Prediction of prognosis and immunotherapy response with a robust immune-related lncRNA pair signature in lung adenocarcinoma

Integrating bulk, single-cell, and spatial transcriptomics to identify and functionally validate novel targets to enhance immunotherapy in NSCLC

Programmed cell deaths (PCDs) are crucial for tumor progression. By analyzing 18 PCDs, we generated a robust multigene signature, Combined Cell Death Index (CCDI), comprising necroptosis and autophagy genes for non-small cell lung cancer (NSCLC). The CCDI accurately stratified patients by survival prognosis and predicted immunotherapy responses. We validated CCDI and prioritized CCDI genes using five single-cell RNA sequencing and two spatial transcriptomics datasets. CCDI positively correlates with tumor malignancy, invasiveness, and immunotherapy resistance. Four necroptosis genes (PTGES3, MYO6, CCT6A, and CTSH) may affect cancer cell evolution. In vitro, CTSH overexpression or PTGES3 knockdown inhibited NSCLC cell proliferation and migration while inducing necroptosis with necrosome formation. Moreover, we observed diminished CTSH, heightened PTGES3, and low necroptosis activity in 12 pairs of NSCLC tumors and normal tissues. CTSH overexpression or PTGES3 knockdown induced necroptosis and improved anti-PD1 therapy efficiency in syngeneic cancer mouse models. These findings indicate necroptosis genes as potential therapeutic targets in cancer treatments.

Multi-Omics Analysis of Aberrances and Functional Implications of IRF5 in Digestive Tract Tumours

Oesophageal cancer (EC) is a common gastrointestinal malignancy and includes oesophageal squamous cell carcinoma (ESCC) and oesophageal adenocarcinoma (EAC) sub-types. Gene signatures predicting patient outcomes are not routinely used in clinical practice, particularly owing to batch effects and data standardisation. Here, we sought to establish and validate a reliable signature of senescence-related genes (SRGs) that would aid in predicting prognosis in patients with EC. We downloaded transcriptomics data, and a novel pairwise comparison algorithm selected valid SRG pairs (SRGPs) to construct a prognostic SRGP signature. The SRGPs were verified using Kaplan-Meier survival and receiver operating characteristic curve analyses. Additionally, the relationships between the SRGP signatures and prognosis, immune cell infiltration and chemotherapeutic drug responsiveness were evaluated. The random forest algorithm identified the most clinically significant genes, followed by experimental validation. 19 and 26 SRGP signatures were created for ESCC (n = 81) and EAC (n = 79), respectively. Patients with EC were divided into two groups based on the median risk score. The Kaplan-Meier analysis demonstrated significant differences in overall survival between the ESCC and EAC groups (p < 0.001). The sub-types exhibited different immune signatures. IRF5 was the most clinically significant gene for ESCC. It was highly expressed in ESCC cells, and IRF5 knockdown inhibited cell migration and proliferation, while promoting apoptosis and senescence. The SRGP signature may predict prognosis and immunotherapeutic responses, and IRF5 is a potential target gene for ESCC.

Characterization of an endoplasmic reticulum stress-associated lncRNA prognostic signature and the tumor-suppressive role of RP11-295G20.2 knockdown in lung adenocarcinoma

Endoplasmic reticulum stress (ERS) is commonly induced by accumulating misfolded or unfolded proteins in tumor microenvironment. Long non-coding RNAs (lncRNAs) play important roles in ERS response and lung adenocarcinoma (LUAD) progression. However, the role of ERS-related lncRNAs in LUAD remains unknown. In this study, we aimed to identify ERS-associated lncRNAs with prognostic value in LUAD and characterize their clinical implications. Cox and least absolute shrinkage and selection operator regression analyses identified nine ERS-related lncRNAs with independent prognostic abilities, including five protective factors (CROCCP2, KIAA0125, LINC0996, RPARP-AS1 and TBX5-AS1) and four risk factors (LINC0857, LINC116, RP11-21L23.2 and RP11-295G20.2). We developed an ERS-related lncRNA risk prediction model in predicting overall survival of LUAD patients, which classified TCGA cohorts into high-risk (HS) and low-risk (LS) groups. Comprehensive bioinformatic analyses revealed HS patients featured with late-stage tumors, greater mutation burdens, weaker anti-tumor immunity/responses, and lower sensitivity to targeted drugs compared to LS patients, contributing to tumor progression and a poor prognosis. Functional enrichment analysis implicated these ERS-related lncRNAs in cell migration, cell death, and immunity. Furthermore, expression of the most significantly upregulated risk lncRNA, RP11-295G20.2, was validated at the mRNA level using clinical LUAD samples. Knockdown of RP11-295G20.2 obviously reduced ERS and suppressed proliferation, invasion, and migration of LUAD cells. This novel ERS-related lncRNA signature provides a new biomarker for prognostic prediction, and ERS-associated RP11-295G20.2 serves as a potential therapeutic target in LUAD.

Molecular subtype construction and prognosis model for stomach adenocarcinoma characterized by metabolism-related genes

Background

Metabolic reprogramming is implicated in cancer progression. However, the impact of metabolism-associated genes in stomach adenocarcinomas (STAD) has not been thoroughly reviewed. Herein, we characterized metabolic transcription-correlated STAD subtypes and evaluated a metabolic RiskScore for evaluation survival.

Method

Genes related to metabolism were gathered from previous study and metabolic subtypes were screened using ConsensusClusterPlus in TCGA-STAD and GSE66229 dataset. The ssGSEA, MCP-Count, ESTIMATE and CIBERSORT determined the immune infiltration. A RiskScore model was established using the WGCNA and LASSO Cox regression in the TCGA-STAD queue and verified in the GSE66229 datasets. RT-qPCR was employed to measure the mRNA expressions of genes in the model.

Result

Two metabolism-related subtypes (C1 and C2) of STAD were constructed on account of the expression profiles of 113 prognostic metabolism genes with different immune outcomes and apparently distinct metabolic characteristic. The overall survival (OS) of C2 subtype was shorter than that of C1 subtype. Four metabolism-associated genes in turquoise model, which closely associated with C2 subtype, were employed to build the RiskScore (MATN3, OSBPL1A, SERPINE1, CPNE8) in TCGA-train dataset. Patients developed a poorer prognosis if they had a high RiskScore than having a low RiskScore. The promising effect of RiskScore was verified in the TCGA-test, TCGA-STAD and GSE66229 datasets. The prediction reliability of the RiskScore was validated by time-dependent receiver operating characteristic curve (ROC) and nomogram. Moreover, samples with high RiskScore had an enhanced immune status and TIDE score. Moreover, MATN3, OSBPL1A, SERPINE1 and CPNE8 mRNA levels were all elevated in SGC7901 cells. Inhibition of OSBPL1A decreased SGC7901 cells invasion numbers.

Conclusion

This work provided a new perspective into heterogeneity in metabolism and its association with immune escape in STAD. RiskScore was considered to be a strong prognostic label that could help individualize the treatment of STAD patients.

m6A-methylated KCTD21-AS1 regulates macrophage phagocytosis through CD47 and cell autophagy through TIPR

Blocking immune checkpoint CD47/SIRPα is a useful strategy to engineer macrophages for cancer immunotherapy. However, the roles of CD47-related noncoding RNA in regulating macrophage phagocytosis for lung cancer therapy remain unclear. This study aims to investigate the effects of long noncoding RNA (lncRNA) on the phagocytosis of macrophage via CD47 and the proliferation of non-small cell lung cancer (NSCLC) via TIPRL. Our results demonstrate that lncRNA KCTD21-AS1 increases in NSCLC tissues and is associated with poor survival of patients. KCTD21-AS1 and its m6A modification by Mettl14 promote NSCLC cell proliferation. miR-519d-5p gain suppresses the proliferation and metastasis of NSCLC cells by regulating CD47 and TIPRL. Through ceRNA with miR-519d-5p, KCTD21-AS1 regulates the expression of CD47 and TIPRL, which further regulates macrophage phagocytosis and cancer cell autophagy. Low miR-519d-5p in patients with NSCLC corresponds with poor survival. High TIPRL or CD47 levels in patients with NSCLC corresponds with poor survival. In conclusion, we demonstrate that KCTD21-AS1 and its m6A modification promote NSCLC cell proliferation, whereas miR-519d-5p inhibits this process by regulating CD47 and TIPRL expression, which further affects macrophage phagocytosis and cell autophagy. This study provides a strategy through miR-519-5p gain or KCTD21-AS1 depletion for NSCLC therapy by regulating CD47 and TIPRL.

Pan-cancer analyses identify MIR210HG overexpression, epigenetic regulation and oncogenic role in human tumors and its interaction with the tumor microenvironment

BACKGROUND

Molecular entities showing dysregulation in multiple cancers may hold great biomarker or therapeutic potential. There is accumulating evidence that highlights the dysregulation of a long non-coding RNA, MIR210HG, in various cancers and its oncogenic role. However, a comprehensive analysis of MIR210HG expression pattern, molecular mechanisms, diagnostic or prognostic significance or evaluation of its interaction with tumor microenvironment across various cancers remains unstudied.

METHODS

A systematic pan-cancer analysis was done using multiple public databases and bioinformatic tools to study the molecular role and clinical significance of MIR210HG. We have analyzed expression patterns, genome alteration, transcriptional and epigenetic regulation, correlation with patient survival, immune infiltrates, co-expressed genes, interacting proteins, and pathways associated with MIR210HG.

RESULTS

The Pan cancer expression analysis of MIR210HG through various tumor datasets demonstrated that MIR210HG is significantly upregulated in most cancers and increased with the tumor stage in a subset of them. Furthermore, prognostic analysis revealed high MIR210HG expression is associated with poor overall and disease-free survival in specific cancer types. Genetic alteration analysis showed minimal alterations in the MIR210HG locus, indicating that overexpression in cancers is not due to gene amplification. The exploration of SNPs on MIR210HG suggested possible structural changes that may affect its interactions with the miRNAs. The correlation of MIR210HG with promoter methylation was found to be significantly negative in nature in majority of cancers depicting the possible epigenetic regulation of expression of MIR210HG. Additionally, MIR210HG showed negative correlations with immune cells and thus may have strong impact on the tumor microenvironment. Functional analysis indicates its association with hypoxia, angiogenesis, metastasis, and DNA damage repair processes. MIR210HG was found to interact with several proteins and potentially regulate chromatin modifications and transcriptional regulation.

CONCLUSIONS

A first pan-can cancer analysis of MIR210HG highlights its transcriptional and epigenetic deregulation and oncogenic role in the majority of cancers, its correlation with tumor microenvironment factors such as hypoxia and immune infiltration, and its potential as a prognostic biomarker and therapeutic target in several cancers.

Analysis of multiple programmed cell death-related prognostic genes and functional validations of necroptosis-associated genes in oesophageal squamous cell carcinoma

BACKGROUND

Oesophageal squamous cell carcinoma (ESCC) is a lethal malignancy. Immune checkpoint inhibitors (ICIs) showed great clinical benefits for patients with ESCC. We aimed to construct a model predicting prognosis and response to ICIs by integrating diverse programmed cell death (PCD) forms.

METHODS

Genes related to 14 PCDs were collected to generate multi-gene signatures, including apoptosis, necroptosis, pyroptosis, ferroptosis, and cuproptosis. Bulk and single-cell RNA transcriptome datasets were used to develop and validate the model. We assessed the functions of two necroptosis-related genes in ESCC cells by Western blot, co-immunoprecipitation (Co-IP), LDH release assay, CCK-8, and migration assay, followed by immunohistochemistry (IHC) staining on samples of patients with ESCC (n = 67).

FINDINGS

We built and validated a 16-gene prognostic combined cell death index (CCDI) by combining immunogenic cell death (ICD) and necroptosis signatures. The CCDI could also predict response to ICIs in cancer, as shown by Tumour Immune Dysfunction and Exclusion (TIDE) analysis, confirmed in four independent ICI clinical trials. Trajectory analysis revealed that HOOK1 and CUL4A might affect ESCC cell fate. We found that HOOK1 induced necroptosis and inhibited the proliferation and migration of ESCC cells, while CUL4A exhibited the opposite effects. Co-IP assay confirmed that HOOK1 and CUL4A promoted and reduced necrosome formation in ESCC cells. Data from patients with ESCC further supported that HOOK1 and CUL4A might be a tumour suppressor and oncogene, respectively.

INTERPRETATION

We constructed a CCDI model with potential in predicting prognosis and response to ICIs in cancer. HOOK1 and CUL4A in the CCDI model are crucial prognostic biomarkers in ESCC.

FUNDING

The Natural Science Foundation of China [82172786], The National Cancer Center Climbing Fund of China [NCC201908B06], The Natural Science Foundation of Heilongjiang Province [LH2021H077].

Unlocking the predictive potential of long non-coding RNAs: a machine learning approach for precise cancer patient prognosis

The intricate web of cancer biology is governed by the active participation of long non-coding RNAs (lncRNAs), playing crucial roles in cancer cells' proliferation, migration, and drug resistance. Pioneering research driven by machine learning algorithms has unveiled the profound ability of specific combinations of lncRNAs to predict the prognosis of cancer patients. These findings highlight the transformative potential of lncRNAs as powerful therapeutic targets and prognostic markers. In this comprehensive review, we meticulously examined the landscape of lncRNAs in predicting the prognosis of the top five cancers and other malignancies, aiming to provide a compelling reference for future research endeavours. Leveraging the power of machine learning techniques, we explored the predictive capabilities of diverse lncRNA combinations, revealing their unprecedented potential to accurately determine patient outcomes.

Screening prognostic markers for hepatocellular carcinoma based on pyroptosis-related lncRNA pairs

BACKGROUND

Pyroptosis is closely related to cancer prognosis. In this study, we tried to construct an individualized prognostic risk model for hepatocellular carcinoma (HCC) based on within-sample relative expression orderings (REOs) of pyroptosis-related lncRNAs (PRlncRNAs).

METHODS

RNA-seq data of 343 HCC samples derived from The Cancer Genome Atlas (TCGA) database were analyzed. PRlncRNAs were detected based on differentially expressed lncRNAs between sample groups clustered by 40 reported pyroptosis-related genes (PRGs). Univariate Cox regression was used to screen out prognosis-related PRlncRNA pairs. Then, based on REOs of prognosis-related PRlncRNA pairs, a risk model for HCC was constructed by combining LASSO and stepwise multivariate Cox regression analysis. Finally, a prognosis-related competing endogenous RNA (ceRNA) network was built based on information about lncRNA-miRNA-mRNA interactions derived from the miRNet and TargetScan databases.

RESULTS

Hierarchical clustering of HCC patients according to the 40 PRGs identified two groups with a significant survival difference (Kaplan-Meier log-rank, p = 0.026). Between the two groups, 104 differentially expressed lncRNAs were identified (|log₂(FC)|> 1 and FDR < 5%). Among them, 83 PRlncRNA pairs showed significant associations between their REOs within HCC samples and overall survival (Univariate Cox regression, p < 0.005). An optimal 11-PRlncRNA-pair prognostic risk model was constructed for HCC. The areas under the curves (AUCs) of time-dependent receiver operating characteristic (ROC) curves of the risk model for 1-, 3-, and 5-year survival were 0.737, 0.705, and 0.797 in the validation set, respectively. Gene Set Enrichment Analysis showed that inflammation-related interleukin signaling pathways were upregulated in the predicted high-risk group (p < 0.05). Tumor immune infiltration analysis revealed a higher abundance of regulatory T cells (Tregs) and M2 macrophages and a lower abundance of CD8 + T cells in the high-risk group, indicating that excessive pyroptosis might occur in high-risk patients. Finally, eleven lncRNA-miRNA-mRNA regulatory axes associated with pyroptosis were established.

CONCLUSION

Our risk model allowed us to determine the robustness of the REO-based PRlncRNA prognostic biomarkers in the stratification of HCC patients at high and low risk. The model is also helpful for understanding the molecular mechanisms between pyroptosis and HCC prognosis. High-risk patients may have excessive pyroptosis and thus be less sensitive to immune therapy.

A novel machine learning model based on ubiquitin-related gene pairs and clinical features to predict prognosis and treatment effect in colon adenocarcinoma

BACKGROUND

Ubiquitin and ubiquitin-like (UB/UBL) conjugations are essential post-translational modifications that contribute to cancer onset and advancement. In colon adenocarcinoma (COAD), nonetheless, the biological role, as well as the clinical value of ubiquitin-related genes (URGs), is unclear. The current study sought to design and verify a ubiquitin-related gene pairs (URGPs)-related prognostic signature for predicting COAD prognoses.

METHODS

Using univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression, URGP's predictive signature was discovered. Signatures differentiated high-risk and low-risk patients. ROC and Kaplan-Meier assessed URGPs' signature. Gene set enrichment analysis (GSEA) examined biological nomogram enrichment. Chemotherapy and tumor immune microenvironment were also studied.

RESULTS

The predictive signature used six URGPs. High-risk patients had a worse prognosis than low-risk patients, according to Kaplan-Meier. After adjusting for other clinical characteristics, the URGPs signature could reliably predict COAD patients. In the low-risk group, we found higher amounts of invading CD4 memory-activated T cells, follicular helper T cells, macrophages, and resting dendritic cells. Moreover, low-risk group had higher immune checkpoint-related gene expression and chemosensitivity.

CONCLUSION

Our research developed a nomogram and a URGPs prognostic signature to predict COAD prognosis, which may aid in patient risk stratification and offer an effective evaluation method of individualized treatment in clinical settings.

Improvement of ACK1-targeted therapy efficacy in lung adenocarcinoma using chloroquine or bafilomycin A1

BACKGROUND

Activated Cdc42-associated kinase 1 (ACK1) is a promising druggable target for cancer, but its inhibitors only showed moderate effects in clinical trials. The study aimed to investigate the underlying mechanisms and improve the antitumor efficacy of ACK1 inhibitors.

METHODS

RNA-seq was performed to determine the downstream pathways of ACK. Using Lasso Cox regression analysis, we built a risk signature with ACK1-related autophagy genes in the lung adenocarcinoma (LUAD) patients from The Cancer Genome Atlas (TCGA) project. The performance of the signature in predicting the tumor immune environment and response to immunotherapy and chemotherapy were assessed in LUAD. CCK8, mRFP-GFP-LC3 assay, western blot, colony formation, wound healing, and transwell migration assays were conducted to evaluate the effects of the ACK1 inhibitor on lung cancer cells. A subcutaneous NSCLC xenograft model was used for in vivo study.

RESULTS

RNA-seq revealed the regulatory role of ACK1 in autophagy. Furthermore, the risk signature separated LUAD patients into low- and high-risk groups with significantly different prognoses. The two groups displayed different tumor immune environments regarding 28 immune cell subsets. The low-risk groups showed high immune scores, high CTLA4 expression levels, high immunophenoscore, and low DNA mismatch repair capacity, suggesting a better response to immunotherapy. This signature also predicted sensitivity to commonly used chemotherapy and targeted drugs. In vitro, the ACK1 inhibitors (AIM-100 and Dasatinib) appeared to trigger adaptive autophagy-like response to protect lung cancer cells from apoptosis and activated the AMPK/mTOR signaling pathway, partially explaining its moderate antitumor efficacy. However, blocking lysosomal degradation with chloroquine/Bafilamycine A1 or inhibiting AMPK signaling with compound C/shPRKAA1 enhanced the ACK1 inhibitor's cytotoxic effects on lung cancer cells. The efficacy of the combined therapy was also verified using a mouse xenograft model.

CONCLUSIONS

The resulting signature from ACK1-related autophagy genes robustly predicted survival and drug sensitivity in LUAD. The lysosomal degradation inhibition improved the therapeutic effects of the ACK1 inhibitor, suggesting a potential role for autophagy in therapy evasion.

LINC00669 promotes lung adenocarcinoma growth by stimulating the Wnt/β-catenin signaling pathway

Lung cancer poses severe threats to human health. It is indispensable to discover more druggable molecular targets. We identified a novel dysregulated long non-coding RNA (lncRNA), LINC00669, in lung adenocarcinoma (LUAD) by analyzing the TCGA and GEO databases. Pan-cancer analysis indicated significantly upregulated LINC00669 across 33 cancer types. GSEA revealed a tight association of LINC00669 with the cell cycle. We next attempted to improve the prognostic accuracy of this lncRNA by establishing a risk signature in reliance on cell cycle genes associated with LINC00669. The resulting risk score combined with LINC00669 and stage showed an AUC of 0.746. The risk score significantly stratified LUAD patients into low- and high-risk subgroups, independently predicting prognosis. Its performance was verified by nomogram (C-index = 0.736) and decision curve analysis. Gene set variation analysis disclosed the two groups' molecular characteristics. We also evaluated the tumor immune microenvironment by dissecting 28 infiltrated immune cells, 47 immune checkpoint gene expressions, and immunophenoscore within the two subgroups. Furthermore, the risk signature could predict sensitivity to immune checkpoint inhibitors and other anticancer therapies. Eventually, in vitro and in vivo experiments were conducted to validate LINC00669's function using qRT-PCR, CCK8, flow cytometry, western blot, and immunofluorescence staining. The gain- and loss-of-function study substantiated LINC00669's oncogenic effects, which stimulated non-small cell lung cancer cell proliferation but reduced apoptosis via activating the Wnt/β-catenin pathway. Its oncogenic potentials were validated in the xenograft mouse model. Overall, we identified a novel oncogenic large intergenic non-coding RNA (lincRNA), LINC00669. The resulting signature may facilitate predicting prognosis and therapy responses in LUAD.

Identification and Application of a Novel Immune-Related lncRNA Signature on the Prognosis and Immunotherapy for Lung Adenocarcinoma

Background: Long non-coding RNA (lncRNA) participates in the immune regulation of lung cancer. However, limited studies showed the potential roles of immune-related lncRNAs (IRLs) in predicting survival and immunotherapy response of lung adenocarcinoma (LUAD). Methods: Based on The Cancer Genome Atlas (TCGA) and ImmLnc databases, IRLs were identified through weighted gene coexpression network analysis (WGCNA), Cox regression, and Lasso regression analyses. The predictive ability was validated by Kaplan−Meier (KM) and receiver operating characteristic (ROC) curves in the internal dataset, external dataset, and clinical study. The immunophenoscore (IPS)-PD1/PD-L1 blocker and IPS-CTLA4 blocker data of LUAD were obtained in TCIA to predict the response to immune checkpoint inhibitors (ICIs). The expression levels of immune checkpoint molecules and markers for hyperprogressive disease were analyzed. Results: A six-IRL signature was identified, and patients were stratified into high- and low-risk groups. The low-risk had improved survival outcome (p = 0.006 in the training dataset, p = 0.010 in the testing dataset, p < 0.001 in the entire dataset), a stronger response to ICI (p < 0.001 in response to anti-PD-1/PD-L1, p < 0.001 in response to anti-CTLA4), and higher expression levels of immune checkpoint molecules (p < 0.001 in PD-1, p < 0.001 in PD-L1, p < 0.001 in CTLA4) but expressed more biomarkers of hyperprogression in immunotherapy (p = 0.002 in MDM2, p < 0.001 in MDM4). Conclusion: The six-IRL signature exhibits a promising prediction value of clinical prognosis and ICI efficacy in LUAD. Patients with low risk might gain benefits from ICI, although some have a risk of hyperprogressive disease.

Increased expression of the RNA-binding protein Musashi-2 is associated with immune infiltration and predicts better outcomes in ccRCC patients

RNA-binding proteins (RBPs) mainly contribute to abnormalities in posttranscriptional gene regulation. The RBP Musashi-2, an evolutionarily conserved protein, has been characterized as an oncoprotein in various tumors. However, the prognostic value and potential roles of Musashi-2 in clear cell renal cell carcinoma (ccRCC) have not yet been elucidated. In this study, we found that Musashi-2 was mainly expressed in the normal distal tubular cells and collecting duct cells of the kidneys, while its expression was significantly decreased in ccRCC. And higher expression levels of Musashi-2 indicated better overall survival (OS) in ccRCC. Furthermore, immunohistochemistry demonstrated that PD-L1 expression was negatively correlated with Musashi-2 expression, and Musashi-2 was found to be remarkably correlated with multiple immune cells and immune inhibitors, including CD8+ T cells, CD4+ T cells, regulatory T (Treg) cells, PDCD1, CTLA4, Foxp3, and LAG3. Functional enrichment analysis revealed that Musashi-2 might be involved in ccRCC metabolic reprogramming and immune infiltration and further predicted the therapeutic sensitivity of ccRCC. Taken together, Musashi-2 is a prognostic biomarker for ccRCC patients that may provide novel insights into individualized treatment strategies and guide effective immunotherapy.

Construction of a redox-related gene signature for overall survival prediction and immune infiltration in non-small-cell lung cancer

Background: An imbalance in the redox homeostasis has been reported in multiple cancers and is associated with a poor prognosis of disease. However, the prognostic value of redox-related genes in non-small-cell lung cancer (NSCLC) remains unclear.

Methods: RNA sequencing data, DNA methylation data, mutation, and clinical data of NSCLC patients were downloaded from The Cancer Genome Atlas and Gene Expression Omnibus databases. Redox-related differentially expressed genes (DEGs) were used to construct the prognostic signature using least absolute shrinkage and selection operator (LASSO) regression analysis. Kaplan–Meier survival curve and receiver operator characteristic (ROC) curve analyses were applied to validate the accuracy of the gene signature. Nomogram and calibration plots of the nomogram were constructed to predict prognosis. Pathway analysis was performed using gene set enrichment analysis. The correlations of risk score with tumor stage, immune infiltration, DNA methylation, tumor mutation burden (TMB), and chemotherapy sensitivity were evaluated. The prognostic signature was validated using GSE31210, GSE26939, and GSE68465 datasets. Real-time polymerase chain reaction (PCR) was used to validate dysregulated genes in NSCLC.

Results: A prognostic signature was constructed using the LASSO regression analysis and was represented as a risk score. The high-risk group was significantly correlated with worse overall survival (OS) (p < 0.001). The area under the ROC curve (AUC) at the 5-year stage was 0.657. The risk score was precisely correlated with the tumor stage and was an independent prognostic factor for NSCLC. The constructed nomogram accurately predicted the OS of patients after 1-, 3-, and 5-year periods. DNA replication, cell cycle, and ECM receptor interaction were the main pathways enriched in the high-risk group. In addition, the high-risk score was correlated with higher TMB, lower methylation levels, increased infiltrating macrophages, activated memory CD4⁺ T cells, and a higher sensitivity to chemotherapy. The signature was validated in GSE31210, GSE26939, and GSE68465 datasets. Real-time PCR validated dysregulated mRNA expression levels in NSCLC.

Conclusions: A prognostic redox-related gene signature was successfully established in NSCLC, with potential applications in the clinical setting.

A novel immune-related lncRNA pair signature for prognostic prediction and immune response evaluation in gastric cancer: a bioinformatics and biological validation study

Background

Gastric cancer (GC), the most commonly diagnosed cancer worldwide with poor 5-year survival rate in advanced stages. Although immune-related and survival-related biomarkers, which typically comprise aberrantly expressed long non-coding RNAs (lncRNAs) and genes, have been identified, there are no reports of immune-related lncRNA pair (IRLP) signatures for GC.

Methods

In this study, we acquired lncRNA expression profiles from The Cancer Genome Atlas (TCGA) and used the least absolute shrinkage and selection operator (LASSO) Cox proportional hazards model (iteration = 1000) to develop a IRLP prognostic signature. The area under curve (AUC) was used to assess the prognosis predictive power. The multivariate Cox regression analysis was performed to identify whether this signature was an independent prognostic factor. The immune cell infiltration analysis was performed between the two risk groups. Last, molecular experiments were performed to explore LINC01082 is involved in the development of GC.

Results

We acquired lncRNA expression profiles and used the LASSO Cox model to develop an 18-IRLP signature with a strong prognostic predictive power. The 5-year AUC values of the training, validation, and overall TCGA datasets were 0.77, 0.86, and 0.80, respectively. The different prognostic outcomes between the high- and low-risk groups were determined using our 18-IRLP signature. Moreover, our 18-IRLP signature was an independent prognostic factor as per the multivariate Cox regression analysis, and showed better prognostic evaluation than the traditional TNM staging system as well as other clinical features. We also found differences in cancer-associated fibroblast and macrophage M2 infiltration and the expression of PD-L1, CTLA4, LAG3, and HLA were also observed between the two risk groups (P < 0.05). Analysis of biological functions revealed that target genes of the lncRNAs in the IRLP signature were enriched in focal adhesion and regulation of actin cytoskeleton. Finally, as one of significant candidates of IRLP signature, overexpression of LINC01082 suppressed the invasion ability of GC cells as well as PD-L1 expression profiles.

Conclusions

Our novel 18-IRLP signature provides new insights regarding immunological biomarkers, imparts a better understanding of the tumor immune microenvironment, and can be used for predicting prognosis and evaluating immune response in GC.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12935-022-02493-2.

Identification of Novel Subtypes in Lung Adenocarcinoma: Evidence from Gene Set Variation Analysis in Tumor and Adjacent Nontumor Samples

In patients with lung adenocarcinoma (LUAD), the prognostic role of adjacent nontumor tissues is still unknown. Alterations in the activity of immunologic and hallmark gene sets in adjacent nontumor tissues may have a potential influence on cell proliferation of normal lung cell after pulmonary lobectomy. We sought to discover LUAD subgroups and prognostic gene sets based on changes in gene set activity in tumor and adjacent nontumor tissues. Firstly, we used gene set variation analysis (GSVA) to characterize the activity changes of 4922 gene sets in LUAD and nontumor samples. Luckily, we identified three novel LUAD subtypes using the nonnegative matrix factorization (NMF) algorithm. In detailed, patients with subtype-3 had a favorable prognosis, but subtypes 1 and 2 had a bad prognosis. In addition, patients with subtype-3 in the validation cohort also lived longer. Meanwhile, using the LASSO-Cox algorithm, we discovered 15 prognostic gene sets in tumors (T gene sets) and two prognostic gene sets in adjacent nontumors (N gene sets). Interestingly, genes from N gene sets were related with immune response in nontumor tissues, but genes from T gene sets were correlated with DNA damaging and repairing in tumor tissues. These findings highlighted the possibility of a stronger immune response in nearby nontumor tissues. In conclusion, our study established a theoretical foundation for selecting therapy strategy for LUAD patients that should be guided by changes in activity in tumor and adjacent nontumor tissues, particularly after pulmonary lobectomy.

Immune-Related Long Non-coding RNA Constructs a Prognostic Signature of Ovarian Cancer

Background

Since ovarian cancer leads to the poor prognosis in women all over the world, we aim to construct an immune-related lncRNAs signature to improve the survival of ovarian cancer patients.

Methods

Normal and cancer patient samples and corresponding clinical data of ovarian were obtained from The Genotype-Tissue Expression (GTEx) portal and The Cancer Genome Atlas (TCGA) database. The predictive signature was constructed by the lasso penalty Cox proportional hazard regression model. The division of different risk groups was accounting for the optimal critical value of the time-dependent Receiver Operating Characteristic (ROC) curve. Finally, we validated and evaluated the application of this prognostic signature based on the clinical factors, chemo-sensitivity and immune status of different risk groups.

Results

The signature was established from 145 DEirlncRNAs and can be shown as an independent prognostic risk factor with accurate prediction on overall survival in ovarian cancer patients. Further analysis on the application of the prognostic signature showed that patients with low-risk had a better sensitivity to chemotherapy and a higher immunogenicity.

Conclusion

We constructed and verified an effective signature based on DEirlncRNA pairs, which could predict the prognosis, drug sensitivity and immune status of ovarian cancer patients and promote the prognostic estimation and individualized treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12575-021-00161-9.