• IRGPs
• bioinformatics
• diffuse large B-cell lymphoma
• immune-related signature
• tumor immune microenvironment

• Humans
• Lymphoma, Large B-Cell, Diffuse/genetics
• Lymphoma, Large B-Cell, Diffuse/immunology
• Lymphoma, Large B-Cell, Diffuse/therapy
• Computational Biology/methods
• Prognosis
• Gene Expression Profiling/methods
• Male
• Female
• Gene Expression Regulation, Neoplastic
• Middle Aged
• Biomarkers, Tumor/genetics
• Transcriptome
• Aged

• Gastric cancer
• Immune cell infiltration
• Immunotherapy
• P53-related lncRNAs
• Tumor microenvironment
• lncRNA signature
• m5C
• m6A

• acute myeloid leukemia
• competing endogenous RNA network
• immune-related gene
• prognostic signature

• Humans
• Leukemia, Myeloid, Acute/genetics
• Leukemia, Myeloid, Acute/immunology
• RNA, Long Noncoding/genetics
• MicroRNAs/genetics
• RNA, Messenger/genetics
• Prognosis
• Gene Regulatory Networks
• Biomarkers, Tumor/genetics
• Gene Expression Profiling
• Gene Expression Regulation, Leukemic
• Transcriptome
• Tumor Microenvironment/genetics
• Tumor Microenvironment/immunology
• RNA, Competitive Endogenous

• Key Discipline Construction
• Guangdong Medical University
• National Undergraduate Training Program

• drug sensitivities
• immune infiltration
• necroptosis
• prognostic signature
• stomach adenocarcinoma

• Humans
• Stomach Neoplasms/genetics
• Stomach Neoplasms/immunology
• Stomach Neoplasms/pathology
• Tumor Microenvironment/immunology
• Tumor Microenvironment/genetics
• RNA, Long Noncoding/genetics
• RNA, Long Noncoding/metabolism
• Adenocarcinoma/genetics
• Adenocarcinoma/immunology
• Adenocarcinoma/pathology
• Adenocarcinoma/drug therapy
• Prognosis
• Necroptosis/genetics
• Gene Expression Regulation, Neoplastic
• Male
• Female
• Drug Resistance, Neoplasm/genetics
• Nomograms
• Biomarkers, Tumor/genetics
• Middle Aged
• Kaplan-Meier Estimate

• Glioblastoma multiforme
• Immune infiltration
• Immunity
• LncRNAs
• Radiomics

• Adult
• Humans
• Glioblastoma/diagnostic imaging
• Glioblastoma/genetics
• RNA, Long Noncoding/genetics
• Radiomics
• Prognosis
• Area Under Curve
• Tumor Microenvironment/genetics

• 82271953 National Natural Science Foundation of China
• 81971585 National Natural Science Foundation of China
• 61976110 National Natural Science Foundation of China
• 202103010001 Guangzhou Science and Technology Planning Project
• 2022-1-2031 Capital's Funds for Health Improvement and Research
• Z211100003521009 Beijing Municipal Science and Technology Project
• Capital’s Funds for Health Improvement and Research

• Glioblastoma (GBM)
• TCGA
• immune-related lncRNA
• risk model

• Humans
• Glioblastoma/genetics
• RNA, Long Noncoding/genetics
• Cell Transformation, Neoplastic
• Immunosuppressive Agents
• Levamisole
• Biomarkers
• Prognosis
• Gene Expression Regulation, Neoplastic/genetics

• Deep learning
• Esophageal cancer
• Immune cell
• LncRNA
• Prognosis

• Humans
• RNA, Long Noncoding/genetics
• Esophageal Neoplasms/genetics
• Biomarkers, Tumor/genetics
• Gene Expression Regulation, Neoplastic
• Tumor Microenvironment

• Genome instability
• Immune checkpoint
• Immunotherapy
• LncRNA
• Pancreatic adenocarcinoma

• PRLPM riskscore
• chemotherapy
• immunotherapy
• pyroptosis-related LncRNA
• tumor immune microenvironment

• Bioinformatics
• Gaussian finite mixture model
• Machine learning
• Pancreatic cancer
• Prognosis
• RNA-seq

• Lung squamous cell carcinoma
• immune subtypes
• overall survival
• tumor environment

• Humans
• Carcinoma, Non-Small-Cell Lung
• Carcinoma, Squamous Cell/genetics
• Lung Neoplasms/genetics
• Databases, Factual
• Lung
• Prognosis

• ferroptosis
• immune infiltration
• long non‑coding RNA
• pancreatic adenocarcinoma
• signature

• Humans
• Pancreatic Neoplasms/diagnosis
• Pancreatic Neoplasms/genetics
• Adenocarcinoma
• Prognosis
• RNA, Long Noncoding/genetics
• Nerve Tissue Proteins/genetics
• Tumor Microenvironment

• KiKme Study
• NGS - next generation sequencing
• RNA-Seq
• differential gene expression analysis
• radiation experiments
• radiation response
• weighted co-expression network analysis (WGCNA)

• Bundesministerium für Bildung und Forschung

• XGBoost
• diagnostic
• digestive system tumors
• immunologic gene set
• prognostic

• National Natural Science Foundation of China

• AP
• autophagy
• bioinformatics analysis
• ceRNA
• lncRNA
• m6A modification

• Acute Disease
• Autophagy/genetics
• Gene Regulatory Networks
• Humans
• MicroRNAs/genetics
• MicroRNAs/metabolism
• Pancreatitis/genetics
• Phosphatidylinositol 3-Kinase/genetics
• Phosphatidylinositol 3-Kinases/genetics
• Proto-Oncogene Proteins c-akt
• RNA, Long Noncoding/genetics
• RNA, Long Noncoding/metabolism
• RNA, Messenger/genetics
• RNA, Messenger/metabolism

• National Natural Science Foundation of China
• National Key Research and Development Program of China
• Science and Technology Program of Hunan Province
• Natural Science Foundation of Hunan Province

Background: Cancer-associated fibroblasts (CAFs) are an essential cell population in the pancreatic cancer tumor microenvironment and are extensively involved in drug resistance and immune evasion mechanisms. Long non-coding RNAs (lncRNAs) are involved in pancreatic cancer evolution and regulate the biological behavior mediated by CAFs. However, there is a lack of understanding of the prognostic signatures of CAFs-associated lncRNAs in pancreatic cancer patients.

Methods: Transcriptomic and clinical data for pancreatic adenocarcinoma (PAAD) and the corresponding mutation data were obtained from The Cancer Genome Atlas database. lncRNAs associated with CAFs were obtained using co-expression analysis. lncRNAs were screened by Cox regression analysis using least absolute shrinkage and selection operator (LASSO) algorithm for constructing predictive signature. According to the prognostic model, PAAD patients were divided into high-risk and low-risk groups. Kaplan-Meier analysis was used for survival validation of the model in the training and validation groups. Clinicopathological parameter correlation analysis, univariate and multivariate Cox regression, time-dependent receiver operating characteristic (ROC) curves, and nomogram were performed to evaluate the model. The gene set variation analysis (GSVA) and gene ontology (GO) analyses were used to explore differences in the biological behavior of the risk groups. Furthermore, single-sample gene set enrichment analysis (ssGSEA), tumor mutation burden (TMB), ESTIMATE algorithm, and a series of immune correlation analyses were performed to investigate the relationship between predictive signature and the tumor immune microenvironment and screen for potential responders to immune checkpoint inhibitors. Finally, drug sensitivity analyses were used to explore potentially effective drugs in high- and low-risk groups.

Results: The signature was constructed with seven CAFs-related lncRNAs (AP005233.2, AC090114.2, DCST1-AS1, AC092171.5, AC002401.4, AC025048.4, and CASC8) that independently predicted the prognosis of PAAD patients. Additionally, the high-risk group of the model had higher TMB levels than the low-risk group. Immune correlation analysis showed that most immune cells, including CD8⁺ T cells, were negatively correlated with the model risk scores. ssGSEA and ESTIMATE analyses further indicated that the low-risk group had a higher status of immune cell infiltration. Meanwhile, the mRNA of most immune checkpoint genes, including PD1 and CTLA4, were highly expressed in the low-risk group, suggesting that this population may be “hot immune tumors” and have a higher sensitivity to immune checkpoint inhibitors (ICIs). Finally, the predicted half-maximal inhibitory concentrations of some chemical and targeted drugs differ between high- and low-risk groups, providing a basis for treatment selection.

Conclusion: Our findings provide promising insights into lncRNAs associated with CAFs in PAAD and provide a personalized tool for predicting patient prognosis and immune microenvironmental landscape.

• colon cancer
• immune
• immune checkpoint inhibitors
• long noncoding rna
• prognostic model

• Biomarkers, Tumor/genetics
• Colonic Neoplasms/genetics
• Colonic Neoplasms/pathology
• Gene Expression Regulation, Neoplastic
• Humans
• Prognosis
• RNA, Long Noncoding/genetics

• adrenocortical carcinoma
• immunology
• prognosis
• signatures
• tumor microenvironment

• Adrenal Cortex Neoplasms/genetics
• Adrenal Cortex Neoplasms/metabolism
• Adrenal Cortex Neoplasms/pathology
• Adrenocortical Carcinoma/genetics
• Adrenocortical Carcinoma/metabolism
• Adrenocortical Carcinoma/pathology
• Biomarkers, Tumor/genetics
• Gene Expression Regulation, Neoplastic
• Humans
• Prognosis
• Tumor Microenvironment/genetics

• Science and Technology Commission of Shanghai Municipality
• Shanghai Hospital Development Center

Disclosing prognostic information is necessary to enable good treatment selection and improve patient outcomes. Previous studies suggest that hypoxia is associated with an adverse prognosis in patients with HNSCC and that long non-coding RNAs (lncRNAs) show functions in hypoxia-associated cancer biology. Nevertheless, the understanding of lncRNAs in hypoxia related HNSCC progression remains confusing.

Data were downloaded from TCGA and GEO database. Bioinformatic tools including R packages GEOquery, limma, pheatmap, ggplot2, clusterProfiler, survivalROC and survcomp and LASSO cox analysis were utilized. Si-RNA transfection, CCK8 and real-time quantified PCR were used in functional study.

GEO data (GSE182734) revealed that lncRNA regulation may be important in hypoxia related response of HNSCC cell lines. Further analysis in TCGA data identified 314 HRLs via coexpression analysis between differentially expressed lncRNAs and hypoxia-related mRNAs. 23 HRLs were selected to build the prognosis predicting model using lasso Cox regression analyses. Our model showed excellent performance in predicting survival outcomes among patients with HNSCC in both the training and validation sets. We also found that the risk scores were related to tumor stage and to tumor immune infiltration. Moreover, LINC01116 were selected as a functional study target. The knockdown of LINC01116 significantly inhibited the proliferation of HNSCC cells and effected the hypoxia induced immune and the NF-κB/AKT signaling.

Data analysis of large cohorts and functional experimental validation in our study suggest that hypoxia related lncRNAs play an important role in the progression of HNSCC, and its expression model can be used for prognostic prediction.

•

NcRNAs regulations showed significance in hypoxia related response in HNSCC.

Ferroptosis is a more relatively recently identified type of programmed cell death, which is associated with tumor progression. However, the mechanism underlying the effect of ferroptosis-related long non-coding RNAs (lncRNAs) in lung adenocarcinoma (LUAD) remains elusive. Therefore, the current study aimed to investigate the role of ferroptosis-related lncRNAs in LUAD and to develop a prognostic model. The clinicopathological characteristics of patients and the gene sequencing data were obtained from The Cancer Genome Atlas, while the ferroptosis-associated mRNAs were downloaded from the FerrDb database. A ferroptosis-related lncRNA signature was established with Least Absolute Shrinkage and Selection Operator Cox regression analysis. Furthermore, the risk scores of ferroptosis-related lncRNAs were calculated and LUAD patients were then assigned to high- and low-risk groups based on the median risk score. The prognostic model was established by K-M plotters and nomograms. Gene set enrichment analysis (GSEA) was performed to evaluate the association between immune responses and ferroptosis-related lncRNAs. A total of 10 ferroptosis-related lncRNAs were identified as independent predictors of LUAD outcome, namely RP11-386M24.3, LINC00592, FENDRR, AC104699.1, AC091132.1, LANCL1-AS1, LINC-PINT, IFNG-AS1, LINC00968 and AC006129.2. The area under the curve verified that the established signatures could determine LUAD prognosis. The nomogram model was used to assess the predictive accuracy of the established signatures. Additionally, GSEA revealed that the 10 ferroptosis-related lncRNAs could be involved in immune responses in LUAD. Overall, the results of the current study may provide novel insights into the development of novel therapies or diagnostic strategies for LUAD.

• Adenocarcinoma/genetics
• Ferroptosis/genetics
• Gene Expression Regulation, Neoplastic
• Humans
• Lung/pathology
• Prognosis
• RNA, Long Noncoding/genetics
• RNA, Long Noncoding/metabolism
• Tumor Microenvironment/genetics

• Gene Expression Regulation, Neoplastic
• Humans
• Prognosis
• RNA, Long Noncoding/genetics
• Reproducibility of Results
• Stomach Neoplasms/genetics
• Stomach Neoplasms/therapy
• Tumor Microenvironment/genetics

• Autophagy/genetics
• Gene Expression Regulation, Neoplastic
• Humans
• Pancreatic Neoplasms/genetics
• Prognosis
• RNA, Long Noncoding/genetics
• Pancreatic Neoplasms

• Bioinformatics analysis
• Ovarian cancer
• Prognosis
• TCGA
• microRNA signature

Colon adenocarcinoma (COAD) is one of the most common malignant tumors and is diagnosed at an advanced stage with a poor prognosis worldwide. Pyroptosis is involved in the initiation and progression of tumors. This research focused on constructing a pyroptosis-related ceRNA network to generate a reliable risk model for risk prediction and immune infiltration analysis of COAD.

Transcriptome data, miRNA-sequencing data, and clinical information were downloaded from the TCGA database. First, differentially expressed mRNAs (DEmRNAs), miRNAs (DEmiRNAs), and lncRNAs (DElncRNAs) were identified to construct a pyroptosis-related ceRNA network. Second, a pyroptosis-related lncRNA risk model was developed applying univariate Cox regression analysis and least absolute shrinkage and selection operator method (LASSO) regression analysis. Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Ontology (GO) enrichment analyses were utilized to functionally annotate RNAs contained in the ceRNA network. In addition, Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, univariate and multivariate Cox regression, and nomogram were applied to validate this risk model. Finally, the relationship of this risk model with immune cells and immune checkpoint blockade (ICB)-related genes was analyzed.

A total of 5373 DEmRNAs, 1159 DElncRNAs, and 355 DEmiRNAs were identified. A pyroptosis-related ceRNA regulatory network containing 132 lncRNAs, 7 miRNAs, and 5 mRNAs was constructed, and a ceRNA-based pyroptosis-related risk model including 11 lncRNAs was built. The tumor tissues were classified into high- and low-risk groups according to the median risk score. Kaplan-Meier analysis showed that the high-risk group had a shorter survival time; ROC analysis, independent prognostic analysis, and nomogram further indicated the risk model was a significant independent prognostic factor what had an excellent ability to predict patients’ risk. Moreover, immune infiltration analysis indicated that the risk model was related to immune infiltration cells (i.e., B cell naïve, T cell follicular helper, macrophage M1) and ICB-related genes (i.e., PD-1, CTLA4, HAVCR2).

This pyroptosis-related lncRNA risk model possessed good prognostic value, and the ability to predict the outcome of ICB immunotherapy in COAD.

The online version contains supplementary material available at 10.1186/s12957-022-02572-8.

• Colon adenocarcinoma
• Immunotherapy
• Pyroptosis
• Tumor immune microenvironment
• ceRNA network

An increasing number of studies have shown that immune-related long noncoding RNAs (lncRNAs) do not require a unique expression level. This finding may help predict the survival and drug sensitivity of patients with colon cancer.

We retrieved original transcriptome and clinical data from The Cancer Genome Atlas (TCGA), sorted the data, differentiated mRNAs and lncRNAs, and then downloaded immune-related genes. Coexpression analysis predicted immune-related lncRNAs (irlncRNAs) and univariate analysis identified differentially expressed irlncRNAs (DEirlncRNAs). We have also amended the lasso pending region. Next, we compared the areas under the curve (AUCs), counted the Akaike information standard (AIC) value of the 3-year receiver operating characteristic (ROC) curve, and determined the cutoff point to establish the best model to differentiate the high or low disease risk group of colon cancer patients.

We reevaluated the patients regarding the survival rate, clinicopathological features, tumor-infiltrating immune cells, immunosuppressive biomarkers, and chemosensitivity. A total of 155 irlncRNA pairs were confirmed, 31 of which were involved in the Cox regression model. After the colon cancer patients were regrouped according to the cutoff point, we could better distinguish the patients based on adverse survival outcomes, invasive clinicopathological features, the specific tumor immune cell infiltration status, high expression of immunosuppressive biomarkers, and low chemosensitivity.

In this study, we established a characteristic model by pairing irlncRNAs to better predict the survival rate, chemotherapy efficacy, and prognostic value of patients with colon cancer.

The online version contains supplementary material available at 10.1186/s12957-022-02508-2.

Background: Long non-coding RNAs (lncRNAs) are key regulators of pancreatic cancer development and are involved in ferroptosis regulation. LncRNA transcript levels serve as a prognostic factor for pancreatic cancer. Therefore, identifying ferroptosis-related lncRNAs (FRLs) with prognostic value in pancreatic cancer is critical.

Methods: In this study, FRLs were identified by combining The Cancer Genome Atlas (TCGA) and FerrDb databases. For training cohort, univariate Cox, Lasso, and multivariate Cox regression analyses were applied to identify prognosis FRLs and then construct a prognostic FRLs signature. Testing cohort and entire cohort were applied to validate the prognostic signature. Moreover, the nomogram was performed to predict prognosis at different clinicopathological stages and risk scores. A co-expression network with 76 lncRNA-mRNA targets was constructed.

Results: Univariate Cox analysis was performed to analyze the prognostic value of 193 lncRNAs. Furthermore, the least absolute shrinkage and selection operator and the multivariate Cox analysis were used to assess the prognostic value of these ferroptosis-related lncRNAs. A prognostic risk model, of six lncRNAs, including LINC01705, AC068620.2, TRAF3IP2-AS1, AC092171.2, AC099850.3, and MIR193BHG was constructed. The Kaplan Meier (KM) and time-related receiver operating characteristic (ROC) curve analysis were performed to calculate overall survival and compare high- and low-risk groups. There was also a significant difference in survival time between the high-risk and low-risk groups for the testing cohort and the entire cohort, with AUCs of .723, .753, respectively. Combined with clinicopathological characteristics, the risk model was validated as a new independent prognostic factor for pancreatic adenocarcinoma through univariate and multivariate Cox regression. Moreover, a nomogram showed good prediction.

Conclusion: The signature of six FRLs had significant prognostic value for pancreatic adenocarcinoma. They may be a promising therapeutic target in clinical practice.

• cancer metabolism
• ferroptosis
• immune microenvironment
• lncRNA
• pancreatic cancer

• Carcinoma, Pancreatic Ductal/drug therapy
• Carcinoma, Pancreatic Ductal/genetics
• Carcinoma, Pancreatic Ductal/metabolism
• China
• Ferroptosis/genetics
• Humans
• Pancreatic Neoplasms/drug therapy
• Pancreatic Neoplasms/genetics
• Pancreatic Neoplasms/metabolism
• RNA, Long Noncoding/genetics
• RNA, Long Noncoding/metabolism
• Tumor Microenvironment

Patients with small-cell lung cancer (SCLC) are burdened by limited treatment options and the disease’s dismal prognosis. Long non-coding RNAs (lncRNAs) are essential regulators of genetic alteration and are actively involved in tumor immunity. However, few studies have examined interactions between immune genes and lncRNAs in SCLC.

Immune-related lncRNA (irlncRNA) expression profiles and their clinical significance were explored. We enrolled 227 patients with SCLC, including 79 cases from GSE65002 and 148 cases from a validation cohort with corresponding qPCR data. The least absolute shrinkage and selection operator (LASSO) model was applied to identify prognostic irlncRNAs for an irlncRNA-based SCLC signature. We additionally investigated the potential mechanisms and immune landscape of the signature using bioinformatics methods.

An irlncRNA signature including 8 irlncRNAs (ENOX1-AS1, AC005162, LINC00092, RPL34-AS1, AC104135, AC015971, AC126544, AP001189) was established for patients with SCLC in the training cohort. Low-risk patients were more likely to benefit from chemotherapy and achieve a favorable prognosis. The signature was also well-validated in the validation cohort and various clinical subgroups. Compared to other clinical parameters, the irlncRNA signature exhibited superior predictive performance for chemotherapy response and prognosis. The signature was as an independent prognostic factor in the training and validation cohorts. Interestingly, low-risk patients showed an activated immune phenotype.

We constructed the first irlncRNA-based signature for chemotherapy efficacy and outcome prediction. The irlncRNA signature is a reliable and robust prognostic classifier that could be useful for clinical management and determination of potential chemotherapy benefit for patients with SCLC.

The online version contains supplementary material available at 10.1186/s12935-021-02357-1.

Growing evidence has shown that the prognosis for colon cancer depends on changes in microenvironment. The purpose of this study was to elucidate the prognostic value of long noncoding RNAs (lncRNAs) related to immune microenvironment (IM) in colon cancer.

Single sample gene set enrichment analysis (ssGSEA) was used to identify the subtypes of colon cancer based on the immune genomes of 29 immune signatures. Cox regression analysis identified a lncRNA signatures associated with immune infiltration. The Tumor Immune Estimation Resource database was used to analyze immune cell content.

Colon cancer samples were divided into three subtypes by unsupervised cluster analysis. Cox regression analysis identified an immune infiltration-related 5-lncRNA signature. This signature combined with clinical factors can effectively improve the predictive ability for the overall survival (OS) of colon cancer. At the same time, we found that the expression of H19 affects the content of B cells and macrophages in the microenvironment of colon cancer and affects the prognosis of colon cancer. Finally, we constructed the H19 regulatory network and further analyzed the possible mechanisms. We found that knocking down the expression of H19 can significantly inhibit the expression of CCND1 and VEGFA. At the same time, the immunohistochemical assay found that the expression of CCND1 and VEGFA protein was significantly positively correlated with the infiltration of M2 type macrophages.

The findings may help to formulate clinical strategies and understand the underlying mechanisms of H19 regulation. H19 may be a biomarker for targeted treatment of colon cancer.

The online version contains supplementary material available at 10.1186/s12935-021-02342-8.

• Colon cancer
• H19
• Immune infiltration
• Noncoding RNA
• Survival
• Tumor microenvironment

• PDAC
• lncRNA
• m5C methylation
• prognostic model
• tumor immune microenvironment

• 5-Methylcytosine/metabolism
• Carcinoma, Pancreatic Ductal/genetics
• Carcinoma, Pancreatic Ductal/immunology
• Humans
• Pancreatic Neoplasms/genetics
• Pancreatic Neoplasms/immunology
• Prognosis
• RNA Processing, Post-Transcriptional
• RNA, Long Noncoding/genetics
• Risk Factors
• Tumor Microenvironment/genetics
• Tumor Microenvironment/immunology

• National Natural Science Foundation of China

• Cervical cancer
• immune
• long non-coding RNA (lncRNA)
• signature
• survival

• Immune-related lncRNA
• immune infiltration
• pancreatic cancer
• random survival forest
• risk model

• Algorithms
• Computational Biology/methods
• Female
• Humans
• Male
• Models, Statistical
• Pancreatic Neoplasms/classification
• Pancreatic Neoplasms/genetics
• Pancreatic Neoplasms/immunology
• Pancreatic Neoplasms/mortality
• Prognosis
• RNA, Long Noncoding/genetics
• RNA, Long Noncoding/metabolism
• Risk
• Transcriptome/genetics
• Tumor Microenvironment/genetics
• Tumor Microenvironment/immunology

Lung cancer remains the leading cause of cancer death globally, with lung adenocarcinoma (LUAD) being its most prevalent subtype. Due to the heterogeneity of LUAD, patients given the same treatment regimen may have different responses and clinical outcomes. Therefore, identifying new subtypes of LUAD is important for predicting prognosis and providing personalized treatment for patients. Pyroptosis-related genes play an essential role in anticancer, but there is limited research investigating pyroptosis in LUAD. In this study, 33 pyroptosis gene expression profiles and clinical information were collected from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. By bioinformatics and machine learning analyses, we identified novel subtypes of LUAD based on 10 pyroptosis-related genes and further validated them in the GEO dataset, with machine learning models performing up to an AUC of 1 for classifying in GEO. A web-based tool was established for clinicians to use our clustering model (http://www.aimedicallab.com/tool/aiml-subphe-luad.html). LUAD patients were clustered into 3 subtypes (A, B, and C), and survival analysis showed that B had the best survival outcome and C had the worst survival outcome. The relationships between pyroptosis gene expression and clinical characteristics were further analyzed in the three molecular subtypes. Immune profiling revealed significant differences in immune cell infiltration among the three molecular subtypes. GO enrichment and KEGG pathway analyses were performed based on the differential genes of the three subtypes, indicating that differentially expressed genes (DEGs) were involved in multiple cellular and biological functions, including RNA catabolic process, mRNA catabolic process, and pathways of neurodegeneration-multiple diseases. Finally, we developed an 8-gene prognostic model that accurately predicted 1-, 3-, and 5-year overall survival. In conclusion, pyroptosis-related genes may play a critical role in LUAD, and provide new insights into the underlying mechanisms of LUAD.

• lung adenocarcinoma
• machine learning
• prognostic
• pyroptosis
• subtype

• cancer immunity
• gastric cancer
• immunosuppressive microenvironment
• lncRNA signature
• overall survival

The tumor microenvironment (TME) is not only a key factor in the malignant progression of cancer but also plays an indispensable role in tumor immunotherapy. As an important regulatory factor in the TME, long non-coding RNAs (incRNA) are important for the development of bladder cancer. The purpose of this study was to explore the molecular mechanism of malignant progression of bladder cancer (BCa) from the perspective of immunology, establish a reliable signature, and evaluate its effect on prognosis, metastasis, and the effectiveness of immunotherapy.

The TME was assessed by single-sample gene set enrichment analysis (ssGSEA) in 373 patients with muscle invasive bladder cancer (MIBC) in The Cancer Genome Atlas (TCGA). Combining RNA sequence data from 49 BCa patients in our center, we established TME-related prognostic signatures (TMERPS) based on TME-related immune prognosis genes using weighted gene correlation network analysis, selection operator Cox analysis, minimum absolute shrinkage, and survival analysis. Real-Time Quantitative PCR was used for expression level analysis of related genes. Functional enrichment analysis and nomograms were used to explore the potential impact of TMERPS on the immune system, prognosis, and metastasis.

The ssGSEA proved to be an accurate assessment of immune levels in BCa samples. TMERPS was established based on six TME-associated prognostic lncRNAs and was shown to be closely associated with prognosis, metastasis, and immune levels, and to have a significant stratifying effect on the therapeutic efficacy of immune checkpoint inhibitors. Finally, three TMERPS-based nomograms were shown to be effective in predicting prognosis, lymph node metastasis, and distant metastasis in BCa patients.

TMERPS can stratify BCa patients into different risk groups with different prognoses, immunotherapy sensitivity, and risk of metastasis. TMERPS-based nomograms can effectively predict prognosis and metastasis in BCa patients.

• Tumor immune microenvironment
• distant metastasis
• long non-coding RNA (lncRNA)
• lymph node metastasis
• muscle invasion bladder cancer (MIBC)

• bioinformatics analysis
• corticosteroid resistance
• hub genes
• human airway epithelial cells
• microarray analysis
• scopoletin

Papillary renal cell carcinoma (pRCC) ranks second in renal cell carcinoma and the prognosis of pRCC remains poor. Here, we aimed to screen and identify a novel prognostic cancer-related lncRNA signature in pRCC.

The RNA-seq profile and clinical feature of pRCC cases were downloaded from TCGA database. Significant cancer-related lncRNAs were obtained from the Immlnc database. Differentially expressed cancer-related lncRNAs (DECRLs) in pRCC were screened for further analysis. Cox regression report was implemented to identify prognostic cancer-related lncRNAs and establish a prognostic risk model, and ROC curve analysis was used to evaluate its precision. The correlation between RP11-63A11.1 and clinical characteristics was further analyzed. Finally, the expression level and role of RP11-63A11.1 were studied in vitro.

A total of 367 DECRLs were finally screened and 26 prognostic cancer-related lncRNAs were identified. Among them, ten lncRNAs (RP11-573D15.8, LINC01317, RNF144A-AS1, TFAP2A-AS1, LINC00702, GAS6-AS1, RP11-400K9.4, LUCAT1, RP11-63A11.1, and RP11-156L14.1) were independently associated with prognosis of pRCC. These ten lncRNAs were incorporated into a prognostic risk model. In accordance with the median value of the riskscore, pRCC cases were separated into high and low risk groups. Survival analysis indicated that there was a significant difference on overall survival (OS) rate between the two groups. The area under curve (AUC) in different years indicated that the model was of high efficiency in prognosis prediction. RP11-63A11.1 was mainly expressed in renal tissues and it correlated with the tumor stage, T, M, N classifications, OS, PFS, and DSS of pRCC patients. Consistent with the expression in pRCC tissue samples, RP11-63A11.1 was also down-regulated in pRCC cells. More importantly, up-regulation of RP11-63A11.1 attenuated cell survival and induced apoptosis.

Ten cancer-related lncRNAs were incorporated into a powerful model for prognosis evaluation. RP11-63A11.1 functioned as a cancer suppressor in pRCC and it might be a potential therapeutic target for treating pRCC.

The online version contains supplementary material available at 10.1186/s12935-021-02247-6.