A stroma-related lncRNA panel for predicting recurrence and adjuvant chemotherapy benefit in patients with early-stage colon cancer

Integrative analysis of co-expression pattern of solute carrier transporters reveals molecular subtypes associated with tumor microenvironment hallmarks and clinical outcomes in colon cancer

Recent findings have suggested that solute carrier (SLC) transporters play an important role in tumor development and progression, and alterations in the expression of individual SLC genes are critical for fulfilling the heightened metabolic requirements of cancerous cells. However, the global influence of the co-expression pattern of SLC transporters on the clinical stratification and characteristics of the tumor microenvironment (TME) remains unexplored. In this study, we identified five SLC gene subtypes based on transcriptome co-expression patterns of 187 SLC transporters by consensus clustering analysis. These subtypes, which were characterized by distinct TME and biological characteristics, were successfully employed for prognostic and chemotherapy response prediction in colon cancer patients, as well as demonstrated associations with immunotherapy benefits. Then, we generated an SLC score model comprising 113 genes to quantify SLC gene co-expression patterns and validated it as an independent prognostic factor and drug response predictor in several independent colon cancer cohorts. Patients with a high SLC score possessed distinct characteristics of copy number variation, genomic mutations, DNA methylation, and indicated an SLC-S2 subtype, which was characterized by strong stromal cell infiltration, stromal pathway activation, poor prognosis, and low predicted fluorouracil and immunotherapeutic responses. Furthermore, the analysis of the Cancer Therapeutics Response Portal database revealed that inhibitors targeting PI3K catalytic subunits could serve as promising chemosensitizing agents for individuals exhibiting high SLC scores. In conclusion, the co-expression patterns of SLC transporters aided the disease classification, and the SLC score proved to be a reliable tool for distinguishing SLC gene subtypes and guiding precise treatment in patients with colon cancer.

Serum LINC00152 and UCA1 in HCV-Induced Hepatocellular Carcinoma: Clinical Significance and Prognostic Value

Background

Despite significant advancements in the molecular characterization of hepatocellular carcinoma (HCC), no oncogene addiction has been discovered. Long noncoding RNAs (lncRNAs) have a lot of promise as cancer biomarkers. LINC00152 and UCA1 have shown potential as diagnostic, prognostic, and therapeutic targets for human cancers.

Aim

To investigate the diagnostic and prognostic potential of serum LINC00152 and UCA1 in hepatocellular carcinoma (HCC).

Methods

The expression levels of LINC00152 and UCA1 in blood samples from 120 patients (60 with HCC, 60 with liver cirrhosis) and 40 healthy subjects were assessed using real-time qRT-PCR.

Results

Serum LINC00152 and UCA1 expression were considerably higher in HCC patients compared to patients with liver cirrhosis and the healthy controls (p<0.001 and p<0.001 respectively). And their expressions in the liver cirrhosis group were significantly higher than in healthy controls. Both lncRNAs performed well in the ROC analysis, distinguishing HCC patients from patients with liver cirrhosis. Higher levels of LINC00152 expression were linked to lesions in both lobes of the liver (p=0.02), while higher levels of UCA1 expression were linked to vascular invasion and the late stage (p=0.01, p=0.03 respectively). The multivariate analysis showed that a high level of LINC00152 in the blood was an independent indicator of a bad outcome for HCC patients (HR=2.23, 95% CI= 1.30-5.29, p=0.03).

Conclusion

Serum LINC00152 and UCA1 expression were upregulated in patients with HCC, suggesting their use as non-invasive biomarkers for HCC. Furthermore, LINC00152 has the potential to serve as a prognostic indicator.

Expression pattern of secretory-cell-related transcriptional signatures in colon adenocarcinomas defines tumor microenvironment characteristics and correlates with clinical outcomes

Despite the connection of secretory cells to distinct mucus-containing colon cancer histological subtypes and the interaction of secretory cells with immune cells in the pathogenesis of intestinal inflammatory diseases, whether the secretory cell signatures are associated with tumor microenvironment (TME) heterogeneity and can aid in colon cancer patient classification have not been investigated. Here, by performing the principal component analysis and consensus clustering analysis, we identified four distinct expression patterns based on secretory cell signatures which were significantly associated with different clinical behaviors, TME landscape, pathway activation, genomic mutations, and DNA methylation characteristics. Subsequently, a 'SCS score' model was constructed. The high SCS score indicated a pattern of 'secretory cell subtype 2', which was characterized by stromal infiltration and activation, and predicted poor prognosis and low sensitivity to fluorouracil-based chemotherapy and immunotherapy, but high sensitivity to PI3K catalytic subunit inhibitors. In conclusion, our study comprehensively uncovered the tumor heterogeneity related to secretory cell signature expression patterns. Moreover, the SCS score can supplement routine histopathological assessments to guide personalized therapeutic strategies in colon cancer patients.

HELLPAR/RRM2 axis related to HMMR as novel prognostic biomarker in gliomas

BACKGROUND

Gliomas are the most frequent type of central nervous system tumor, accounting for more than 70% of all malignant CNS tumors. Recent research suggests that the hyaluronan-mediated motility receptor (HMMR) could be a novel potential tumor prognostic marker. Furthermore, mounting data has highlighted the important role of ceRNA regulatory networks in a variety of human malignancies. The complexity and behavioural characteristics of HMMR and the ceRNA network in gliomas, on the other hand, remained unknown.

METHODS

Transcriptomic expression data were collected from TCGA, GTEx, GEO, and CGGA database.The relationship between clinical variables and HMMR was analyzed with the univariate and multivariate Cox regression. Kaplan-Meier method was used to assess OS. TCGA data are analyzed and processed, and the correlation results obtained were used to perform GO, GSEA, and ssGSEA. Potentially interacting miRNAs and lncRNAs were predicted by miRWalk and StarBase.

RESULTS

HMMR was substantially expressed in gliomas tissues compared to normal tissues. Multivariate analysis revealed that high HMMR expression was an independent predictive predictor of OS in TCGA and CGGA. Functional enrichment analysis found that HMMR expression was associated with nuclear division and cell cycle. Base on ssGSEA analysis, The levels of HMMR expression in various types of immune cells differed significantly. Bioinformatics investigation revealed the HEELPAR-hsa-let-7i-5p-RRM2 ceRNA network, which was linked to gliomas prognosis. And through multiple analysis, the good predictive performance of HELLPAR/RRM2 axis for gliomas patients was confirmed.

CONCLUSION

This study provides multi-layered and multifaceted evidence for the importance of HMMR and establishes a HMMR-related ceRNA (HEELPAR-hsa-let-7i-5p-RRM2) overexpressed network related to the prognosis of gliomas.

lncRNAs Functioned as ceRNA to Sponge miR-15a-5p Affects the Prognosis of Pancreatic Adenocarcinoma and Correlates With Tumor Immune Infiltration

Pancreatic adenocarcinoma (PAAD) is one of the most common malignant tumors with poor prognosis worldwide. Mounting evidence suggests that the expression of lncRNAs and the infiltration of immune cells have prognostic value for patients with PAAD. We used Gene Expression Omnibus (GEO) database and identified six genes (COL1A2, ITGA2, ITGB6, LAMA3, LAMB3, and LAMC2) that could affect the survival rate of pancreatic adenocarcinoma patients. Based on a series of in silico analyses for reverse prediction of target genes associated with the prognosis of PAAD, a ceRNA network of mRNA (COL1A2, ITGA2, LAMA3, LAMB3, and LAMC2)–microRNA (miR-15a-5p)–long non-coding RNA (LINC00511, LINC01578, PVT1, and TNFRSF14-AS1) was constructed. We used the algorithm “CIBERSORT” to assess the proportion of immune cells and found three overall survival (OS)–associated immune cells (monocytes, M1 macrophages, and resting mast cell). Moreover, the OS-associated gene level was significantly positively associated with immune checkpoint expression and biomarkers of immune cells. In summary, our results clarified that ncRNA-mediated upregulation of OS-associated genes and tumor-infiltration immune cells (monocytes, M1 macrophages M1, and resting mast cell resting) correlated with poor prognosis in PAAD.

Cross talk between acetylation and methylation regulators reveals histone modifier expression patterns posing prognostic and therapeutic implications on patients with colon cancer

Background

Alterations in histone modifications have been reported to be related to tumorigenicity and tumor progression. However, whether histone modification can aid the classification of patients or influence clinical behavior in patients with colon cancer remains unclear. Therefore, this study aimed to evaluate histone modifier expression patterns using the unsupervised clustering of the transcriptomic expressions of 88 histone acetylation and methylation regulators.

Results

In this study, by consensus clustering analysis based on the transcriptome data of 88 histone modification regulators, we identified four distinct expression patterns of histone modifiers associated with different prognoses, intrinsic fluorouracil sensitivities, biological pathways, and tumor microenvironment characteristics among 1372 colon cancer samples. In these four clusters, the HMC4 cluster represented a stroma activation phenotype characterized by both the worst prognosis and lowest response rates to fluorouracil treatment. Then, we established a scoring scheme comprising 155 genes designated as “HM_score” by using the Boruta algorithm to distinguish colon cancer patients within the HMC4 cluster. Patients with a high HM_score were considered to have high stromal pathway activation, high stromal fraction, and an unfavorable prognosis. Further analyses indicated that a high HM_score also correlated with reduced therapeutic benefits from fluorouracil chemotherapy. Moreover, through CRISPR library screening, ZEB2 was found to be a critical driver gene that mediates fluorouracil resistance, which is associated with histone modifier expression patterns.

Conclusions

This study highlights that characterizing histone modifier expression patterns may help better understand the epigenetic mechanisms underlying tumor heterogeneity in patients with colon cancer and provide more personalized therapeutic strategies.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13148-022-01290-y.

Identification of immune-related lncRNA panel for predicting immune checkpoint blockade and prognosis in head and neck squamous cell carcinoma

Purpose

Immunotherapy is changing head and neck squamous cell carcinoma (HNSCC) treatment pattern. According to the Chinese Society of Clinical Oncology (CSCO) guidelines, immunotherapy has been deemed as first‐line recommendation for recurrent/metastatic HNSCC, marking that advanced HNSCC has officially entered the era of immunotherapy. Long non‐coding RNAs (lncRNAs) impact every step of cancer immunity. Therefore, reliable immune‐lncRNAs able to accurately predict the immune landscape and survival of HNSCC are crucial to clinical management.

Methods

In the current study, we downloaded the transcriptomic and clinical data of HNSCC from The Cancer Genome Atlas and identified differentially expressed immune‐related lncRNAs (DEir‐lncRNAs). Further then, Cox and least absolute shrinkage and selection operator (LASSO) regression analyses were performed to identify proper DEir‐lncRNAs to construct optimal risk model. Low‐risk and high‐risk groups were classified based on the optimal cut‐off value generated by the areas under curve for receiver operating characteristic curves (AUC), and Kaplan–Meier survival curves were utilized to validate the prediction model. We then evaluated the model based on the clinical factors, immune cell infiltration, and chemotherapeutic and immunotherapeutic efficacy between two groups.

Results

In our study, we identified 256 Deir‐lncRNAs in HNSCC. A total of 18 Deir‐lncRNA pairs (consisting of 35 Deir‐lncRNAs) were used to construct a risk model significantly associated with survival of HNSCC. Cox proportional hazard regression analysis confirmed that our risk model could be served as an independent prognostic indicator. Besides, HNSCC patients with low‐risk score significantly enriched of CD8⁺ T cell, and corelated with high chemosensitivity and immunotherapeutic sensitivity.

Conclusion

Our risk model could be served as a promising clinical prediction indicator, effective discoursing of the immune cell infiltration of HNSCC patients, and distinguishing patients who could benefit from chemotherapy and immunotherapy.

Evaluation of stromal cell infiltration in the tumor microenvironment enable prediction of treatment sensitivity and prognosis in colon cancer

Current clinical factors for screening candidates that might benefit from adjuvant chemotherapy in colon cancer are inadequate. Tumor microenvironment, especially the stromal components, has the potential to determine treatment response. However, clinical translation of the tumor-associated stromal characterization into a practical biomarker for helping treatment decision has not been established. Using machine learning, we established a novel 31-gene signature, called stromal cell infiltration intensity score (SIIS), to distinguish patients characterized by the enrichment of abundant stromal cells in five colon cancer datasets from GEO (N = 990). Patients with high-SIIS were at higher risk for recurrence and mortality, and could not benefit from adjuvant chemotherapy due to their intrinsic drug resistance; however, the opposite was reported for patients with low-SIIS. The role of SIIS in detection of patients with high stromal cell infiltration and reduced drug efficiency was consistently validated in the TCGA-COAD cohort (N = 382), Sun Yat-sen University Cancer Center cohort (N = 30), and could also be observed in TCGA pan-cancer settings (N = 4898) and four independent immunotherapy cohorts (N = 467). Based on multi-omics data analysis and the CRISPR library screen, we reported that lack of gene mutation, hypomethylation in ADCY4 promoter region, activation of WNT-PCP pathway and SIAH2-GPX3 axis were potential mechanisms responsible for the chemoresistance of patients within high-SIIS group. Our findings demonstrated that SIIS provide an important reference for those making treatment decisions for such special patients.

Long Non-Coding RNA: A Potential Strategy for the Diagnosis and Treatment of Colorectal Cancer

Colorectal cancer (CRC), being one of the most commonly diagnosed cancers worldwide, endangers human health. Because the pathological mechanism of CRC is not fully understood, there are many challenges in the prevention, diagnosis, and treatment of this disease. Long non-coding RNAs (lncRNAs) have recently drawn great attention for their potential roles in the different stages of CRC formation, invasion, and progression, including regulation of molecular signaling pathways, apoptosis, autophagy, angiogenesis, tumor metabolism, immunological responses, cell cycle, and epithelial-mesenchymal transition (EMT). This review aims to discuss the potential mechanisms of several oncogenic lncRNAs, as well as several suppressor lncRNAs, in CRC occurrence and development to aid in the discovery of new methods for CRC diagnosis, treatment, and prognosis assessment.

Association of dysbindin expression with individualized postoperative prognosis and chemotherapy benefit among patients with gastric adenocarcinoma

Background: The current model for predicting prognosis and chemotherapy response of patients with gastric adenocarcinoma is the TNM staging system, which may lack adequate accuracy and evaluations of molecular features at the individual level. We aimed to develop a prediction model to assess the individualized prognosis and responsiveness to fluorouracil-based adjuvant chemotherapy.

Method: This retrospective study concluded 2 independent cohorts of patients with GAC. The expression of dysbindin was quantified and evaluated the association with the overall survival for GAC patients. A prediction model for postoperative overall survival was generated and internally and externally validated. The interaction between dysbindin expression and PACT was detected in advanced GAC patients.

Results: Of the 637 patients enrolled in the study, 425 were men (66.7%) with a mean (SD) age of 59.79 (9.81) years. High levels of dysbindin expression predicted a poor prognosis in patients with GAC. Multivariate analysis demonstrated dysbindin expression was an independent prognostic predictor of overall survival in the test, validation and combined cohorts. A prognostic predictive model incorporating age, dysbindin expression, pathological differentiation, Lauren's classification and the TNM staging system was established. This model had better predictive accuracy for overall survival than the traditional TNM staging system and was internally and externally validated. More importantly, advanced GAC patients with low dysbindin expression were likely to benefit from fluorouracil-based PACT.

Conclusion: The risk stratification model incorporating dysbindin expression and TNM staging system showed better predictive accuracy. Advanced GAC patients with low dysbindin expression revealed better response of fluorouracil-based adjuvant chemotherapy.

Construction of a Myc-associated ceRNA network reveals a prognostic signature in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) remains an extremely lethal disease worldwide. High-throughput methods have revealed global transcriptome dysregulation; however, a comprehensive investigation of the complexity and behavioral characteristics of the competing endogenous RNA (ceRNA) network in HCC is lacking. In this study, we extracted the transcriptome (RNA) sequencing data of 371 HCC patients from The Cancer Genome Atlas platform. With the comparison of the high Myc expression (Myc^high) tumor and low Myc expression (Myc^low) tumor groups in HCC, we identified 1,125 differentially expressed (DE) mRNAs, 589 long non-coding RNAs (lncRNAs), and 93 microRNAs (miRNAs). DE RNAs predicted the interactions necessary to construct an associated Myc ceRNA network, including 19 DE lncRNAs, 5 miRNAs, and 72 mRNAs. We identified a significant signature (long intergenic non-protein-coding [LINC] RNA 2691 [LINC02691] and LINC02499) that effectively predicted overall survival and had protective effects. The target genes of microRNA (miR)-212-3p predicted to intersect with DE mRNAs included SEC14-like protein 2 (SEC14L2) and solute carrier family 6 member 1 (SLC6A1), which were strongly correlated with survival and prognosis. With the use of the lncRNA-miRNA-mRNA axis, we constructed a ceRNA network containing four lncRNAs (LINC02691, LINC02499, LINC01354, and NAV2 antisense RNA 4), one miRNA (miR-212-3p), and two mRNAs (SEC14L2 and SLC6A1). Overall, we successfully constructed a mutually regulated ceRNA network and identified potential precision-targeted therapies and prognostic biomarkers.

Comprehensive analysis to identify DLEU2L/TAOK1 axis as a prognostic biomarker in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is one of the deadliest malignant tumors that are harmful to human health. Increasing evidence has underscored the critical role of the competitive endogenous RNA (ceRNA) regulatory networks among various human cancers. However, the complexity and behavior characteristics of the ceRNA network in HCC were still unclear. In this study, we aimed to clarify a phosphatase and tensin homolog (PTEN)-related ceRNA regulatory network and identify potential prognostic markers associated with HCC. The expression profiles of three RNAs (long non-coding RNAs [lncRNAs], microRNAs [miRNAs], and mRNAs) were extracted from The Cancer Genome Atlas (TCGA) database. The DLEU2L-hsa-miR-100-5p/ hsa-miR-99a-5p-TAOK1 ceRNA network related to the prognosis of HCC was obtained by performing bioinformatics analysis. Importantly, we identified the DLEU2L/TAOK1 axis in the ceRNA by using correlation analysis, and it appeared to become a clinical prognostic model by Cox regression analysis. Furthermore, methylation analyses suggested that the abnormal upregulation of the DLEU2L/TAOK1 axis likely resulted from hypomethylation, and immune infiltration analysis showed that the DLEU2L/TAOK1 axis may have an impact on the changes in the tumor immune microenvironment and the development of HCC. In summary, the current study constructing a ceRNA-based DLEU2L/TAOK1 axis might be a novel important prognostic factor associated with the diagnosis and prognosis of HCC.

An Eight-CircRNA Assessment Model for Predicting Biochemical Recurrence in Prostate Cancer

Prostate cancer (PCa) is a high morbidity malignancy in males, and biochemical recurrence (BCR) may appear after the surgery. Our study is designed to build up a risk score model using circular RNA sequencing data for PCa. The dataset is from the GEO database, using a cohort of 144 patients in Canada. We removed the low abundance circRNAs (FPKM < 1) and obtained 546 circRNAs for the next step. BCR-related circRNAs were selected by Logistic regression using the “survival” and “survminer” R package. Least absolute shrinkage and selector operation (LASSO) regression with 10-fold cross-validation and penalty was used to construct a risk score model by “glmnet” R software package. In total, eight circRNAs (including circ_30029, circ_117300, circ_176436, circ_112897, circ_112897, circ_178252, circ_115617, circ_14736, and circ_17720) were involved in our risk score model. Further, we employed differentially expressed mRNAs between high and low risk score groups. The following Gene Ontology (GO) analysis were visualized by Omicshare Online tools. As per the GO analysis results, tumor immune microenvironment related pathways are significantly enriched. “CIBERSORT” and “ESTIMATE” R package were used to detect tumor-infiltrating immune cells and compare the level of microenvironment scores between high and low risk score groups. What’s more, we verified two of eight circRNA’s (circ_14736 and circ_17720) circular characteristics and tested their biological function with qPCR and CCK8 in vitro. circ_14736 and circ_17720 were detected in exosomes of PCa patients’ plasma. This is the first bioinformatics study to establish a prognosis model for prostate cancer using circRNA. These circRNAs were associated with CD8⁺ T cell activities and may serve as a circRNA-based liquid biopsy panel for disease prognosis.

Long non-coding RNAs towards precision medicine in gastric cancer: early diagnosis, treatment, and drug resistance

Gastric cancer is a deadly disease and remains the third leading cause of cancer-related death worldwide. The 5-year overall survival rate of patients with early-stage localized gastric cancer is more than 60%, whereas that of patients with distant metastasis is less than 5%. Surgical resection is the best option for early-stage gastric cancer, while chemotherapy is mainly used in the middle and advanced stages of this disease, despite the frequently reported treatment failure due to chemotherapy resistance. Therefore, there is an unmet medical need for identifying new biomarkers for the early diagnosis and proper management of patients, to achieve the best response to treatment. Long non-coding RNAs (lncRNAs) in body fluids have attracted widespread attention as biomarkers for early screening, diagnosis, treatment, prognosis, and responses to drugs due to the high specificity and sensitivity. In the present review, we focus on the clinical potential of lncRNAs as biomarkers in liquid biopsies in the diagnosis and prognosis of gastric cancer. We also comprehensively discuss the roles of lncRNAs and their molecular mechanisms in gastric cancer chemoresistance as well as their potential as therapeutic targets for gastric cancer precision medicine.

Macrophage correlates with immunophenotype and predicts anti-PD-L1 response of urothelial cancer

Immune-checkpoint blockades (ICBs) have been routinely implemented to treat metastatic urothelial cancer (mUC), whereas robust biomarkers are urgently warranted. Herein, we explored latent promising biomarkers based on 348 pretreatment mUC samples from IMvigor210. Methods: The genome, transcriptome, immunome, and metabolome were systemically analyzed using the external TCGA dataset for validation. Kaplan-Meier and ROC curve analyses were performed to estimate the predictive capacity of M1-macrophage infiltration. Chi-square/Spearman/Mann Whitney U test are used to determine its correlation to genetic, biochemical, and clinicopathological parameters. Results: M1 frequency is a robust biomarker for predicting the prognosis and response to ICBs, which is non-inferior to tumor mutation burden (TMB) or tumor neoantigen burden (TNB), and exceeds CD8 T cells, T cell inflamed gene expression profile (GEP), and PD-L1 expression. Moreover, M1 infiltration is associated with immune phenotypes (AUC = 0.785) and is negatively correlated with immune exclusion. Additionally, transcriptomic analysis showed immune activation in the high-M1 subgroup, whereas it showed steroid and drug metabolism reprograming in the M1-deficient subset, which characterized the limited sensitivity to ICB therapy. Notably, investigation of the corresponding intrinsic genomic profiles highlighted the significance of TP53 and FGFR alterations. Conclusions: M1 infiltration is a robust biomarker for immunotherapeutic response and immunophenotype determination in an mUC setting. Innate immunity activation involving macrophage polarization remodeling and anti-FGFR mutations may be promising strategies for synergy with anti-PD-L1 treatments and may help prolong the clinical survival of patients with mUC.

A stroma-related lncRNA panel for predicting recurrence and adjuvant chemotherapy benefit in patients with early-stage colon cancer

The heterogeneity in prognoses and chemotherapeutic responses of colon cancer patients with similar clinical features emphasized the necessity for new biomarkers that help to improve the survival prediction and tailor therapies more rationally and precisely. In the present study, we established a stroma‐related lncRNA signature (SLS) based on 52 lncRNAs to comprehensively predict clinical outcome. The SLS model could not only distinguish patients with different recurrence and mortality risks through univariate analysis, but also served as an independent factor for relapse‐free and overall survival. Compared with the conventionally used TNM stage system, the SLS model clearly possessed higher predictive accuracy. Moreover, the SLS model also effectively screened chemotherapy‐responsive patients, as only patients in the low‐SLS group could benefit from adjuvant chemotherapy. The following cell infiltration and competing endogenous RNA (ceRNA) network functional analyses further confirmed the association between the SLS model and stromal activation‐related biological processes. Additionally, this study also identified three phenotypically distinct colon cancer subtypes that varied in clinical outcome and chemotherapy benefits. In conclusion, our SLS model may be a significant determinant of survival and chemotherapeutic decision‐making in colon cancer and may have a strong clinical transformation value.