Identification of a Four Hypoxia-Associated Long Non-Coding RNA Signature and Establishment of a Nomogram Predicting Prognosis of Clear Cell Renal Cell Carcinoma

Construction of a novel prognostic model based on lncRNAs-related to DNA damage repair for predicting the prognosis of clear cell renal cell carcinoma

BACKGROUND

CcRCC has the characteristics of high aggression, high metastasis, high mortality, wide tumour heterogeneity and variable clinical course. The purpose of this study was to explore the potential value of lncRNAs-related to DNA damage repair (DDR) in predicting the prognosis of ccRCC by construction and verification a novel prognostic model.

METHODS

RNA-seq data and clinical data of ccRCC were downloaded from public databases. Subsequently, Pearson correlation analysis and differential expression analysis were performed to identify DElncRNAs-related to DDR. Then, through univariate Cox analysis and LASSO analysis, the DElncRNAs-related to DDR associated with prognosis were screened for the construction of novel risk score prognostic model. In addition, functional annotation, tumour mutation burden, immune correlation and drug sensitivity analyses were performed based on risk score to assess the characteristics of patients in different risk score groups.

RESULTS

Based on univariate Cox analysis and LASSO analysis, four best DElncRNAs-related to DDR were selected. Subsequently, a novel risk score prognostic model based on these four DElncRNAs was constructed through LASSO. Multivariate Cox analysis showed that risk score and age were independent prognostic factors for ccRCC (p < 0.05). Functional enrichment analysis showed that DDR-related biological processes were mainly enriched in the high risk group. The highly mutated genes in the high and low risk groups were the same (VHL, PBRM1 and TTN), but they also had their own unique mutated genes. Pearson correlation analysis showed that the risk score was significantly (p < 0.05) positively correlated with the infiltration degree of CD8 T cells evaluated by six algorithms. In addition, it was found that the high and low risk groups had different sensitivities to the drugs Etoposide, Imatinib, Sorafenib, Bosutinib and Sunitinib.

CONCLUSION

A novel prognostic model was constructed based on four DElncRNAs-related to DDR. The model has satisfactory accuracy in predicting survival of ccRCC patients.

Activation of RHO-GTPase gene pattern correlates with adverse clinical outcome and immune microenvironment in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC), the most prevalent renal cancer subtype, is frequently associated with poor prognosis. RHO-GTPase signaling genes have been implicated in tumor aggressiveness and unfavorable survival, but their potential in risk stratification and therapeutic guidance for ccRCC patients remains unexplored. Univariate regression identified prognostically relevant RHO-GTPase signaling genes, followed by consensus clustering for ccRCC subtype classification. LASSO regression selected key genes to construct a six-gene risk model. The model was evaluated for prognostic stratification, immune status, immunotherapy response, and chemotherapy sensitivity. Key genes were analyzed at the genomic, single-cell, and protein levels to explore underlying mechanisms. Among 62 prognostically relevant RHO-GTPase signaling genes, six (ARHGAP11B, NUF2, PLK1, CYFIP2, IQGAP2, and VAV3) were identified to form a robust prognostic signature. This model stratified patients into high- and low-risk groups, with high-risk patients demonstrating significantly worse outcomes. The model exhibited excellent predictive accuracy (AUC > 0.7 in training and validation cohorts). High-risk patients were characterized by an immunosuppressive microenvironment and reduced sensitivity to immunotherapy. Drug sensitivity analysis revealed 107 agents correlated with the risk score, underscoring therapeutic relevance. Mechanistically, the six key genes showed distinct expression patterns, cellular distribution, and positive correlation with VHL mutations, highlighting their potential as actionable drug targets. This study established a novel six-gene RHO-GTPase signaling model for predicting prognosis, immune status, and therapeutic responses in ccRCC, which offers potential for improving patient stratification and guiding personalized treatment strategies.

HAGLR, A Long Non-coding RNA of Potential Tumor Suppressive Function in Clear Cell Renal Cell Carcinoma: Diagnostic and Prognostic Implications

Research works suggested the role of long non-coding RNAs (lncRNAs) in pathogenesis of clear cell renal cell carcinoma (ccRCC). lncRNA HAGLR is studied in several malignancies, but not in ccRCC. From The Cancer Genome Atlas Kidney Renal Clear Cell Carcinoma (TCGA-KIRC) dataset, we analyzed molecular alterations of HAGLR and constructed a competitive endogenous RNA (ceRNA) network with related miRNAs and mRNAs. Gene Ontology analysis was done to identify important pathways enriched with HAGLR recovered mRNAs. Clinical importance of HAGLR and related mRNAs was assessed and, the impact of selected mRNA-encoding genes on tumor immune infiltration was studied using TIMER. HAGLR expression was reduced in ccRCC than in normal kidneys, and correlated significantly with gene promoter methylation. Low HAGLR level in tumors showed diagnostic potency, and was associated with clinicopathological parameters (stage/grade/metastasis) and poor patient survival. The HAGLR-associated ceRNA network constituted 13 miRNAs and 23 mRNAs differentially expressed in the TCGA-KIRC dataset. From HAGLR recovered mRNA-encoding genes, we developed a 5-gene (PAQR5, ARHGAP24, HABP4, PDLIM5, and RPS6KA2) prognostic signature in the training dataset and validated it in testing as well as entire datasets. The expression level of signature genes showed negative correlation with tumor infiltration of immune cells having adverse impact on ccRCC prognosis and also with tumor derived chemokines facilitating the infiltration. In conclusion, HAGLR seemed to play a tumor suppressive role in ccRCC. HAGLR and associated gene signature may have implementation in improving existing prognostic measure and developing effective immunotherapeutic strategies for ccRCC.

Disulfidptosis-associated LncRNA signature predicts prognosis and immune response in kidney renal clear cell carcinoma

BACKGROUND

Kidney renal clear cell carcinoma (KIRC) represents a significant proportion of renal cell carcinomas and is characterized by high aggressiveness and poor prognosis despite advancements in immunotherapy. Disulfidptosis, a novel cell death pathway, has emerged as a critical mechanism in various cellular processes, including cancer. This study leverages machine learning to identify disulfidptosis-related long noncoding RNAs (DRlncRNAs) as potential prognostic biomarkers in KIRC, offering new insights into tumor pathogenesis and treatment avenues.

RESULTS

Our analysis of data from The Cancer Genome Atlas (TCGA) led to the identification of 431 DRlncRNAs correlated with disulfidptosis-related genes. Five key DRlncRNAs (SPINT1-AS1, AL161782.1, OVCH1-AS1, AC131009.3, and AC108673.3) were used to develop a prognostic model that effectively distinguished between low- and high-risk patients with significant differences in overall survival and progression-free survival. The low-risk group had a favorable prognosis associated with a protective immune microenvironment and a better response to targeted drugs. Conversely, the high-risk group displayed aggressive tumor features and poor immunotherapy outcomes. Validation through qRT‒PCR confirmed the differential expression of these DRlncRNAs in KIRC cells compared to normal kidney cells, underscoring their potential functional significance in tumor biology.

CONCLUSIONS

This study established a robust link between disulfidptosis-related lncRNAs and patient prognosis in KIRC, underscoring their potential as prognostic biomarkers and therapeutic targets. The differential expression of these lncRNAs in tumor versus normal tissue further highlights their relevance in KIRC pathogenesis. The predictive model not only enhances our understanding of KIRC biology but also provides a novel stratification tool for precision medicine approaches, improving treatment personalization and outcomes in KIRC patients.

POLR3G promotes EMT via PI3K/AKT signaling pathway in bladder cancer

RNA Polymerase III Subunit G (POLR3G) promotes tumorigenesis, metastasis, cancer stemness, and chemoresistance of breast cancer and lung cancer; however, its biological function in bladder cancer (BLCA) remains unclear. Through bioinformatic analyses, we found that POLR3G expression was significantly elevated in BLCA tumor tissues and was associated with decreased survival. Multivariate Cox analysis indicated that POLR3G could serve as an independent prognostic risk factor. Our functional investigations revealed that POLR3G deficiency resulted in reduced migration and invasion of BLCA cells both in vitro and in vivo. Additionally, the expressions of EMT-related mesenchymal markers were also downregulated in POLR3G knockdown cells. Mechanistically, we showed that POLR3G could activate the PI3K/AKT signaling pathway. Inhibition of this pathway with LY294002 reduced the enhanced migration and invasion of BLCA cells induced by POLR3G overexpression, whereas the activation of this pathway using 740Y-P restored the abilities that were inhibited by POLR3G knockdown. Taken together, our findings suggested that POLR3G is a prognostic predictor for BLCA and promotes EMT of BLCA through activation of the PI3K/AKT signaling pathway.

A prognostic and immunotherapy effectiveness model for pancreatic adenocarcinoma based on cuproptosis-related lncRNAs signature

Pancreatic adenocarcinoma (PAAD) results in one of the deadliest solid tumors with discouraging clinical outcomes. Growing evidence suggests that long non-coding RNAs (lncRNAs) play a crucial role in altering the growth, prognosis, migration, and invasion of pancreatic cancer cells. Cuproptosis is a novel type of cell death induced by copper (Cu) and is associated with mitochondrial respiration during the tricarboxylic acid cycle. However, the relationship between lncRNAs related to cuproptosis and PAAD is poorly studied. In this study, we investigated the association between a signature of cuproptosis-related lncRNAs and the diagnosis of PAAD. Genomic data and clinical information were obtained using the TCGA dataset, while cuproptosis-related genes (CRGs) from previous studies. Co-expression analysis was utilized to identify lncRNAs associated with cuproptosis. We developed and verified a prognostic risk model following a classification of patients into high- and low-risk categories. The prediction capacity of the risk model was assessed using a number of methods including Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, nomograms, and principal component analysis (PCA). Furthermore, differentially expressed genes (DEGs) were used to perform functional enrichment analyses, and to examine the behaviors of various risk groups in terms of immune-related activities and medication sensitivity. We identified 7 cuproptosis-related lncRNA signatures, including CASC19, FAM83A-AS1, AC074099.1, AC007292.2, AC026462.3, AL358944.1, and AC009019.1, as overall survival (OS) predictors. OS and progression-free survival (PFS) showed significant differences among patients in different risk groups. Independent prognostic analysis revealed that the cuproptosis-related lncRNA signatures can independently achieve patient prognosis. The risk model demonstrated strong predictive ability for patient outcomes, as evidenced by ROC curves, nomograms, and PCA. Higher tumor mutation burden (TMB) and lower tumor immune dysfunction and exclusion (TIDE) scores were observed in the high-risk group. Additionally, the low-risk group was hypersensitive to 3 anti-cancer medications, whereas the high-risk group was hypersensitive to one. A prognostic risk model with a good predictive ability based on cuproptosis-related lncRNAs was developed, providing a theoretical basis for personalized treatment and immunotherapeutic responses in pancreatic cancer.

Machine learning-based identification of tumor-infiltrating immune cell-associated model with appealing implications in improving prognosis and immunotherapy response in bladder cancer patients

Background

Immune cells are crucial components of the tumor microenvironment (TME) and regulate cancer cell development. Nevertheless, the clinical implications of immune cell infiltration-related mRNAs for bladder cancer (BCa) are still unclear.

Methods

A 10-fold cross-validation framework with 101 combinations of 10 machine-learning algorithms was employed to develop a consensus immune cell infiltration-related signature (IRS). The predictive performance of IRS in terms of prognosis and immunotherapy was comprehensively evaluated.

Results

The IRS demonstrated high accuracy and stable performance in prognosis prediction across multiple datasets including TCGA-BLCA, eight independent GEO datasets, our in-house cohort (PUMCH_Uro), and thirteen immune checkpoint inhibitors (ICIs) cohorts. Additionally, IRS was superior to traditional clinicopathological features (e.g., stage and grade) and 94 published signatures. Furthermore, IRS was an independent risk factor for overall survival in TCGA-BLCA and several GEO datasets, and for recurrence-free survival in PUMCH_Uro. In the PUMCH_Uro cohort, patients in the high-IRS group were characterized by upregulated CD8A and PD-L1 and TME of inflamed and immunosuppressive phenotypes. As predicted, these patients should benefit from ICI therapy and chemotherapy. Furthermore, in the ICI cohorts, the high-IRS group was related to a favorable prognosis and responders have dramatically higher IRS compared to non-responders.

Conclusions

Generally, these indicators suggested the promising application of IRS in urological practices for the early identification of high-risk patients and potential candidates for ICI application to prolong the survival of individual BCa patients.

Identification of MET fusions in solid tumors: A multicenter, large scale study in China

MET amplification and exon 14 skipping are well known as oncogenic drivers in multiple cancer types. However, MET fusions in most cancer types are poorly defined. To explore the profile and analyze the characteristics of MET fusions, a large-cohort study was conducted to screen MET fusions in clinical samples (n = 10 882) using DNA-based NGS. A total of 37 potentially functional MET fusions containing the intact tyrosine kinase domain (TKD) of MET were identified in 36 samples. Further, 15 novel MET fusions were identified in five cancer types, and the incidence of novel MET fusions accounted for 40.5% (15/37). Brain cancer had the highest incidence of MET fusion, with PTPRZ1-MET as the most common fusion (37.0%). All MET breakpoints in brain cancer (n = 27) were also located in intron 1, while those in lung cancer (n = 4) occurred in intron 1, intron 11, intron 14 and exon 14, respectively. The positive consistency of the common fusion group was 100% (11/11), while that of the rare fusion group was 53.8% (7/13). In conclusion, we provided a comprehensive genomic landscape of MET rearrangement and updated the MET fusions database for clinical test. In addition, we revealed that DNA-based NGS might serve as the clinical test for common MET fusions; however, rare MET fusions must be validated by both DNA-based NGS and RNA-based NGS. Prospective trials are necessary to confirm the treatment efficacy of MET inhibitors.

Hypoxia-Related lncRNA Prognostic Model of Ovarian Cancer Based on Big Data Analysis

Background

Hypoxia is regarded as a key factor in promoting the occurrence and development of ovarian cancer. In ovarian cancer, hypoxia promotes cell proliferation, epithelial to mesenchymal transformation, invasion, and metastasis. Long non-coding RNAs (lncRNAs) are extensively involved in the regulation of many cellular mechanisms, i.e., gene expression, cell growth, and cell cycle.

Materials and Methods

In our study, a hypoxia-related lncRNA prediction model was established by applying LASSO-penalized Cox regression analysis in public databases. Patients with ovarian cancer were divided into two groups based on the median risk score. The survival rate was analyzed in the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets, and the mechanisms were investigated.

Results

Through the prognostic analysis of DElncRNAs (differentially expressed long non-coding RNAs), a total of 5 lncRNAs were found to be closely associated with OS (overall survival) in ovarian cancer patients. It was evaluated through Kaplan-Meier analysis that low-risk patients can live longer than high-risk patients (TCGA: p = 1.302e - 04; ICGC: 1.501e - 03). The distribution of risk scores and OS status revealed that higher risk score will lead to lower OS. It was evaluated that low-risk group had higher immune score (p = 0.0064) and lower stromal score (p = 0.00023).

Conclusion

It was concluded that a hypoxia-related lncRNA model can be used to predict the prognosis of ovarian cancer. Our designed model is more accurate in terms of age, grade, and stage when predicting the overall survival of the patients of ovarian cancer.

The Role of Long Noncoding RNA (lncRNAs) Biomarkers in Renal Cell Carcinoma

Renal cell carcinoma is one of the common cancers whose incidence and mortality are continuously growing worldwide. Initially, this type of tumour is usually asymptomatic. Due to the lack of reliable diagnostic markers, one-third of ccRCC patients already have distant metastases at the time of diagnosis. This underlines the importance of establishing biomarkers that would enable the prediction of the disease's course and the risk of metastasis. LncRNA, which modulates genes at the epigenetic, transcriptional, and post-transcriptional levels, appears promising. The actions of lncRNA involve sponging and sequestering target miRNAs, thus affecting numerous biological processes. Studies have confirmed the involvement of RNAs in various diseases, including RCC. In this review, we focused on MALAT1 (a marker of serious pathological changes and a factor in the promotion of tumorigenesis), RCAT1 (tumour promoter in RCC), DUXAP9 (a plausible marker of localized ccRCC), TCL6 (exerting tumour-suppressive effects in renal cancer), LINC00342 (acting as an oncogene), AGAP2 Antisense1 (plausible predictor of RCC progression), DLEU2 (factor promoting tumours growth via the regulation of epithelial-mesenchymal transition), NNT-AS1 (sponge of miR-22 contributing to tumour progression), LINC00460 (favouring ccRCC development and progression) and Lnc-LSG1 (a factor that may stimulate ccRCC metastasis).

A hypoxia risk score for prognosis prediction and tumor microenvironment in adrenocortical carcinoma

Background: Adrenocortical carcinoma (ACC) is a rare malignant endocrine tumor derived from the adrenal cortex. Because of its highly aggressive nature, the prognosis of patients with adrenocortical carcinoma is not impressive. Hypoxia exists in the vast majority of solid tumors and contributes to invasion, metastasis, and drug resistance. This study aimed to reveal the role of hypoxia in Adrenocortical carcinoma and develop a hypoxia risk score (HRS) for Adrenocortical carcinoma prognostic prediction. Methods: Hypoxia-related genes were obtained from the Molecular Signatures Database. The training cohorts of patients with adrenocortical carcinoma were downloaded from The Cancer Genome Atlas, while another three validation cohorts with comprehensive survival data were collected from the Gene Expression Omnibus. In addition, we constructed a hypoxia classifier using a random survival forest model. Moreover, we explored the relationship between the hypoxia risk score and immunophenotype in adrenocortical carcinoma to evaluate the efficacy of immune check inhibitors (ICI) therapy and prognosis of patients. Results: HRS and tumor stage were identified as independent prognostic factors. HRS was negatively correlated with immune cycle activity, immune cell infiltration, and the T cell inflammatory score. Therefore, we considered the low hypoxia risk score group as the inflammatory immunophenotype, whereas the high HRS group was a non-inflammatory immunophenotype. In addition, the HRS was negatively related to the expression of common immune checkpoint molecules such as PD-L1, CD200, CTLA-4, and TIGIT, suggesting that patients with a lower hypoxia risk score respond better to immunotherapy. Conclusion: We developed and validated a novel hypoxia risk score to predict the immunophenotype and response of patients with adrenocortical carcinoma to immune check inhibitors therapy. These findings not only provide fresh prognostic indicators for adrenocortical carcinoma but also offer several promising treatment targets for this disease.

Foreboding lncRNA markers of low-grade gliomas dependent on metabolism

At present, there is no systematic study on the signature of long-chain noncoding RNAs (lncRNAs) involved in metabolism that can fully predict the prognosis in patients with low-grade gliomas (LGGs). Therefore, consistent metabolic-related lncRNA signatures need to be established. The Cancer Genome Atlas (TCGA) was used to identify the expression profile of lncRNAs containing 529 LGGs samples. LncRNAs and genes related to metabolism are used to establish a network in the form of coexpression to screen lncRNAs related to metabolism. LncRNA was more clearly described by univariate Cox regression. Moreover, lncRNA signatures were explored by multivariate Cox regression and lasso regression. The risk score was established according to the signature and it was an unattached prognostic marker according to Cox regression analysis. Functional enrichment of lncRNAs was shown by employing Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG). Univariate Cox retrospective analysis showed that 543 metabolism-related lncRNAs were independent prognostic factors of LGG, and multivariate Cox regression analysis confirmed that 19 metabolism-related lncRNAs were prognostic genes of LGG. In the risk model, the low-risk group had a higher Overall survival (OS) than the high-risk group (P < .001). Univariate Cox regression analysis of risk score and clinical factors showed that risk score was an independent prognostic factor (P < .001, HR = 1.047, 95% CI: 1.038-1.056). Multivariate Cox results showed that risk score could predict the prognosis of LGG (P < .001, HR = 1.036, 95% CI: 1.026-1.045). ROC curve analysis showed that risk score could predict the prognosis of LGG. The areas of 1-year, 3-years, and 5 years are 0.891, 0.904 and 0.832. GO and KEGG analysis showed that metabolism-related lncRNAs was mainly concentrated in the pathways related to tumor metabolism. In order to find a more stable and reliable target for the treatment of LGG, we established 19 metabolic-related lncRNAs prognostic model, and determined that it can predict the prognosis of LGG patients. This provides a new solution approach to the poor prognosis of patients with LGG and may reverse the trend of LGG's transformation to high-grade gliomas.

Qualification of Necroptosis-Related lncRNA to Forecast the Treatment Outcome, Immune Response, and Therapeutic Effect of Kidney Renal Clear Cell Carcinoma

Background

Kidney renal clear cell carcinoma (KIRC) is considered as a highly immune infiltrative tumor. Necroptosis is an inflammatory programmed cell death associated with a wide range of diseases. Long noncoding RNAs (lncRNAs) play important roles in gene regulation and immune function. lncRNA associated with necroptosis could systematically explore the prognostic value, regulate tumor microenvironment (TME), etc.

Method

The patients' data was collected from TCGA datasets. We used the univariate Cox regression (UCR) to select prediction lncRNAs that are related to necroptosis. Meanwhile, risk models were constructed using LASSO Cox regression (LCR). Kaplan–Meier (KM) analysis, accompanied with receiver operating characteristic (ROC) curves, was performed to assess the independent risk factors of different clinical characteristics. The evaluated factors are age, gender, disease staging, grade, and their related risk score. Databases such as Gene Ontology (GO), Kyoto encyclopedia of genes and genomes (KEGG), and Gene set enrichment analysis (GSEA) were used to search the probable biological characteristics that could influence the risk groups, containing signaling pathway and immue-related pathways. The single-sample gene set enrichment analysis (ssGSEA) was chosen to perform gene set variation analysis (GSVA), and the GSEABase package was selected to detect the immune and inflammatory infiltration profiles. The TIDE and IC₅₀ evaluation were used to estimate the effectiveness of clinical treatment on KIRC.

Results

Based on the above analysis, we have got a conclusion that patients who show high risk had higher immune infiltration, immune checkpoint expression, and poorer prognosis. We identified 19 novel prognostic necroptosis-related lncRNAs, which could offer opinions for a deeper study of KIRC.

Conclusion

The risk model we constructed makes it possible to predict the prognosis of KIRC patients and offers directions for further research on the prognostication and treatment strategies for KIRC.

Construction and Characterization of n6-Methyladenosine-Related lncRNA Prognostic Signature and Immune Cell Infiltration in Kidney Renal Clear Cell Carcinoma

Background. Kidney renal clear cell carcinoma (KIRC) lacks effective prognostic biomarkers and the role and mechanism of N6-methyladenosine (m6A) modification of long noncoding RNAs (lncRNAs) in KIRC remain unclear. Methods. We extracted standard mRNA-sequencing and clinical data from the TCGA database. The prognostic risk model was obtained by Lasso regression and Cox regression. We randomly divided the samples into training and test sets, each taking half of the cases. Based on Lasso regression and Cox regression for training set, the prognostic risk signature was constructed; risk scores were calculated with the R package “glmnet.” Based on the median value of the prognostic risk score, risk scores were calculated for each patient and we divided all KIRC samples into high-risk and low-risk groups. Then, high- and low-risk subtypes were established and their prognosis, clinical features, and immune infiltration microenvironment were evaluated in test set and the entire sampled data set. The reliability of the prognostic model was confirmed by receiver operating characteristic curve analysis. Results. We found 28 prognostic m6A-related lncRNAs and established a m6A-related lncRNAs prognostic signature. $\text{Risk\hspace{0.17em}score}=AC015813.1\ast \left(0.0086\right)+EMX2OS\ast \left(-0.0101\right)+LINC00173\ast \left(0.0309\right)+PWAR5\ast \left(-0.0146\right)+SNHG1\ast \left(0.0043\right).$ The signature showed a better predictive ability than other clinical indicators, including tumor node metastasis classification (TNM), histological, and pathological stages. In the high-risk group, M0 macrophages, CD8+ T cells, and regulatory T cells had significantly higher scores. Contrarily, in the low-risk group, activated dendritic cells, M1 macrophages, mast resting cells, and monocytes had significantly higher scores. In the high-risk group, LSECtin was overexpressed. In the low-risk group, PD-L1 was overexpressed. Moreover, high-risk patients may benefit more from AZ628. Conclusions. In conclusion, prognosis prediction of patients with KIRC and new insights for immunotherapy are provided by the m6A-related lncRNA prognostic signature.

A novel algorithm for lung adenocarcinoma based on N6 methyladenosine-related immune long noncoding RNAs as a reliable biomarker for predicting survival outcomes and selecting sensitive anti-tumor therapies

Background

Lung cancer is a highly heterogeneous malignant tumor with high incidence and mortality. Recently, increasing evidence has demonstrated that N6‐methyladenosine (m6A) methylation and the tumor microenvironment (TME) play important roles in the occurrence and development of lung adenocarcinoma (LUAD).

Methods

In this study, we constructed a novel and reliable algorithm based on m6A‐related immune lncRNAs (mrilncRNAs), consisting of molecular subtypes and a prognostic signature.

Results

According to the analyses of molecular subtypes, patients in cluster 1 were in a more advanced stage, showed poor prognosis, were sensitive to immunotherapy (anti‐programmed cell death 1 Ligand 1 (PD‐L1) and anti‐lymphocyte activating 3 (LAG‐3)), and had a highest tumor mutational burden (TMB), while anti‐cytotoxic T‐lymphocyte‐associated protein 4 (CTLA‐4) therapy seemed to be a good choice for patients in cluster 3. Subsequently, the results of the risk assessment model indicated that the low‐risk patients exhibited a survival advantage, had an earlier stage, and showed a higher response to common anti‐cancer drugs, including chemotherapy (Docetaxel, Paclitaxel), molecular targeted therapy (Erlotinib), and immunotherapy (anti‐CTLA‐4 therapy), while Gefitinib could be a good choice for patients with high‐risk scores.

Conclusion

In conclusion, the constructed algorithm exhibits promising practical prospects, and allows the selection of suitable and sensitive anti‐cancer drugs, which could provide theoretical support to predict the survival outcomes of patients with LUAD.

A hypoxia-related lncRNA model for prediction of head and neck squamous cell carcinoma prognosis

BACKGROUND

Head and neck squamous cell carcinoma (HNSCC) is one of the most common and highly heterogeneous malignancies worldwide. Increasing studies have proven that hypoxia and related long non-coding RNA (lncRNA) are involved in the occurrence and prognosis of HNSCC. The goal of this work is to construct a risk assessment model using hypoxia-related lncRNAs (hrlncRNAs) for HNSCC prognosis prediction and personalized treatment.

METHODS

Transcriptome expression matrix, clinical follow-up data, and somatic mutation data of HNSCC patients were obtained from The Cancer Genome Atlas (TCGA). We used co-expression analysis to identify hrlncRNAs, then screened for differentially expressed lncRNAs (DEhrlncRNAs), and paired these DEhrlncRNAs. The risk model was established through univariate, least absolute shrinkage and selection operator (LASSO), and stepwise multivariate Cox regression. Finally, we assessed the model from multiple perspectives of tumor mutation burden (TMB), tumor immune infiltration, chemotherapeutic sensitivity, immune checkpoint inhibitor (ICI), and functional enrichment.

RESULTS

The risk assessment model included 14 hrlncRNA pairs. The risk score was observed to be a reliable prognostic factor. The high-risk patients had an unfavorable prognosis and significant differences from the low-risk group in TMB and tumor immune infiltration. In the high-risk patients, the common immune checkpoints were down-regulated, including CTLA4 and PDCD1, and the sensibility to paclitaxel and docetaxel was higher. The functional enrichment analysis suggested that the low-risk group was accompanied by activated immune function.

CONCLUSIONS

The risk assessment model of 14-hrlncRNA-pairs demonstrated a promising prognostic prediction for HNSCC patients and can guide personalized clinical treatment.

A novel 7‑hypoxia‑related long non‑coding RNA signature associated with prognosis and proliferation in melanoma

Hypoxia‑related long non‑coding RNAs (lncRNAs) are important indicators of the poor prognosis of cancers. The present study aimed to explore the potential relationship between melanoma and hypoxia‑related lncRNAs. The transcriptome and clinical data of patients with melanoma were downloaded from The Cancer Genome Atlas database. The prognostic hypoxia‑related lncRNAs were screened out using Pearson's correlation test and univariate Cox analysis. As a result, a hypoxia‑related‑lncRNA signature based on the expression of 7 lncRNAs was constructed, with one unfavourable [MIR205 host gene (MIR205HG)] and six favourable (T cell receptor β variable 11‑2, HLA‑DQB1 antisense RNA 1, AL365361.1, AC004847.1, ubiquitin specific peptidase 30 antisense RNA 1 and AC022706.1) lncRNAs as prognostic factors for melanoma. Patients with melanoma were divided into high‑ and low‑risk groups based on the risk score obtained. Survival analyses were performed to assess the prognostic value of the present risk model. Potential tumour‑associated biological pathways associated with the present signature were explored using gene set enrichment analysis. The CIBERSORT algorithm demonstrated the important role of the hypoxia‑related lncRNAs in regulating tumour‑infiltrating immune cells. Clinical samples collected from our center partly confirmed our findings. Cell Counting Kit‑8 and flow cytometry assays indicated the suppression of proliferation of melanoma cells following inhibition of MIR205HG expression. Indicators of the canonical Wnt/β‑catenin signalling pathway were detected by western blotting. The present study demonstrated that MIR205HG could promote melanoma cell proliferation partly via the canonical Wnt/β‑catenin signalling pathway. These findings indicated a 7‑hypoxia‑related‑lncRNA signature that can serve as a novel predictor of melanoma prognosis.

Identifying the role of transient receptor potential channels (TRPs) in kidney renal clear cell carcinoma and their potential therapeutic significances using genomic and transcriptome analyses

Kidney renal clear cell carcinoma (KIRC) is among the major causes of cancer-caused mortality around the world. Transient receptor potential channels (TRPs), due to their role in various human diseases, might become potential drug targets in cancer. The mRNA expression, copy number variation, single-nucleotide variation, prognostic values, drug sensitivity, and pathway regulation of TRPs were studied across cancer types. The ArrayExpress and The Cancer Genome Atlas (TCGA) databases were used to retrieve KIRC samples. Simultaneously, training, internal, and external cohorts were grouped. In KIRC, a prognostic signature with superior survival prediction in contrast with other well-established signatures was created after a stepwise screening of optimized genes linked to TRPs using univariate Cox, weighted gene co-expression network analysis, multivariate Cox, and least absolute shrinkage and selection operator regression analyses. Subsequent to the determination of risk levels, the variations in the expression of immune checkpoint genes, tumor mutation burden, and immune subtypes and response between low-risk and high-risk subgroups were studied using a variety of bioinformatics algorithms, including ESTIMATE, XCELL, EPIC, CIBERSORT-ABS, CIBERSORT, MCPCOUNTER, TIMER, and QUANTISEQ. Gene set enrichment analysis helped in the identification of abnormal pathways across the low- and high-risk subgroups. Besides, high-risk KIRC patients might benefit from ABT888, AZD6244, AZD7762, Bosutinib, Camptothecin, CI1040, JNK inhibitor VIII, KU55933, Lenalidomide, Nilotinib, PLX4720, RO3306, Vinblastine, and ZM.447439; however, low-risk populations might benefit from Bicalutamide, FH535, and OSI906. Finally, calibration curves were used to validate the nomogram with a satisfactory predictive survival probability. In conclusion, this research provides useful insight that can aid and guide clinical practice and scientific research.

Identification and Verification of Immune Subtype-Related lncRNAs in Clear Cell Renal Cell Carcinoma

Background

According to clinical study results, immune checkpoint blockade (ICB) treatment enhances the survival outcome of patients with clear cell renal cell carcinoma (ccRCC). Previous research has divided ccRCC patients into immune subtypes with distinct ICB response rates. However, the study on the association between lncRNAs and ccRCC immune subtypes is lacking.

Methods

Differentially expressed lncRNAs/mRNAs between two major immune subgroups were calculated. A weighted gene co-expression network analysis (WGCNA) was conducted to establish the lncRNA-mRNA co-expression network and select the key lncRNAs. Then, prognostic lncRNAs were selected from the network by the bioinformatics method. Next, the risk-score was estimated by lncRNA expression and their coefficients. Finally, a nomogram based on lncRNAs and clinical parameters was created to predict the prognosis of ccRCC.

Results

LncRNAs and mRNAs associated with ccRCC immune subtypes were identified. The lncRNAs and mRNAs from a gene module closely linked to the immune subtype were used to construct a network. The KEGG pathways enriched in the network were related to immune system activation processes. These 8 lncRNAs (AL365361.1, LINC01934, AC090152.1, PCED1B-AS1, LINC00426, AC007728.2, AC243829.4, and LINC00158) were found to be positively correlated with immune cells of the tumor microenvironment. The C-index of the nomogram was 0.777, and the calibration curve data suggests that the nomogram has a high degree of discriminating capacity.

Conclusion

In summary, we discovered core lncRNAs linked with immune subtypes and created corresponding lncRNA–mRNA networks. These lncRNAs are anticipated to have predictive significance for ccRCC and may provide insight into novel biomarkers for the disease.

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.

Identification of a Methylation-Regulating Genes Prognostic Signature to Predict the Prognosis and Aid Immunotherapy of Clear Cell Renal Cell Carcinoma

Methylation is one of the most extensive modifications of biological macromolecules and affects cell-fate determination, development, aging, and cancer. Several methylation modifications, including 5-methylcytosine and N6-methyladenosine, play an essential role in many cancers. However, little is known about the relationship between methylation and the prognosis of clear cell renal cell carcinoma (ccRCC). Here, we established a methylation-regulating genes prognostic signature (MRGPS) to predict the prognoses of ccRCC patients. We obtained ccRCC samples from The Cancer Genome Atlas and identified methylation-regulatingd genes (MRGs) from the Gene Set Enrichment Analysis database. We also determined differentially expressed genes (DEGs) and performed cluster analysis to identify candidate genes. Subsequently, we established and validated an MRGPS to predict the overall survival of ccRCC patients. This was also verified in 15 ccRCC samples collected from the Fujian Provincial Hospital via quantitative real-time transcription (qRT-PCR). While 95 MRGs were differentially expressed (DEGs1) between tumor and normal tissues, 17 MRGs were differentially expressed (DEGs2) between cluster 1 and 2. Notably, 13 genes common among DEGs1 and DEGs2 were identified as hub genes. In fact, we established three genes (NOP2, NSUN6, and TET2) to be an MRGPS based on their multivariate Cox regression analysis coefficients (p &lt; 0.05). A receiver operating characteristic curve analysis confirmed this MRGPS to have a good prognostic performance. Moreover, the MRGPS was associated with characteristics of the tumor immune microenvironment and responses to inhibitor checkpoint inhibitors. Data from “IMvigor 210” demonstrated that patients with a low MRGPS would benefit more from atelozumab (p &lt; 0.05). Furthermore, a multivariate analysis revealed that MRGPS was an independent risk factor associated with ccRCC prognosis (p &lt; 0.05). Notably, a nomogram constructed by combining with clinical characteristics (age, grade, stage, and MRGPS risk score) to predict the overall survival of a ccRCC patient had a favorable predictive value. Eventually, our qRT-PCR results showed that tumor tissues had higher NOP2 and NSUN6 expression levels and lower TET2 expression than normal tissues of ccRCC samples. While the proposed MRGPS comprising NOP2, NSUN6, and TET2 can be an alternative prognostic biomarker for ccRCC patients, it is a promising index for personalized ICI treatments against ccRCC.

Identification of a Three-Glycolysis-Related lncRNA Signature Correlated With Prognosis and Metastasis in Clear Cell Renal Cell Carcinoma

The clear cell renal cell carcinoma (ccRCC) is not only a malignant disease but also an energy metabolic disease, we aimed to identify a novel prognostic model based on glycolysis-related long non-coding RNA (lncRNAs) and explore its mechanisms. With the use of Pearson correlation analysis between the glycolysis-related differentially expressed genes and lncRNAs from The Cancer Genome Atlas (TCGA) dataset, we identified three glycolysis-related lncRNAs and successfully constructed a prognostic model based on their expression. The diagnostic efficacy and the clinically predictive capacity of the signature were evaluated by univariate and multivariate Cox analyses, Kaplan–Meier survival analysis, and principal component analysis (PCA). The glycolysis-related lncRNA signature was constructed based on the expressions of AC009084.1, AC156455.1, and LINC00342. Patients were grouped into high- or low-risk groups according to risk score demonstrated significant differences in overall survival (OS) period, which were validated by patients with ccRCC from the International Cancer Genome Consortium (ICGC) database. Univariate Cox analyses, multivariate Cox analyses, and constructed nomogram-confirmed risk score based on our signature were independent prognosis predictors. The CIBERSORT algorithms demonstrated significant correlations between three-glycolysis-related lncRNAs and the tumor microenvironment (TME) components. Functional enrichment analysis demonstrated potential pathways and processes correlated with the risk model. Clinical samples validated expression levels of three-glycolysis-related lncRNAs, and LINC00342 demonstrated the most significant aberrant expression. in vitro, the general overexpression of LINC00342 was detected in ccRCC cells. After silencing LINC00342, the aberrant glycolytic levels and migration abilities in 786-O cells were decreased significantly, which might be explained by suppressed Wnt/β-catenin signaling pathway and reversed Epithelial mesenchymal transformation (EMT) process. Collectively, our research identified a novel three-glycolysis-related lncRNA signature as a promising model for generating accurate prognoses for patients with ccRCC, and silencing lncRNA LINC00342 from the signature could partly inhibit the glycolysis level and migration of ccRCC cells.

Identification of a competing endogenous RNA network related to immune signature in clear cell renal cell carcinoma

Clear cell renal cell carcinoma (ccRCC) is a fatal cancer of the urinary system. Long non-coding RNAs (lncRNAs) act as competitive endogenous RNAs (ceRNAs) involving the ccRCC progression. However, the relationship between the ceRNA network and immune signature is largely unknown. In this study, the ccRCC-related gene expression profiles retrieved from the TCGA database were used first to identify the differentially expressed genes through differential gene expression analysis and weighted gene co-expression network analysis. The interaction among differentially expressed lncRNAs, miRNAs, and mRNAs were matched using public databases. As a result, a ceRNA network was developed that contained 144 lncRNAs, 23 miRNAs, as well as 62 mRNAs. Four of 144 lncRNAs including LINC00943, SRD5A3-AS1, LINC02345, and U62317.3 were identified through LASSO regression and Cox regression analyses, and were used to create a prognostic risk model. Then, the ccRCC samples were divided into the high- and low-risk groups depending on their risk scores. ROC curves, Kaplan-Meier survival analysis, and the survival risk plots indicated that the predictive performance of our developed risk model was accurate. Moreover, the CIBERSORT algorithm was used to measure the infiltration levels of immune cells in the ccRCC samples. The further genomic analysis illustrated a positive correlation between most immune checkpoint blockade-related genes and the risk score. In conclusion, the present findings effectually contribute to the comprehensive understanding of the ccRCC pathogenesis, and may offer a reference for developing novel therapeutic and prognostic biomarkers.

Distinct Hypoxia-Related Gene Profiling Characterizes Clinicopathological Features and Immune Status of Mismatch Repair-Deficient Colon Cancer

Despite dramatic responses to immune checkpoint inhibitors (ICIs) in patients with colon cancer (CC) harboring deficient mismatch repair (dMMR), more than half of these patients ultimately progress and experience primary or secondary drug resistance. There is no useful biomarker that is currently validated to accurately predict this resistance or stratify patients who may benefit from ICI-based immunotherapy. As hypoxic and acidic tumor microenvironment would greatly impair tumor-suppressing functions of tumor-infiltrating lymphocytes (TILs), we sought to explore distinct immunological phenotypes by analysis of the intratumoral hypoxia state using a well-established gene signature. Based on the Gene Expression Omnibus (GEO) (n = 88) and The Cancer Genome Atlas (TCGA) (n = 49) databases of patients with CC, we found that dMMR CC patients could be separated into normoxia subgroup (NS) and hypoxia subgroup (HS) with different levels of expression of hypoxia-related genes (lower in NS group and higher in HS group) using NMF package. Tumoral parenchyma in the HS group had a relatively lower level of immune cell infiltration, particularly CD8+ T cells and M1 macrophages than the NS group, and coincided with higher expression of immune checkpoint molecules and C-X-C motif chemokines, which might be associated with ICI resistance and prognosis. Furthermore, three genes, namely, MT1E, MT2A, and MAFF, were identified to be differentially expressed between NS and HS groups in both GEO and TCGA cohorts. Based on these genes, a prognostic model with stable and valuable predicting ability has been built for clinical application. In conclusion, the varying tumor-immune microenvironment (TIME) classified by hypoxia-related genes might be closely associated with different therapeutic responses of ICIs and prognosis of dMMR CC patients.

Identification and validation of a hypoxia-related prognostic signature in clear cell renal cell carcinoma patients

Increasing evidence has shown that hypoxia is closely related to the development, progression, and prognosis of clear cell renal cell carcinoma (ccRCC). Nevertheless, reliable prognostic signatures based on hypoxia have not been well-established. This study aimed to establish a hypoxia-related prognostic signature and construct an optimized nomogram for patients with ccRCC.We accessed hallmark gene sets of hypoxia, including 200 genes, and an original RNA seq dataset of ccRCC cases with integrated clinical information obtained by mining the Cancer Genome Atlas database and the International Cancer Genome Consortium (ICGC) database. Univariate Cox regression analysis and multivariate Cox proportional hazards regression were performed to identify prognostic hub genes and further established prognostic model as well as visualized the nomogram. External validation of the optimized nomogram was performed in independent cohorts from the ICGC database.ANKZF1, ETS1, PLAUR, SERPINE1, FBP1, and PFKP were selected as prognostic hypoxia-related hub genes, and the prognostic model effectively distinguishes high-risk and low-risk patients with ccRCC. The results of receiver operating characteristic curve, risk plots, survival analysis, and independent analysis suggested that RiskScore was a useful tool and independent predictive factor. A novel prognosis nomogram optimized via RiskScore showed its promising performance in both the Cancer Genome Atlas-ccRCC cohort and an ICGC-ccRCC cohort.Our study reveals that the differential expressions of hypoxia-related genes are associated with the overall survival of patients with ccRCC. The prognostic model we established showed a good predictive and discerning ability in ccRCC patients. The novel nomogram optimized via RiskScore exhibited a promising predictive ability. It may be able to serve as a visualized tool for guiding clinical decisions and selecting effective individualized treatments.