvement and therapeutic strategies development for glioblastoma patients.

Hypoxia, a Targetable Culprit to Counter Pancreatic Cancer Resistance to Therapy

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, and it is a disease of dismal prognosis. While immunotherapy has revolutionized the treatment of various solid tumors, it has achieved little success in PDAC. Hypoxia within the stroma-rich tumor microenvironment is associated with resistance to therapies and promotes angiogenesis, giving rise to a chaotic and leaky vasculature that is inefficient at shuttling oxygen and nutrients. Hypoxia and its downstream effectors have been implicated in immune resistance and could be contributing to the lack of response to immunotherapy experienced by patients with PDAC. Paradoxically, increasing evidence has shown hypoxia to augment genomic instability and mutagenesis in cancer, suggesting that hypoxic tumor cells could have increased production of neoantigens that can potentially enable their clearance by cytotoxic immune cells. Strategies aimed at relieving this condition have been on the rise, and one such approach opts for normalizing the tumor vasculature to reverse hypoxia and its downstream support of tumor pathogenesis. An important consideration for the successful implementation of such strategies in the clinic is that not all PDACs are equally hypoxic, therefore hypoxia-detection approaches should be integrated to enable optimal patient selection for achieving improved patient outcomes.

The prognosis of bladder cancer is affected by fatty acid metabolism, inflammation, and hypoxia

Background

The prognosis of bladder cancer (BC) is poor, and there is no effective personalized management method for BC patients at present. Developing an accurate model is helpful to make treatment plan and prognosis analysis for BC patients. Endogenous fatty acid metabolism causes cancer cells to become hypoxic, and the coexistence of hypoxia and inflammation is often characteristic of cancer. All three together influence the tumor immune microenvironment, treatment, and prognosis of BC.

Methods

We used The Cancer Genome Atlas-Bladder Urothelial Carcinoma (TCGA-BLAC) cohorts as a train group to build a risk model based on fatty acid metabolism, hypoxia and inflammation-related gene signatures and performed external validation with GSE13507, GSE31684, and GSE39281 cohorts. We validated the model to correlate with the clinicopathological characteristics of patients, created an accuracy nomogram, and explored the differences in immune microenvironment and enrichment pathways.

Results

We found significant differences in overall survival and progression-free survival between high- and low-risk groups, and patients in the low-risk group had a better prognosis than those in the high-risk group. In the train group, the AUCs for predicting overall survival at 1, 3, and 5 years were 0.745, 0.712, and 0.729, respectively. The 1-, 3-, and 5-year overall survival AUCs were 0.589, 0.672, and 0.666 in the external validation group, respectively. The risk score independently predicted the prognosis of BC patients with AUCs of 0.729. In addition, there was a significant correlation between risk scores and BC clinicopathological features and, in the GSE13507 cohort, we observed that BC progression and deeper invasion were associated with higher risk scores. Risk scores were highly correlated with coproptosis, pyroptosis, m7G, immune checkpoint-related genes, and immune microenvironment. In addition, we found that patients in the low-risk group responded better to immunotherapy, whereas patients in the high-risk group were more sensitive to commonly used chemotherapy drugs.

Conclusion

Our findings provide new treatment decisions for BC, and can effectively predict the prognosis of BC patients, which is helpful for the management of BC patients.

Determination of a DNA repair-related gene signature with potential implications for prognosis and therapeutic response in pancreatic adenocarcinoma

Background

Pancreatic adenocarcinoma (PAAD) is one of the leading causes of cancer death worldwide. Alterations in DNA repair-related genes (DRGs) are observed in a variety of cancers and have been shown to affect the development and treatment of cancers. The aim of this study was to develop a DRG-related signature for predicting prognosis and therapeutic response in PAAD.

Methods

We constructed a DRG signature using least absolute shrinkage and selection operator (LASSO) Cox regression analysis in the TCGA training set. GEO datasets were used as the validation set. A predictive nomogram was constructed based on multivariate Cox regression. Calibration curve and decision curve analysis (DCA) were applied to validate the performance of the nomogram. The CIBERSORT and ssGSEA algorithms were utilized to explore the relationship between the prognostic signature and immune cell infiltration. The pRRophetic algorithm was used to estimate sensitivity to chemotherapeutic agents. The CellMiner database and PAAD cell lines were used to investigate the relationship between DRG expression and therapeutic response.

Results

We developed a DRG signature consisting of three DRGs (RECQL, POLQ, and RAD17) that can predict prognosis in PAAD patients. A prognostic nomogram combining the risk score and clinical factors was developed for prognostic prediction. The DCA curve and the calibration curve demonstrated that the nomogram has a higher net benefit than the risk score and TNM staging system. Immune infiltration analysis demonstrated that the risk score was positively correlated with the proportions of activated NK cells and monocytes. Drug sensitivity analysis indicated that the signature has potential predictive value for chemotherapy. Analyses utilizing the CellMiner database showed that RAD17 expression is correlated with oxaliplatin. The dynamic changes in three DRGs in response to oxaliplatin were examined by RT-qPCR, and the results show that RAD17 is upregulated in response to oxaliplatin in PAAD cell lines.

Conclusion

We constructed and validated a novel DRG signature for prediction of the prognosis and drug sensitivity of patients with PAAD. Our study provides a theoretical basis for further unraveling the molecular pathogenesis of PAAD and helps clinicians tailor systemic therapies within the framework of individualized treatment.

Heterogeneous cancer-associated fibroblasts: A new perspective for understanding immunosuppression in pancreatic cancer

Immunotherapy has shown promising efficacy in the treatment of a wide range of cancers; however, it has had little effect on pancreatic cancer. Cancer-associated fibroblasts (CAFs), the predominant mesenchymal cells present in the pancreatic cancer microenvironment, are powerful supporters of the malignant progression of pancreatic cancer. CAFs can modify the microenvironment, establish a refuge to aid cancer cells in immune escape by secreting large amounts of extracellular matrix, and produce soluble cytokines and exosomal vesicles. Hence, CAFs are important contributors to the failure of immunotherapy. Current in-depth studies of CAFs have shown that CAFs are a heterogeneous population of mesenchymal cells; therefore, the functional complexity of their populations needs in-depth explorations in future studies. This review summarizes how heterogeneous CAFs help cancer cells achieve immune escape and suggests potential directions for using CAFs as targets to address immune escape.

Development and validation of a hypoxia-stemness-based prognostic signature in pancreatic adenocarcinoma

Background: Pancreatic adenocarcinoma (PAAD) is one of the most aggressive and fatal gastrointestinal malignancies with high morbidity and mortality worldwide. Accumulating evidence has revealed the clinical significance of the interaction between the hypoxic microenvironment and cancer stemness in pancreatic cancer progression and therapies. This study aims to identify a hypoxia-stemness index-related gene signature for risk stratification and prognosis prediction in PAAD.

Methods: The mRNA expression-based stemness index (mRNAsi) data of PAAD samples from The Cancer Genome Atlas (TCGA) database were calculated based on the one-class logistic regression (OCLR) machine learning algorithm. Univariate Cox regression and LASSO regression analyses were then performed to establish a hypoxia-mRNAsi-related gene signature, and its prognostic performance was verified in both the TCGA-PAAD and GSE62452 corhorts by Kaplan-Meier and receiver operating characteristic (ROC) analyses. Additionally, we further validated the expression levels of signature genes using the TCGA, GTEx and HPA databases as well as qPCR experiments. Moreover, we constructed a prognostic nomogram incorporating the eight-gene signature and traditional clinical factors and analyzed the correlations of the risk score with immune infiltrates and immune checkpoint genes.

Results: The mRNAsi values of PAAD samples were significantly higher than those of normal samples (p < 0.001), and PAAD patients with high mRNAsi values exhibited worse overall survival (OS). A novel prognostic risk model was successfully constructed based on the eight-gene signature comprising JMJD6, NDST1, ENO3, LDHA, TES, ANKZF1, CITED, and SIAH2, which could accurately predict the 1-, 3-, and 5-year OS of PAAD patients in both the training and external validation datasets. Additionally, the eight-gene signature could distinguish PAAD samples from normal samples and stratify PAAD patients into low- and high-risk groups with distinct OS. The risk score was closely correlated with immune cell infiltration patterns and immune checkpoint molecules. Moreover, calibration analysis showed the excellent predictive ability of the nomogram incorporating the eight-gene signature and traditional clinical factors.

Conclusion: We developed a hypoxia-stemness-related prognostic signature that reliably predicts the OS of PAAD. Our findings may aid in the risk stratification and individual treatment of PAAD patients.

An Inflammatory Response-Related Gene Signature Reveals Distinct Survival Outcome and Tumor Microenvironment Characterization in Pancreatic Cancer

Background: Desmoplasia or rich fibrotic stroma is a typical property of pancreatic cancer (PC), with a significant impact on tumor progression, metastasis, and chemotherapy response. Unusual inflammatory responses are considered to induce fibrosis of tissue, but the expression and clinical significance of inflammatory response-related genes in PC have not been clearly elucidated. Methods: Prognosis-related differentially expressed genes (DEGs) between tumor and normal tissues were identified by comparing the transcriptome data of PC samples based on The Cancer Genome Atlas (TCGA) portal and the Genotype Tissue Expression (GTEx) databases. Samples from the ArrayExpress database were used as an external validation cohort. Results: A total of 27 inflammatory response-related DEGs in PC were identified. Least absolute shrinkage and selection operator (LASSO) analysis revealed three core genes that served as an inflammatory response gene signature (IRGS), and a risk score was calculated. The diagnostic accuracy of the IRGS was validated in the training (n = 176) and validation (n = 288) cohorts, which reliably predicted the overall survival (OS) and disease-free survival (DFS) of patients with PC. Furthermore, multivariate analysis identified the risk score as an independent risk factor for OS and DFS. The comprehensive results suggested that a high IRGS score was correlated with decreased CD8⁺ T-cell infiltration, increased M2 macrophage infiltration, increased occurrence of stroma-activated molecular subtype and hypoxia, enriched myofibroblast-related signaling pathways, and greater benefit from gemcitabine. Conclusion: The IRGS was able to promisingly distinguish the prognosis, the tumor microenvironment characteristics, and the benefit from chemotherapy for PC.

Hypoxia-Related Signature Is a Prognostic Biomarker of Pancreatic Cancer

Background

Hypoxia plays a significant role in the pathogenesis of pancreatic cancer, but the effect of hypoxia-related genes in pancreatic cancer remains to be elucidated. This study aimed to identify hypoxia-related genes related to pancreatic cancer and construct a prognostic signature.

Methods

Pancreatic cancer datasets were retrieved from TCGA database. Cox regression analyses were used to identify hypoxia-related genes and construct a prognostic signature. Datasets from International Cancer Genome Consortium and GEO databases were used as validated cohorts. The CIBERSORT method was applied to estimate the fractions of immune cell types. DNA methylation and protein levels of the genes in pancreatic cancer were examined.

Results

Three hypoxia-related genes (TES, LDHA, and ANXA2) were identified as associated with patient survival and selected to construct a prognostic signature. Patients were divided into high- and low-risk groups based on the signature. Those in the high-risk group showed worse survival than those in the low-risk group. The signature was shown to be involved in the HIF-1 signaling pathway. The time-dependent ROC analyses of three independent validated cohorts further revealed that this signature had a better prognostic value in the prediction of the survival of pancreatic cancer patients. Immune cells analysis for three datasets demonstrated that high-risk signature was significantly associated with macrophages and T cells. DNA methylation and protein levels of the three genes validated their aberrant expression in pancreatic cancer.

Conclusions

Our research provided a novel and reliable prognostic signature that composes of three hypoxia-related genes to estimate the prognosis of pancreatic cancer.

The Effect of Hypoxia and Hypoxia-Associated Pathways in the Regulation of Antitumor Response: Friends or Foes?

Hypoxia is an environmental stressor that is instigated by low oxygen availability. It fuels the progression of solid tumors by driving tumor plasticity, heterogeneity, stemness and genomic instability. Hypoxia metabolically reprograms the tumor microenvironment (TME), adding insult to injury to the acidic, nutrient deprived and poorly vascularized conditions that act to dampen immune cell function. Through its impact on key cancer hallmarks and by creating a physical barrier conducive to tumor survival, hypoxia modulates tumor cell escape from the mounted immune response. The tumor cell-immune cell crosstalk in the context of a hypoxic TME tips the balance towards a cold and immunosuppressed microenvironment that is resistant to immune checkpoint inhibitors (ICI). Nonetheless, evidence is emerging that could make hypoxia an asset for improving response to ICI. Tackling the tumor immune contexture has taken on an in silico, digitalized approach with an increasing number of studies applying bioinformatics to deconvolute the cellular and non-cellular elements of the TME. Such approaches have additionally been combined with signature-based proxies of hypoxia to further dissect the turbulent hypoxia-immune relationship. In this review we will be highlighting the mechanisms by which hypoxia impacts immune cell functions and how that could translate to predicting response to immunotherapy in an era of machine learning and computational biology.

An Inflammatory Response Related Gene Signature Associated with Survival Outcome and Gemcitabine Response in Patients with Pancreatic Ductal Adenocarcinoma

Background: Pancreatic ductal adenocarcinoma (PDAC) is one of the most aggressive tumors with an extremely low 5-year survival rate. Accumulating evidence has unveiled that inflammatory response promotes tumor progression, enhances angiogenesis, and causes local immunosuppression. Herein, we aim to develop an inflammatory related prognostic signature, and found it could be used to predict gemcitabine response in PDAC.

Methods: PDAC cohorts with mRNA expression profiles and clinical information were systematically collected from the four public databases. An inflammatory response related genes (IRRGs) prognostic signature was constructed by LASSO regression analysis. Kaplan–Meier survival analysis, receiver operating characteristic analysis, principal component analysis, and univariate and multivariate Cox analyses were carried out to evaluate effectiveness, and reliability of the signature. The correlation between gemcitabine response and risk score was evaluated in the TCGA-PAAD cohort. The GDSC database, pRRophetic algorithm, and connectivity map analysis were used to predict gemcitabine sensitivity and identify potential drugs for the treatment of PDAC. Finally, we analyzed differences in frequencies of gene mutations, infiltration of immune cells, as well as biological functions between different subgroups divided by the prognostic signature.

Results: We established a seven IRRGs (ADM, DCBLD2, EREG, ITGA5, MIF, TREM1, and BTG2) signature which divided the PDAC patients into low- and high-risk groups. Prognostic value of the signature was validated in 11 PDAC cohorts consisting of 1337 PDAC patients from 6 countries. A nomogram that integrated the IRRGs signature and clinicopathologic factors of PDAC patients was constructed. The risk score showed positive correlation with gemcitabine resistance. Two drugs (BMS-536924 and dasatinib) might have potential therapeutic implications in high-risk PDAC patients. We found that the high-risk group had higher frequencies of KRAS, TP53, and CDKN2A mutations, increased infiltration of macrophages M0, neutrophils, and macrophages M2 cells, as well as upregulated hypoxia and glycolysis pathways, while the low-risk group had increased infiltration of CD8⁺ T, naïve B, and plasma and macrophages M1 cells.

Conclusion: We constructed and validated an IRRGs signature that could be used to predict the prognosis and gemcitabine response of patients with PDAC, as well as two drugs (BMS-536924 and dasatinib) may contribute to PDAC treatment.

Hypoxia-Related lncRNA Correlates With Prognosis and Immune Microenvironment in Lower-Grade Glioma

Background

Hypoxia-related genes are demonstrated to correlate with the prognosis of various cancers. However, the role of hypoxia-related long non-coding RNAs (HRLs) in lower-grade glioma (LGG) remains unclear.

Methods

A total of 700 LGG samples were extracted from TCGA and CGGA databases. Pearson correlation analysis was used to identify HRLs. Lasso analysis was adopted to construct the HRL signature. TIDE algorithm was used to predict responses to immune checkpoint inhibitors. Cell proliferation was estimated by cell counting kit-8 assay, colony formation assay, and EdU assay.

Results

We identified 340 HRLs and constructed a novel risk signature composed of 19 HRLs. The risk score exhibited potent value in predicting the prognosis of LGG patients and was significantly associated with the prognosis of LGG patients. Moreover, HRL signature could distinguish patients with similar expression levels of immune checkpoints and might predict the efficacy of immune checkpoint inhibitors. Additionally, hypoxia-related pathways and immune pathways were enriched in high-risk group, and high risk score indicated low tumor purity and high immune infiltration. Two major HRLs, LINC00941 and BASP1-AS1, could significantly affect the proliferation of glioma cells.

Conclusions

Our study constructed a novel HRL signature that could predict the prognosis and immunotherapy response of LGG patients. HRLs could be novel biomarkers to predict the prognosis of LGG patients and potential targets for LGG treatment.

Prognostic value of immune-related lncRNA pairs in patients with bladder cancer

BACKGROUND

The characteristics of immune-related long non-coding ribonucleic acids (ir-lncRNAs), regardless of their specific levels, have important implications for the prognosis of patients with bladder cancer.

METHODS

Based on The Cancer Genome Atlas database, original transcript data were analyzed. The ir-lncRNAs were obtained using a coexpression method, and their differentially expressed pairs (DE-ir-lncRNAs) were identified by univariate analysis. The lncRNA pairs were verified using a Lasso regression test. Thereafter, receiver operating characteristic curves were generated, and an optimal risk model was established. The clinical value of the model was verified through the analysis of patient survival rates, clinicopathological characteristics, presence of tumor-infiltrating immune cells, and chemotherapy efficacy evaluation.

RESULTS

In total, 49 pairs of DE-ir-lncRNAs were identified, of which 21 were included in the Cox regression model. A risk regression model was established on the premise of not involving the specific expression value of the transcripts.

CONCLUSIONS

The method and model used in this study have important clinical predictive value for bladder cancer and other malignant tumors.

Plasma heat shock protein 90alpha as a biomarker for the diagnosis of liver cancer: in patients with different clinicopathologic characteristics

PURPOSES

The purposes of this study were to assess the correlation between the plasma level of Hsp90α and the clinicopathological characteristics of patients with liver cancer and compare the diagnostic efficacy of Hsp90α, AFP, CEA, and CA199 in HCC.

EXPERIMENTAL DESIGN

A total of 200 individuals, including 140 patients with liver cancer or benign liver diseases and 60 healthy people, were enrolled for quantitative measurement of plasma Hsp90α by ELISA.

RESULTS

The plasma level of Hsp90α was significantly different between patients with liver cancer or benign liver diseases and healthy controls (P < 0.001). The sensitivity, specificity, and AUC (95% CI) of Hsp90α were 93.2%, 85.4%, and 0.931% (0.891-0.972%), respectively, when Hsp90α was applied to differentiate liver cancer patients and healthy controls. Significant positive correlations between the plasma Hsp90α level and clinicopathological characteristics such as the history of basic liver disease (P = 0.038), active stage of hepatitis (P = 0.039), Child-Pugh score (P < 0.001), size of focal liver lesions (P = 0.004), and extrahepatic metastasis (P < 0.001) were observed. AFP + Hsp90α was the best combination strategy for the auxiliary diagnosis of HCC, with a sensitivity of 95.7%, a specificity of 97.5%, and an AUC of 0.990 (0.976-1.000). The level of plasma Hsp90α decreased significantly (P < 0.001) after resection of tumor tissue.

CONCLUSIONS

This study demonstrated that plasma Hsp90α levels are useful as a diagnostic biomarker in liver cancer and may predict the responses of patients with liver cancer to surgery. Some clinicopathological characteristics could affect the plasma Hsp90α levels.