Identification of novel lactate metabolism-related lncRNAs with prognostic value for bladder cancer.

Background: Bladder cancer (BCA) has high recurrence and metastasis rates, and current treatment options show limited efficacy and significant adverse effects. It is crucial to find diagnostic markers and therapeutic targets with clinical value. This study aimed to identify lactate metabolism-related lncRNAs (LM_lncRNAs) to establish a model for evaluating bladder cancer prognosis. Method: A risk model consisting of lactate metabolism-related lncRNAs was developed to forecast bladder cancer patient prognosis using The Cancer Genome Atlas (TCGA) database. Kaplan‒Meier survival analysis, receiver operating characteristic curve (ROC) analysis and decision curve analysis (DCA) were used to evaluate the reliability of risk grouping for predictive analysis of bladder cancer patients. The results were also validated in the validation set. Chemotherapeutic agents sensitive to lactate metabolism were assessed using the Genomics of Drug Sensitivity in Cancer (GDSC) database. Results: As an independent prognostic factor for patients, lactate metabolism-related lncRNAs can be used as a nomogram chart that predicts overall survival time (OS). There were significant differences in survival rates between the high-risk and low-risk groups based on the Kaplan‒Meier survival curve. decision curve analysis and receiver operating characteristic curve analysis confirmed its good predictive capacity. As a result, 22 chemotherapeutic agents were predicted to positively affect the high-risk group. Conclusion: An lactate metabolism-related lncRNA prediction model was proposed to predict the prognosis for patients with bladder cancer and chemotherapeutic drug sensitivity in high-risk groups, which provided a new idea for the prognostic evaluation of the clinical treatment of bladder cancer.

Nanoparticle-mediated synergistic anticancer effect of ferroptosis and photodynamic therapy: Novel insights and perspectives.

Current antitumor monotherapy has many limitations, highlighting the need for novel synergistic anticancer strategies. Ferroptosis is an iron-dependent form of nonapoptotic cell death that plays a pivotal regulatory role in tumorigenesis and treatment. Photodynamic therapy (PDT) causes irreversible chemical damage to target lesions and is widely used in antitumor therapy. However, PDT's effectiveness is usually hindered by several obstacles, such as hypoxia, excess glutathione (GSH), and tumor resistance. Ferroptosis improves the anticancer efficacy of PDT by increasing oxygen and reactive oxygen species (ROS) or reducing GSH levels, and PDT also enhances ferroptosis induction due to the ROS effect in the tumor microenvironment (TME). Strategies based on nanoparticles (NPs) can subtly exploit the potential synergy of ferroptosis and PDT. This review explores recent advances and current challenges in the landscape of the underlying mechanisms regulating ferroptosis and PDT, as well as nano delivery system-mediated synergistic anticancer activity. These include polymers, biomimetic materials, metal organic frameworks (MOFs), inorganics, and carrier-free NPs. Finally, we highlight future perspectives of this novel emerging paradigm in targeted cancer therapies.

Molecular interplay between EIF4 family and circular RNAs in cancer: Mechanisms and therapeutics.

The eukaryotic translation initiation factor 4 (EIF4) family is a major contributor to the recruitment of mRNAs to ribosomes during the initial translation stage in eukaryotes, whose dysregulation either allows for cancer transformation or prevents disordered cancerous cell growth. Circular RNAs (circRNAs), which exhibit distinctive structures and are widely expressed in eukaryotes, are anticipated to be clinical diagnostic biomarkers for cancer therapy. There is considerable evidence that EIF4s can influence the biogenesis, transport, and function of circRNAs and, in turn, circRNAs can control the expressions of EIF4s through certain molecular pathways. Herein, we primarily review the emerging studies of the EIF4 family and pinpoint the roles of dysregulated EIF4s in cancer. We also evaluate the patterns of intricate interactions between circRNAs and EIF4s and discuss the potential utility of circRNA-based therapeutics targeting EIF4s in clinical cancer research.

Ferroptosis-based nano delivery systems targeted therapy for colorectal cancer: Insights and future perspectives.

There are limited options for patients who develop liver metastasis from colorectal cancer (CRC), the leading cause of cancer-related mortality worldwide. Emerging evidence has provided insights into iron deficiency and excess in CRC. Ferroptosis is an iron-dependent form of programmed cell death characterized by aberrant iron and lipid metabolism, which play crucial roles in tumorigenesis, tumor progression, and treatment options. A better understanding of the underlying molecular mechanism of ferroptosis has shed light on the current findings of ferroptosis-based nanodrug targeting strategies, such as driving ferroptosis in tumor cells and the tumor microenvironment, emerging combination therapy and against multidrug resistance. Furthermore, this review highlights the challenge and perspective of a ferroptosis-driven nanodrug delivery system for CRC-targeted therapy.

Identification of novel lactate metabolism signatures and molecular subtypes for prognosis in hepatocellular carcinoma.

Background: Evidence has shown that lactate, an immune signaling molecule, is associated with hepatocellular carcinoma (HCC) progression and immune suppression. Therefore, identifying lactate metabolism-related molecules is a promising therapeutic strategy to inhibit the development of HCC and overcome chemotherapy resistance. Long noncoding RNAs (lncRNAs) are related to tumorigenesis and metastasis. Hence, verifying the molecular subtypes of lncRNAs related to lactate metabolism will play a critical role in managing HCC. Methods: Based on HCC data in The Cancer Genome Atlas (TCGA), lactate metabolic pathway-related genes were enriched by gene collection and enrichment analysis (GSEA). Lactate metabolism-related lncRNAs (LM_lncRNAs) were identified by correlation analysis, HCC molecular subtypes were determined using nonnegative matrix factorization (NMF) clustering, and the response of the three subtypes to chemotherapeutics was further evaluated using the Genomic Tumor Sensitive Cell Line (GDSC) dataset. LM_lncRNAs were examined via Lasso-Cox regression analysis to determine prognosis for patients. A Nomagram plot was used to predict patient survival time. Results: Three molecular subtypes of HCC were identified. The survival rate of patients with C1 subtype was higher than that of those with C2 and C3. Additionally, patients with C3 subtype have higher levels of immune cell infiltration and high expression of genes related to immune checkpoints. The GDSC results indicated that patients with C3 subtypes were more sensitive to chemotherapy drugs such as sorafenib and sunitinib. The prognostic risk assessment model consisted of six risk factors (AC034229.4, AC131009.1, MYOSLID, AC008667.1, AC012073.1, AC068025.1) and two protective factors (LINC00402 and AC103858.1). Based on Kaplan-Meier analysis, low-risk HCC patients had a high survival rate, and the receiver operating characteristic curve (ROC), calibration curve, and C-index confirmed good prediction ability. Conclusion: In this study, the molecular subtyping method and prediction model of lactate metabolism-related lncRNAs (LM_lncRNAs) were constructed for the prognosis of HCC patients. This work demonstrated the potential targets of LM_lncRNAs and provided a novel perspective and therapeutic paradigm for future clinical translation.

Computational Recognition of a Regulatory T-cell-specific Signature With Potential Implications in Prognosis, Immunotherapy, and Therapeutic Resistance of Prostate Cancer.

Prostate cancer, recognized as a "cold" tumor, has an immunosuppressive microenvironment in which regulatory T cells (Tregs) usually play a major role. Therefore, identifying a prognostic signature of Tregs has promising benefits of improving survival of prostate cancer patients. However, the traditional methods of Treg quantification usually suffer from bias and variability. Transcriptional characteristics have recently been found to have a predictive power for the infiltration of Tregs. Thus, a novel machine learning-based computational framework has been presented using Tregs and 19 other immune cell types using 42 purified immune cell datasets from GEO to identify Treg-specific mRNAs, and a prognostic signature of Tregs (named "TILTregSig") consisting of five mRNAs (SOCS2, EGR1, RRM2, TPP1, and C11orf54) was developed and validated to monitor the prognosis of prostate cancer using the TCGA and ICGC datasets. The TILTregSig showed a stronger predictive power for tumor immunity compared with tumor mutation burden and glycolytic activity, which have been reported as immune predictors. Further analyses indicate that the TILTregSig might influence tumor immunity mainly by mediating tumor-infiltrating Tregs and could be a powerful predictor for Tregs in prostate cancer. Moreover, the TILTregSig showed a promising potential for predicting cancer immunotherapy (CIT) response in five CIT response datasets and therapeutic resistance in the GSCALite dataset in multiple cancers. Our TILTregSig derived from PBMCs makes it possible to achieve a straightforward, noninvasive, and inexpensive detection assay for prostate cancer compared with the current histopathological examination that requires invasive tissue puncture, which lays the foundation for the future development of a panel of different molecules in peripheral blood comprising a biomarker of prostate cancer.

Noncoding RNAs related to the hedgehog pathway in cancer: clinical implications and future perspectives.

Cancer is a type of malignant affliction threatening human health worldwide; however, the molecular mechanism of cancer pathogenesis remains to be elusive. The oncogenic hedgehog (Hh) pathway is a highly evolutionarily conserved signaling pathway in which the hedgehog-Patched complex is internalized to cellular lysosomes for degradation, resulting in the release of Smoothened inhibition and producing downstream intracellular signals. Noncoding RNAs (ncRNAs) with diversified regulatory functions have the potency of controlling cellular processes. Compelling evidence reveals that Hh pathway, ncRNAs, or their crosstalk play complicated roles in the initiation, metastasis, apoptosis and drug resistance of cancer, allowing ncRNAs related to the Hh pathway to serve as clinical biomarkers for targeted cancer therapy. In this review, we attempt to depict the multiple patterns of ncRNAs in the progression of malignant tumors via interactions with the Hh crucial elements in order to better understand the complex regulatory mechanism, and focus on Hh associated ncRNA therapeutics aimed at boosting their application in the clinical setting.

NF-κB-activated SPRY4-IT1 promotes cancer cell metastasis by downregulating TCEB1 mRNA via Staufen1-mediated mRNA decay.

Previous study demonstrated that most long non-coding RNAs (lncRNAs) function as competing endogenous RNAs or molecular sponges to negatively modulate miRNA and regulate tumor development. However, the molecular mechanisms of lncRNAs in cancer are not fully understood. Our study describes the role of the lncRNA SPRY4 intronic transcript 1 (SPRY4-IT1) in cancer metastasis by mechanisms related to Staufen1 (STAU1)-mediated mRNA decay (SMD). Briefly, we found that, high SPRY4-IT1 expression was associated with aggressiveness and poor outcome in human colorectal, breast and ovarian cancer tissues. In addition, functional assays revealed that SPRY4-IT1 significantly promoted colorectal, breast and ovarian cancer metastasis in vitro and in vivo. Mechanistically, microarray analyses identified several differentially-expressed genes upon SPRY4-IT1 overexpression in HCT 116 colorectal cancer cells. Among them, the 3'-UTR of transcription elongation factor B subunit 1 (TCEB1) mRNA can base-pair with the Alu element in the 3'-UTR of SPRY4-IT1. Moreover, SPRY4-IT1 was found to bind STAU1, promote STAU1 recruitment to the 3'-UTR of TCEB1 mRNA, and affect TCEB1 mRNA stability and expression, resulting in hypoxia-inducible factor 1α (HIF-1α) upregulation, and thereby affecting cancer cell metastasis. In addition, STAU1 depletion abrogated TCEB1 SMD and alleviated the pro-metastatic effect of SPRY4-IT1 overexpression. Significantly, we revealed that SPRY4-IT1 is also transactivated by NF-κB/p65, which activates SPRY4-IT1 to inhibit TCEB1 expression, and subsequently upregulate HIF-1α. In conclusion, our results highlight a novel mechanism of cytoplasmic lncRNA SPRY4-IT1 in which SPRY4-IT1 affecting TCEB1 mRNA stability via STAU1-mediated degradation during cancer metastasis.

m6A RNA Methylation Regulators Impact Prognosis and Tumor Microenvironment in Renal Papillary Cell Carcinoma.

Accumulating evidence has proven that N6-methyladenosine (m6A) RNA methylation plays an essential role in tumorigenesis. However, the significance of m6A RNA methylation modulators in the malignant progression of papillary renal cell carcinoma (PRCC) and their impact on prognosis has not been fully analyzed. The present research set out to explore the roles of 17 m6A RNA methylation regulators in tumor microenvironment (TME) of PRCC and identify the prognostic values of m6A RNA methylation regulators in patients afflicted by PRCC. We investigated the different expression patterns of the m6A RNA methylation regulators between PRCC tumor samples and normal tissues, and systematically explored the association of the expression patterns of these genes with TME cell-infiltrating characteristics. Additionally, we used LASSO regression to construct a risk signature based upon the m6A RNA methylation modulators. Two-gene prognostic risk model including IGF2BP3 and HNRNPC was constructed and could predict overall survival (OS) of PRCC patients from the Cancer Genome Atlas (TCGA) dataset. The prognostic signature-based risk score was identified as an independent prognostic indicator in Cox regression analysis. Moreover, we predicted the three most significant small molecule drugs that potentially inhibit PRCC. Taken together, our study revealed that m6A RNA methylation regulators might play a significant role in the initiation and progression of PRCC. The results might provide novel insight into exploration of m6A RNA modification in PRCC and provide essential guidance for therapeutic strategies.

Pan-cancer analysis of NLRP3 inflammasome with potential implications in prognosis and immunotherapy in human cancer.

NLRP3 inflammasome was introduced as a double-edged sword in tumorigenesis and influenced immunotherapy response by modulating host immunity. However, a systematic assessment of the NLRP3-inflammasome-related genes across human cancers is lacking, and the predictive role of NLRP3 inflammasome in cancer immunotherapy (CIT) response remains unexplored. Thus, in this study, we performed a pan-cancer analysis of NLRP3-inflammasome-related genes across 24 human cancers. Out of these 24 cancers, 15 cancers had significantly different expression of NLRP3-inflammasome-related genes between normal and tumor samples. Meanwhile, Cox regression analysis showed that the NLRP3 inflammasome score could be served as an independent prognostic factor in skin cutaneous melanoma. Further analysis indicated that NLRP3 inflammasome may influence tumor immunity mainly by mediating tumor-infiltrating lymphocytes and macrophages, and the effect of NLRP3 inflammasome on immunity is diverse across tumor types in tumor microenvironment. We also found that the NLRP3 inflammasome score could be a stronger predictor for immune signatures compared with tumor mutation burden (TMB) and glycolytic activity, which have been reported as immune predictors. Furthermore, analysis of the association between NLRP3 inflammasome and CIT response using six CIT response datasets revealed the predictive value of NLRP3 inflammasome for immunotherapy response of patients in diverse cancers. Our study illustrates the characterization of NLRP3 inflammasome in multiple cancer types and highlights its potential value as a predictive biomarker of CIT response, which can pave the way for further investigation of the prognostic and therapeutic potentials of NLRP3 inflammasome.

A five-mRNA signature associated with post-translational modifications can better predict recurrence and survival in cervical cancer.

High mortality of patients with cervical cancer (CC) stresses the imperative of prognostic biomarkers for CC patients. Additionally, the vital status of post-translational modifications (PTMs) in the progression of cancers has been reported by numerous researches. Therefore, the purpose of this research was to dig a prognostic signature correlated with PTMs for CC. We built a five-mRNA (GALNTL6, ARSE, DPAGT1, GANAB and FURIN) prognostic signature associated with PTMs to predict both disease-free survival (DFS) (hazard ratio [HR] = 3.967, 95% CI = 1.985-7.927; P < .001) and overall survival (HR = 2.092, 95% CI = 1.138-3.847; P = .018) for CC using data from The Cancer Genome Atlas database. Then, the robustness of the signature was validated using GSE44001 and the Human Protein Atlas (HPA) database. CIBERSORT algorithm analysis displayed that activated CD4 memory T cell was also an independent indicator for DFS (HR = 0.426, 95% CI = 0.186-0.978; P = .044) which could add additional prognostic value to the signature. Collectively, the PTM-related signature and activated CD4 memory T cell can provide new avenues for the prognostic predication of CC. These findings give further insights into effective treatment strategies for CC, providing opportunities for further experimental and clinical validations.

Glycolysis gene expression profilings screen for prognostic risk signature of hepatocellular carcinoma.

Metabolic changes are the markers of cancer and have attracted wide attention in recent years. One of the main metabolic features of tumor cells is the high level of glycolysis, even if there is oxygen. The transformation and preference of metabolic pathways is usually regulated by specific gene expression. The aim of this study is to develop a glycolysis-related risk signature as a biomarker via four common cancer types. Only hepatocellular carcinoma was shown the strong relationship with glycolysis. The mRNA sequencing and chip data of hepatocellular carcinoma, breast invasive carcinoma, renal clear cell carcinoma, colorectal adenocarcinoma were included in the study. Gene set enrichment analysis was performed, profiling three glycolysis-related gene sets, it revealed genes associated with the biological process. Univariate and multivariate Cox proportional regression models were used to screen out prognostic-related gene signature. We identified six mRNAs (DPYSL4, HOMER1, ABCB6, CENPA, CDK1, STMN1) significantly associated with overall survival in the Cox proportional regression model for hepatocellular carcinoma. Based on this gene signature, we were able to divide patients into high-risk and low-risk subgroups. Multivariate Cox regression analysis showed that prognostic power of this six gene signature is independent of clinical variables. Further, we validated this data in our own 55 paired hepatocellular carcinoma and adjacent tissues. The results showed that these proteins were highly expressed in hepatocellular carcinoma tissues compared with adjacent tissue. The survival time of high-risk group was significantly shorter than that of low-risk group, indicating that high-risk group had poor prognosis. We calculated the correlation coefficients between six proteins and found that these six proteins were independent of each other. In conclusions, we developed a glycolysis-related gene signature that could predict survival in hepatocellular carcinoma patients. Our findings provide novel insight to the mechanisms of glycolysis and it is useful for identifying patients with hepatocellular carcinoma with poor prognoses.