CTR-DB, an omnibus for patient-derived gene expression signatures correlated with cancer drug response

Genetically engineered CD276-anchoring biomimetic nanovesicles target senescent escaped tumor cells to overcome chemoresistant and immunosuppressive breast cancer

Chemotherapy-induced cellular senescence leads to an increased proportion of cancer stem cells (CSCs) in breast cancer (BC), contributing to recurrence and metastasis, while effective means to clear them are currently lacking. Herein, we aim to develop new approaches for selectively killing senescent-escape CSCs. High CD276 (95.60%) expression in multidrug-resistant BC cells, facilitates immune evasion by low-immunogenic senescent escape CSCs. CALD1, upregulated in ADR-resistant BC, promoting senescent-escape of CSCs with an anti-apoptosis state and upregulating CD276, PD-L1 to promote chemoresistance and immune escape. We have developed a controlled-released thermosensitive hydrogel containing pH- responsive anti-CD276 scFV engineered biomimetic nanovesicles to overcome BC in primary, recurrent, metastatic and abscopal humanized mice models. Nanovesicles coated anti-CD276 scFV selectively fuses with cell membrane of senescent-escape CSCs, then sequentially delivers siCALD1 and ADR due to pH-responsive MnP shell. siCALD1 together with ADR effectively induce apoptosis of CSCs, decrease expression of CD276 and PD-L1, and upregulate MHC I combined with Mn2+ to overcome chemoresistance and promote CD8+T cells infiltration. This combined therapeutic approach reveals insights into immune surveillance evasion by senescent-escape CSCs, offering a promising strategy to immunotherapy effectiveness in cancer therapy.

RAB42 overexpression correlates with poor prognosis, immune cell infiltration and chemoresistance

Background

RAB42 (Ras-related protein 42) is a new small GTPase that controls the vesicular trafficking from endosomes to trans-Golgi network in mammalian cells. However, the role of RAB42 in multiple cancers, especially in liver hepatocellular carcinoma (LIHC), has not been well investigated.

Methods

A variety of cancer-related databases and online tools, including TCGA, GTEx, TARGET, QUANTISEQ, EPIC, RNAactDrug, CTR-DB, TIMER algorithms and Sangerbox, were applied to explore the correlation of RAB42 expression with prognosis, immune microenvironment, immune regulatory network, RNA modification, pathway activation and drug sensitivity in pan-cancer. The prognostic, immunomodulatory and tumor-promoting effects of RAB42 were verified in various malignancies and determined by a series of in vitro cellular experiments.

Results

RAB42 is significantly overexpressed in most cancers with advanced pathological stages. Its overexpression is correlated with poor survival in pan-cancer. RAB42 overexpression has a high diagnostic accuracy of various cancers (AUC > 0.80). RAB42 overexpression not only correlates with distinct stromal immune infiltration and level of immune checkpoint molecules, but also associates with weak immune cell infiltration, immunomodulatory genes expression, and immunotherapeutic response to immune checkpoint inhibitors (ICIs). Additionally, RAB42 overexpression correlates with enhanced expression of m6A RNA methylation-related genes (MRGs) and its interactors. Moreover, overexpression of RAB42 serves as a drug-resistant marker to certain chemotherapies and acts as a potential biomarker for LIHC. Notably, RAB42 overexpression or activation promotes the cellular proliferation, migration and invasion of LIHC.

Conclusion

Overexpressed RAB42 serves as a potential prognostic biomarker and therapeutic target in pan-cancer, especially in LIHC.

TICRR as a potential prognostic biomarker for lung adenocarcinoma: A comprehensive analysis using TCGA database

To investigate the role of TopBP1-interacting checkpoint and replication regulator (TICRR) in the tumorigenesis and prognosis of lung adenocarcinoma (LUAD) patients. Wilcoxon signed-rank test and logistic regression were utilized to analyze the relationship between clinical characteristics and TICRR expression in LUAD from TCGA dataset. Kaplan-Meier plots and Cox regressions were used to assess the impact of TICRR impact on prognosis. ROC curves and nomograms were generated to further evaluate the relationship between TICRR expression and the risk of LUAD. Gene set enrichment analysis (GSEA) was conducted on TCGA dataset, and ssGSEA was employed to investigate the association between TICRR and immune infiltrates. The results showed that high TICRR expression was significantly associated with various clinical factors including gender, age, pathological stage, T stage, N stage, M stage, outcome of primary therapy and smoking status. ROC curves also demonstrated that TICRR was a promising biomarker for molecular pathology diagnosis in LUAD patients (AUC = 0.952). Further analysis using gene ontology (GO) term enrichment and GSEA revealed an abnormal correlation between TICRR expression and cell division. Interestingly, ssGSEA analysis showed that TICRR expression correlated with multiple immune cell types, such as Th2 cell, TFH cell, mast cell, iDC, eosinophils, and dendritic cell. Lastly, the KM-plotters indicated that LUAD patients with high TICRR expression obtained worse life expectancy (P < .001). TICRR has proven to be a valuable tool in predicting disease progression and prognosis in patients with LUAD, thereby establishing itself as a fitting biomarker for forecasting overall survival (OS) of LUAD patients.

Nek2A prevents centrosome clustering and induces cell death in cancer cells via KIF2C interaction

Unlike normal cells, cancer cells frequently exhibit supernumerary centrosomes, leading to formation of multipolar spindles that can trigger cell death. Nevertheless, cancer cells with supernumerary centrosomes escape the deadly consequences of unequal segregation of genomic material by coalescing their centrosomes into two poles. This unique trait of cancer cells presents a promising target for cancer therapy, focusing on selectively attacking cells with supernumerary centrosomes. Nek2A is a kinase involved in mitotic regulation, including the centrosome cycle, where it phosphorylates linker proteins to separate centrosomes. In this study, we investigated if Nek2A also prevents clustering of supernumerary centrosomes, akin to its separation function. Reduction of Nek2A activity, achieved through knockout, silencing, or inhibition, promotes centrosome clustering, whereas its overexpression results in inhibition of clustering. Significantly, prevention of centrosome clustering induces cell death, but only in cancer cells with supernumerary centrosomes, both in vitro and in vivo. Notably, none of the known centrosomal (e.g., CNAP1, Rootletin, Gas2L1) or non-centrosomal (e.g., TRF1, HEC1) Nek2A targets were implicated in this machinery. Additionally, Nek2A operated via a pathway distinct from other proteins involved in centrosome clustering mechanisms, like HSET and NuMA. Through TurboID proximity labeling analysis, we identified novel proteins associated with the centrosome or microtubules, expanding the known interaction partners of Nek2A. KIF2C, in particular, emerged as a novel interactor, confirmed through coimmunoprecipitation and localization analysis. The silencing of KIF2C diminished the impact of Nek2A on centrosome clustering and rescued cell viability. Additionally, elevated Nek2A levels were indicative of better patient outcomes, specifically in those predicted to have excess centrosomes. Therefore, while Nek2A is a proposed target, its use must be specifically adapted to the broader cellular context, especially considering centrosome amplification. Discovering partners such as KIF2C offers fresh insights into cancer biology and new possibilities for targeted treatment.

Low FHL1 expression indicates a good prognosis and drug sensitivity in ovarian cancer

Chemotherapy resistance is the main reason for the poor prognosis of ovarian cancer (OC). FHL1 is an important tumour regulator, but its relationship with the prognosis, drug resistance, and tumour microenvironment of OC is unknown. Immunohistochemistry was used to determine FHL1 expression in OC. Kaplan‒Meier plotter was used for survival analysis. The value of gene expression in predicting drug resistance was estimated using the area under the curve (AUC). Bivariate correlation was used to determine the coexpression of two genes. Functional cluster and pathway enrichment were used to uncover hidden signalling pathways. The relationship between gene levels and the tumour microenvironment was visualised through the ggstatsplot and pheatmap packages. The mRNA and protein levels of FHL1 were downregulated in 426 and 100 OC tissues, respectively. Low FHL1 expression was correlated with good progression-free survival (PFS), postprogression survival, and overall survival (OS) in 1815 OC patients, and was further confirmed to be associated with good OS by immunohistochemistry in 152 OC tissues. Furthermore, FHL1 was downregulated in drug-sensitive tissues, while its high expression predicted drug resistance (AUC > 0.65). Mechanistically, FHL1 was coexpressed with FLNC, CAV1, PPP1R12B, and FLNA at the mRNA and protein levels in 558 and 174 OC tissues, respectively, and their expression was downregulated in OC. Additionally, very strong coexpression of FHL1 with the four genes was identified in at least 23 different tumours. Low expression of the four genes was associated with good PFS, and the combination of FHL1 with the four genes provided better prognostic power. Meanwhile, the expression of all five genes was strongly and positively associated with the abundance of macrophages. Low FHL1 expression acts as a favourable factor in OC, probably via positive coexpression with FLNC, CAV1, PPP1R12B, and FLNA.

MT1G, an emerging ferroptosis-related gene: A novel prognostic biomarker and indicator of immunotherapy sensitivity in prostate cancer

BACKGROUND

Prostate cancer is a leading cause of cancer-related deaths in men worldwide. Despite advances in treatment strategies, there is still a need for novel therapeutic targets and approaches. Ferroptosis has emerged as a critical process in the development and progression of several cancers, including prostate cancer (PCA). In this study, we investigate the role of MT1G, a gene implicated in immune responses and ferroptosis, in the pathogenesis of PCA. Our objective is to elucidate its prognostic significance and its impact on the tumor microenvironment, while exploring its potential in enhancing the sensitivity to immune checkpoint inhibitor (ICI) therapy.

METHODS

We utilized a combination of in silico analysis and experimental techniques to investigate the role of MT1G in PCA. First, we analyzed large-scale genomic datasets to assess the expression pattern and prognostic significance of MT1G in PCA patients. Subsequently, we performed functional assays to explore the impact of MT1G in PCA and its potential involvement in modulating immune responses. In addition, we conducted in vivo experiments to evaluate the effect of MT1G on tumor growth and response to ICI therapy.

RESULTS

Our analysis revealed that MT1G expression is significantly downregulated in PCA tissues compared to normal prostate tissues and is associated with poor prognosis. Furthermore, MT1G overexpression inhibited the growth of PCA cells in vitro and in vivo. Importantly, we found that MT1G regulates the tumor microenvironment by modulating immune cell infiltration and inhibiting immunosuppressive factors. Furthermore, our study reveals a significant correlation between MT1G expression levels and the response to immune checkpoint inhibitor (ICI) therapy in prostate cancer (PCA) patients, as MT1G upregulation leads to an increase in PDL-1 expression. These findings underscore the potential of MT1G as a promising predictive biomarker for ICI therapy response in PCA patients.

CONCLUSION

Our study elucidates the pivotal role played by MT1G in the pathogenesis of prostate cancer (PCA) and its profound implications for prognosis. Moreover, it raises the intriguing possibility that MT1G could pave the way for novel therapeutic approaches in PCA treatment. This potential arises from its ability to orchestrate immune infiltration within the tumor microenvironment, consequently enhancing sensitivity to immune checkpoint inhibitor (ICI) therapy. Therefore, our findings hold substantial promise for advancing our comprehension of PCA and exploring innovative therapeutic strategies.

ClinicalOmicsDB: exploring molecular associations of oncology drug responses in clinical trials

Matching patients to optimal treatment is challenging, in part due to the limited availability of real-world clinical datasets for predictive biomarker identification. The growing integration of omics profiling into clinical trials presents a new opportunity to tackle this challenge. Here, we introduce ClinicalOmicsDB, a web application for exploring molecular associations of oncology drug responses in clinical trials. This database includes transcriptomic data from 40 clinical trial studies, with 5913 patients spanning 11 cancer types. These studies include 67 treatment arms with a variety of chemotherapy, targeted therapy and immunotherapy drugs, and their combinations, which we organize based on an established ontology for easier navigation. The web application provides users with three options to explore molecular associations of oncology drug responses, focusing on studies, treatments or genes, respectively. Gene set analysis further connects treatment response to pathway activity and tumor microenvironment attributes. The user-friendly web interface of ClinicalOmicsDB streamlines interactive analysis. A Rust-based backend speeds up response time, and application programming interfaces and an R package enable programmatic access. We use three case studies to demonstrate the utility of this resource in human cancer studies. ClinicalOmicsDB is freely available at http://trials.linkedomics.org/.

DRMref: comprehensive reference map of drug resistance mechanisms in human cancer

Drug resistance poses a significant challenge in cancer treatment. Despite the initial effectiveness of therapies such as chemotherapy, targeted therapy and immunotherapy, many patients eventually develop resistance. To gain deep insights into the underlying mechanisms, single-cell profiling has been performed to interrogate drug resistance at cell level. Herein, we have built the DRMref database (https://ccsm.uth.edu/DRMref/) to provide comprehensive characterization of drug resistance using single-cell data from drug treatment settings. The current version of DRMref includes 42 single-cell datasets from 30 studies, covering 382 samples, 13 major cancer types, 26 cancer subtypes, 35 treatment regimens and 42 drugs. All datasets in DRMref are browsable and searchable, with detailed annotations provided. Meanwhile, DRMref includes analyses of cellular composition, intratumoral heterogeneity, epithelial-mesenchymal transition, cell-cell interaction and differentially expressed genes in resistant cells. Notably, DRMref investigates the drug resistance mechanisms (e.g. Aberration of Drug's Therapeutic Target, Drug Inactivation by Structure Modification, etc.) in resistant cells. Additional enrichment analysis of hallmark/KEGG (Kyoto Encyclopedia of Genes and Genomes)/GO (Gene Ontology) pathways, as well as the identification of microRNA, motif and transcription factors involved in resistant cells, is provided in DRMref for user's exploration. Overall, DRMref serves as a unique single-cell-based resource for studying drug resistance, drug combination therapy and discovering novel drug targets.

CDS-DB, an omnibus for patient-derived gene expression signatures induced by cancer treatment

Patient-derived gene expression signatures induced by cancer treatment, obtained from paired pre- and post-treatment clinical transcriptomes, can help reveal drug mechanisms of action (MOAs) in cancer patients and understand the molecular response mechanism of tumor sensitivity or resistance. Their integration and reuse may bring new insights. Paired pre- and post-treatment clinical transcriptomic data are rapidly accumulating. However, a lack of systematic collection makes data access, integration, and reuse challenging. We therefore present the Cancer Drug-induced gene expression Signature DataBase (CDS-DB). CDS-DB has collected 78 patient-derived, paired pre- and post-treatment transcriptomic source datasets with uniformly reprocessed expression profiles and manually curated metadata such as drug administration dosage, sampling time and location, and intrinsic drug response status. From these source datasets, 2012 patient-level gene perturbation signatures were obtained, covering 85 therapeutic regimens, 39 cancer subtypes and 3628 patient samples. Besides data browsing, download and search, CDS-DB also supports single signature analysis (including differential gene expression, functional enrichment, tumor microenvironment and correlation analyses), signature comparative analysis and signature connectivity analysis. This provides insights into drug MOA and its heterogeneity in patients, drug resistance mechanisms, drug repositioning and drug (combination) discovery, etc. CDS-DB is available at http://cdsdb.ncpsb.org.cn/.

TheMarker: a comprehensive database of therapeutic biomarkers

Distinct from the traditional diagnostic/prognostic biomarker (adopted as the indicator of disease state/process), the therapeutic biomarker (ThMAR) has emerged to be very crucial in the clinical development and clinical practice of all therapies. There are five types of ThMAR that have been found to play indispensable roles in various stages of drug discovery, such as: Pharmacodynamic Biomarker essential for guaranteeing the pharmacological effects of a therapy, Safety Biomarker critical for assessing the extent or likelihood of therapy-induced toxicity, Monitoring Biomarker indispensable for guiding clinical management by serially measuring patients' status, Predictive Biomarker crucial for maximizing the clinical outcome of a therapy for specific individuals, and Surrogate Endpoint fundamental for accelerating the approval of a therapy. However, these data of ThMARs has not been comprehensively described by any of the existing databases. Herein, a database, named 'TheMarker', was therefore constructed to (a) systematically offer all five types of ThMAR used at different stages of drug development, (b) comprehensively describe ThMAR information for the largest number of drugs among available databases, (c) extensively cover the widest disease classes by not just focusing on anticancer therapies. These data in TheMarker are expected to have great implication and significant impact on drug discovery and clinical practice, and it is freely accessible without any login requirement at: https://idrblab.org/themarker.

Identification of cuproptosis-related gene SLC31A1 and upstream LncRNA-miRNA regulatory axis in breast cancer

Mounting evidence indicate that cuproptosis, a novel form of programmed cell death, contributes to cancer development and progression. However, a comprehensive analysis regarding the expressions, functions, and regulatory network of cuproptosis-related genes is still lacking. In the present work, cuproptosis-related genes, upstream miRNAs and lncRNAs, and clinical data of breast cancer from TCGA database were analyzed by R language including Cox regression analysis, correlation calculation, ROC curve construction, and survival evaluation, and were further verified by public-available databases. Chemosensitivity and immune infiltration were also evaluated by online tools. SLC31A1 was significantly increased in breast cancer samples than those in normal tissues. SLC31A1 was negatively related to a favorable outcome in breast cancer, and the AUC value increased with the prolongation of follow-up time. LINC01614 and miR-204-5p were potential upstream regulators of SLC31A1. Moreover, SLC31A1 was significantly positively correlated with different immune cells infiltration, immune cell biomarkers, and immune checkpoints in breast cancer. SLC31A1 was a potential cuproptosis-related gene in breast cancer, which was significantly upregulated and was able to predict diagnosis, prognosis, chemosensitivity, and immune infiltration. LINC01640/miR-204-5p/SLC31A1 might be a significant and promising axis during cuproptosis in breast cancer.

Prostaglandin F2α synthase promotes oxaliplatin resistance in colorectal cancer through prostaglandin F2α-dependent and F2α-independent mechanism

BACKGROUND

Oxaliplatin (Oxa) is the first-line chemotherapy drug for colorectal cancer (CRC), and Oxa resistance is crucial for treatment failure. Prostaglandin F2α synthase (PGF2α) (PGFS), an enzyme that catalyzes the production of PGF2α, is involved in the proliferation and growth of a variety of tumors. However, the role of PGFS in Oxa resistance in CRC remains unclear.

AIM

To explore the role and related mechanisms of PGFS in mediating Oxa resistance in CRC.

METHODS

The PGFS expression level was examined in 37 pairs of CRC tissues and paracancerous tissues at both the mRNA and protein levels. Overexpression or knockdown of PGFS was performed in CRC cell lines with acquired Oxa resistance (HCT116-OxR and HCT8-OxR) and their parental cell lines (HCT116 and HCT8) to assess its influence on cell proliferation, chemoresistance, apoptosis, and DNA damage. For determination of the underlying mechanisms, CRC cells were examined for platinum-DNA adducts and reactive oxygen species (ROS) levels in the presence of a PGFS inhibitor or its products.

RESULTS

Both the protein and mRNA levels of PGFS were increased in the 37 examined CRC tissues compared to the adjacent normal tissues. Oxa induced PGFS expression in the parental HCT116 and HCT8 cells in a dose-dependent manner. Furthermore, overexpression of PGFS in parental CRC cells significantly attenuated Oxa-induced proliferative suppression, apoptosis, and DNA damage. In contrast, knockdown of PGFS in Oxa-resistant HCT116 and HCT8 cells (HCT116-OxR and HCT8-OxR) accentuated the effect of Oxa treatment in vitro and in vivo. The addition of the PGFS inhibitor indomethacin enhanced the cytotoxicity caused by Oxa. Treatment with the PGFS-catalyzed product PGF2α reversed the effect of PGFS knockdown on Oxa sensitivity. Interestingly, PGFS inhibited the formation of platinum-DNA adducts in a PGF2α-independent manner. PGF2α exerts its protective effect against DNA damage by reducing ROS levels.

CONCLUSION

PGFS promotes resistance to Oxa in CRC via both PGF2α-dependent and PGF2α-independent mechanisms.

High expression of CCDC69 is correlated with immunotherapy response and protective effects on breast cancer

BACKGROUND

As a molecule controlling the assembly of central spindles and recruitment of midzone component, coiled-coil domain-containing protein 69 (CCDC69) plays an important role in multiple cancers. Currently, the relationships between CCDC69 and immune infiltration or immunotherapy in breast cancer remain unclear.

METHODS

The expression and prognostic significance of CCDC69 in breast cancer were comprehensively analyzed by quantitative real-time PCR, immunohistochemical staining and various databases. The data source of differentially expressed genes, gene set enrichment analysis, and immune cell infiltration analysis came from The Cancer Genome Atlas (TCGA) database. Single-cell analysis based on IMMUcan database was used. The protein-protein interaction network was developed applying STRING, Cytoscape, CytoHubba, and GeneMANIA. TISIDB was employed in analyzing the CCDC69 co-expressed immune related genes. The correlations between CCDC69 and immunotherapy or immune-related scores were analyzed by CAMOIP and TISMO. Ctr-db was also used to conduct drug sensitivity analysis.

RESULTS

The mRNA of CCDC69 was downregulated in breast cancer tissues compared with normal tissues. Higher CCDC69 expression was associated with a better breast cancer prognosis. Enrichment analysis showed that the co-expression genes of CCDC69 were mainly related to immune-related pathways. The expression of CCDC69 was found to be positively correlated with multiple tumor-suppression immune infiltration cells, especially T cells and dendritic cells. Meanwhile, high CCDC69 expression can predict better immunotherapy responses when compared with low CCDC69 expression. After the interferon-gamma treatment, the CCDC69 expression was elevated in vitro. CCDC69 expression was a reliable predictor for the response status of two therapeutic strategies in breast cancer.

CONCLUSIONS

Our research revealed the clinical significance of CCDC69 in breast cancer and validated the critical roles of CCDC69 in the tumor immune infiltration and immunotherapy responses.

Identification of Potential Protein Targets in Extracellular Vesicles Isolated from Chemotherapy-Treated Ovarian Cancer Cells

Despite the ongoing clinical trials and the introduction of novel treatments over the past few decades, ovarian cancer remains one of the most fatal malignancies in women worldwide. Platinum- and paclitaxel-based chemotherapy is effective in treating the majority of patients with ovarian cancer. However, more than 70% of patients experience recurrence and eventually develop chemoresistance. To improve clinical outcomes in patients with ovarian cancer, novel technologies must be developed for identifying molecular alterations following drug-based treatment of ovarian cancer. Recently, extracellular vesicles (EVs) have gained prominence as the mediators of tumor progression. In this study, we used mass spectrometry to identify the changes in EV protein signatures due to different chemotherapeutic agents used for treating ovarian cancer. By examining these alterations, we identified the specific protein induction patterns of cisplatin alone, paclitaxel alone, and a combination of cisplatin and paclitaxel. Specifically, we found that drug sensitivity was correlated with the expression levels of ANXA5, CD81, and RAB5C in patients receiving cisplatin with paclitaxel. Our findings suggest that chemotherapy-induced changes in EV protein signatures are crucial for the progression of ovarian cancer.

An integrated computational strategy to predict personalized cancer drug combinations by reversing drug resistance signatures

Drug resistance currently poses the greatest barrier to cancer treatments. To overcome drug resistance, drug combination therapy has been proposed as a promising treatment strategy. Herein, we present Re-Sensitizing Drug Prediction (RSDP), a novel computational strategy, for predicting the personalized cancer drug combination A + B by reversing the resistance signature of drug A. The process integrates multiple biological features using a robust rank aggregation algorithm, including Connectivity Map, synthetic lethality, synthetic rescue, pathway, and drug target. Bioinformatics assessments revealed that RSDP achieved a relatively accurate prediction performance for identifying personalized combinational re-sensitizing drug B against cell line-specific intrinsic resistance, cell line-specific acquired resistance, and patient-specific intrinsic resistance to drug A. In addition, we developed the largest resource of cell line-specific cancer drug resistance signatures, including intrinsic and acquired resistance, as a byproduct of the proposed strategy. The findings indicate that personalized drug resistance signature reversal is a promising strategy for identifying personalized drug combinations, which may guide future clinical decisions regarding personalized medicine.

Evaluating the expression of heat shock protein 27 and topoisomerase II α in a retrospective cohort of patients diagnosed with locally advanced breast cancer and treated with neoadjuvant anthracycline-based chemotherapies

Background

Neoadjuvant anthracycline-based chemotherapy (NAC) is a major regimen for the treatment of local advanced breast cancer (LABC), while resistance to NAC remains a paramount clinical obstacle. To investigate the role of heat shock protein 27 (Hsp27) and/or topoisomerase IIα (TopoIIα) in LABC patients treated with NAC, we performed this retrospective study.

Methods

Associations of Hsp27 transcripts with clinic-pathological characteristics, survival and drug response were investigated in public databases. Hsp27-related genes were identified, followed by functional enrichment analyses. Besides, two protein-protein interaction networks were built. Then, tumors from 103 patients who were diagnosed with LABC and received NAC were collected, and Hsp27 and TopoIIα were examined by Immunohistochemistry (IHC). Chi-square or Fisher's exact tests were performed, as well as survival analyses.

Results

Either at the transcriptional level in public databases or at the protein level tested by IHC, a high level of Hsp27 was associated with aggressive tumor characteristics such as lymph node invasion and chemotherapy resistance. Hsp27-related genes mostly involved in the metabolic pathway and the gamete generation biological process. An elevated Hsp27 indicated a poor prognosis in patients with breast cancer (log-rank test P = 0.002 and 0.004 for disease-free survival [DFS] and overall survival [OS], respectively), while it might not be an independent predictor. Of note, tumors with high TopoIIα expression (TopoIIα+) was less likely to express Hsp27 (Hsp27+), in contrast to those with TopoIIα negativity (31.1% vs. 86.2%, P<0.001), and survival analyses revealed that patients with Hsp27+ and TopoIIα- tumors had a significantly lower DFS and OS (log-rank test P < 0.001 and 0.001, respectively), in contrast to the other three groups.

Conclusions

Hsp27 was associated with aggressive breast cancers and more predictable for the prognosis of LABC patients treated with NAC when concomitantly considering TopoIIα expression.

Cytoprotective Role of Heme Oxygenase-1 in Cancer Chemoresistance: Focus on Antioxidant, Antiapoptotic, and Pro-Autophagy Properties

Chemoresistance remains the foremost challenge in cancer therapy. Targeting reactive oxygen species (ROS) manipulation is a promising strategy in cancer treatment since tumor cells present high levels of intracellular ROS, which makes them more vulnerable to further ROS elevation than normal cells. Nevertheless, dynamic redox evolution and adaptation of tumor cells are capable of counteracting therapy-induced oxidative stress, which leads to chemoresistance. Hence, exploring the cytoprotective mechanisms of tumor cells is urgently needed to overcome chemoresistance. Heme oxygenase-1 (HO-1), a rate-limiting enzyme of heme degradation, acts as a crucial antioxidant defense and cytoprotective molecule in response to cellular stress. Recently, emerging evidence indicated that ROS detoxification and oxidative stress tolerance owing to the antioxidant function of HO-1 contribute to chemoresistance in various cancers. Enhanced HO-1 expression or enzymatic activity was revealed to promote apoptosis resistance and activate protective autophagy, which also involved in the development of chemoresistance. Moreover, inhibition of HO-1 in multiple cancers was identified to reversing chemoresistance or improving chemosensitivity. Here, we summarize the most recent advances regarding the antioxidant, antiapoptotic, and pro-autophagy properties of HO-1 in mediating chemoresistance, highlighting HO-1 as a novel target for overcoming chemoresistance and improving the prognosis of cancer patients.

A Multi-Omics Analysis of NASH-Related Prognostic Biomarkers Associated with Drug Sensitivity and Immune Infiltration in Hepatocellular Carcinoma

Background: Nonalcoholic steatohepatitis (NASH)-driven hepatocellular carcinoma (HCC) is becoming a major health-related problem. The exploration of NASH-related prognostic biomarkers and therapeutic targets is necessary. Methods: Data were downloaded from the GEO database. The "glmnet" package was used to identify differentially expressed genes (DEGs). The prognostic model was constructed by the univariate Cox and LASSO regression analyses. Validation of the expression and prognosis by immunohistochemistry (IHC) in vitro. Drug sensitivity and immune cell infiltration were analyzed by CTR-DB and ImmuCellAI. Results: We constructed a prognostic model that identified the NASH-related gene set (DLAT, IDH3B, and MAP3K4), which was validated in a real-world cohort. Next, seven prognostic transcription factors (TFs) were identified. The prognostic ceRNA network included three mRNAs, four miRNAs, and seven lncRNAs. Finally, we found that the gene set was associated with drug response which was validated in six clinical trial cohorts. Moreover, the expression level of the gene set was inversely correlated with CD8 T cell infiltration in HCC. Conclusions: We established a NASH-related prognostic model. Upstream transcriptome analysis and the ceRNA network provided clues for mechanism exploration. The mutant profile, drug sensitivity, and immune infiltration analysis further guided precise diagnosis and treatment strategies.

Genomic and immunogenomic analysis of three prognostic signature genes in LUAD

BACKGROUND

Searching for immunotherapy-related markers is an important research content to screen for target populations suitable for immunotherapy. Prognosis-related genes in early stage lung cancer may also affect the tumor immune microenvironment, which in turn affects immunotherapy.

RESULTS

We analyzed the differential genes affecting lung cancer patients receiving immunotherapy through the Cancer Treatment Response gene signature DataBase (CTR-DB), and set a threshold to obtain a total of 176 differential genes between response and non-response to immunotherapy. Functional enrichment analysis found that these differential genes were mainly involved in immune regulation-related pathways. The early-stage lung adenocarcinoma (LUAD) prognostic model was constructed through the cancer genome atlas (TCGA) database, and three target genes (MMP12, NFE2, HOXC8) were screened to calculate the risk score of early-stage LUAD. The receiver operating characteristic (ROC) curve indicated that the model had good prognostic value, and the validation set (GSE50081, GSE11969 and GSE42127) from the gene expression omnibus (GEO) analysis indicated that the model had good stability, and the risk score was correlated with immune infiltrations to varying degrees. Multi-type survival analysis and immune infiltration analysis revealed that the transcriptome, methylation and the copy number variation (CNV) levels of the three genes were correlated with patient prognosis and some tumor microenvironment (TME) components. Drug sensitivity analysis found that the three genes may affect some anti-tumor drugs. The mRNA expression of immune checkpoint-related genes showed significant differences between the high and low group of the three genes, and there may be a mutual regulatory network between immune checkpoint-related genes and target genes. Tumor immune dysfunction and exclusion (TIDE) analysis found that three genes were associated with immunotherapy response and maybe the potential predictors to immunotherapy, consistent with the CTR-DB database analysis.

CONCLUSIONS

From the perspective of data mining, this study suggests that MMP12, NFE2, and HOXC8 may be involved in tumor immune regulation and affect immunotherapy. They are expected to become markers of immunotherapy and are worthy of further experimental research.

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome

BACKGROUND

Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers.

METHODS

We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort.

FINDINGS

Evaluating ENLIGHT's performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient's treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy.

CONCLUSIONS

ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome.

FUNDING

This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

DRESIS: the first comprehensive landscape of drug resistance information

Widespread drug resistance has become the key issue in global healthcare. Extensive efforts have been made to reveal not only diverse diseases experiencing drug resistance, but also the six distinct types of molecular mechanisms underlying this resistance. A database that describes a comprehensive list of diseases with drug resistance (not just cancers/infections) and all types of resistance mechanisms is now urgently needed. However, no such database has been available to date. In this study, a comprehensive database describing drug resistance information named 'DRESIS' was therefore developed. It was introduced to (i) systematically provide, for the first time, all existing types of molecular mechanisms underlying drug resistance, (ii) extensively cover the widest range of diseases among all existing databases and (iii) explicitly describe the clinically/experimentally verified resistance data for the largest number of drugs. Since drug resistance has become an ever-increasing clinical issue, DRESIS is expected to have great implications for future new drug discovery and clinical treatment optimization. It is now publicly accessible without any login requirement at: https://idrblab.org/dresis/.

Cuproptosis-related gene SLC31A1 is a potential predictor for diagnosis, prognosis and therapeutic response of breast cancer

Cuproptosis is a recently reported novel way of cell death. A comprehensive study regarding expression, function and mechanism of cuproptosis-related genes in breast cancer is still absent. In this work, a series of in silico analyses were employed and SLC31A1 was selected as the most potential cuproptosis-related gene in breast cancer, which was statistically upregulated and possessed significant abilities to predict diagnosis, prognosis and drug response. Moreover, SLC31A1 was significantly positively correlated with different immune cell infiltration levels, immune cell biomarkers or immune checkpoints in breast cancer. Upstream G2E3-AS1/let-7a-5p and CDKN2B-AS1/let-7b-5p pathways were found to be responsible for SLC31A1 upregulation in breast cancer based on competing endogenous RNA mechanism. Furthermore, we found that SLC31A1 overexpression might be also induced by its high copy number level in breast cancer. Collectively, our current data elucidated that cuproptosis-related SLC31A1 might be a promising diagnostic/prognostic biomarker and drug responsive predictor in breast cancer.

CD69 and SBK1 as potential predictors of responses to PD-1/PD-L1 blockade cancer immunotherapy in lung cancer and melanoma

Background

PD-1/PD-L1 blockade is a promising immunotherapeutic strategy with the potential to improve the outcomes of various cancers. However, there is a critically unmet need for effective biomarkers of response to PD-1/PD-L1 blockade.

Materials and methods

Potential biomarkers of response to PD-1/PD-L1 blockade were obtained from the Cancer Treatment Response gene signature Database (CTR-DB). A comprehensive pan-cancer analysis was done on The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets. Correlations between gene expression and infiltration by immune cells were assessed using TIMER, EPIC, MCPcounter, xCell, CIBERSORT, and quanTIseq. Immunophenoscore (IPS) was used to assess the potential application of the biomarkers to all TCGA tumors.

Results

Analysis of CTR-DB data identified CD69 and SBK1 as potential biomarkers of response to PD-1/PD-L1 blockade. Correlation analysis revealed that in various TCGA cancer datasets, CD69 expression level correlated positively with most immune checkpoints and tumor-infiltrating immune cells, while SBK1 expression level correlated negatively with infiltrating immune cells. IPS analysis demonstrated the ability of CD69 and SBK1 to predict PD-1/PD-L1 blockade responses in various cancers.

Conclusion

CD69 and SBK1 are potential predictors of response to cancer immunotherapy using PD-1/PD-L1 blockade. These biomarkers may guide treatment decisions, leading to precise treatment and minimizing the waste of medical resources.

CPDR: An R Package of Recommending Personalized Drugs for Cancer Patients by Reversing the Individual’s Disease-Related Signature

Due to cancer heterogeneity, only some patients can benefit from drug therapy. The personalized drug usage is important for improving the treatment response rate of cancer patients. The value of the transcriptome of patients has been recently demonstrated in guiding personalized drug use, and the Connectivity Map (CMAP) is a reliable computational approach for drug recommendation. However, there is still no personalized drug recommendation tool based on transcriptomic profiles of patients and CMAP. To fill this gap, here, we proposed such a feasible workflow and a user-friendly R package—Cancer-Personalized Drug Recommendation (CPDR). CPDR has three features. 1) It identifies the individual disease signature by using the patient subgroup with transcriptomic profiles similar to those of the input patient. 2) Transcriptomic profile purification is supported for the subgroup with high infiltration of non-cancerous cells. 3) It supports in silico drug efficacy assessment using drug sensitivity data on cancer cell lines. We demonstrated the workflow of CPDR with the aid of a colorectal cancer dataset from GEO and performed the in silico validation of drug efficacy. We further assessed the performance of CPDR by a pancreatic cancer dataset with clinical response to gemcitabine. The results showed that CPDR can recommend promising therapeutic agents for the individual patient. The CPDR R package is available at https://github.com/AllenSpike/CPDR.

Cellular Interaction Analysis Characterizing Immunosuppressive Microenvironment Functions in MM Tumorigenesis From Precursor Stages

Cell–cell interaction event (CCEs) dysregulation may relate to the heterogeneity of the tumor microenvironment (TME) and would affect therapeutic responses and clinical outcomes. To reveal the alteration of the immune microenvironment in bone marrow from a healthy state to multiple myeloma (MM), scRNA-seq data of the four states, including healthy state normal bone marrow (NBM) and three disease states (MGUS, SMM, and MM), were collected for analysis. With immune microenvironment reconstruction, the cell types, including NK cells, CD8⁺ T cells, and CD4⁺ T cells, with a higher percentage in disease states were associated with prognosis of MM patients. Furthermore, CCEs were annotated and dysregulated CCEs were identified. The number of CCEs were significantly changed between disease states and NBM. The dysregulated CCEs participated in regulation of immune cell proliferation and immune response, such as MIF-TNFRSF14 interacted between early B cells and CD8⁺ T cells. Moreover, CCE genes related to drug response, including bortezomib and melphalan, provide candidate therapeutic markers for MM treatment. Furthermore, MM patients were separated into three risk groups based on the CCE prognostic signature. Immunoregulation-related differentiation and activation of CD4⁺ T cells corresponded to the progression status with moderate risk. These results provide a comprehensive understanding of the critical role of intercellular communication in the immune microenvironment over the evolution of premalignant MM, which is related to the tumorigenesis and progression of MM, which moreover, suggests a way of potential target selection for clinical intervention.

The 2022 Nucleic Acids Research database issue and the online molecular biology database collection

The 2022 Nucleic Acids Research Database Issue contains 185 papers, including 87 papers reporting on new databases and 85 updates from resources previously published in the Issue. Thirteen additional manuscripts provide updates on databases most recently published elsewhere. Seven new databases focus specifically on COVID-19 and SARS-CoV-2, including SCoV2-MD, the first of the Issue's Breakthrough Articles. Major nucleic acid databases reporting updates include MODOMICS, JASPAR and miRTarBase. The AlphaFold Protein Structure Database, described in the second Breakthrough Article, is the stand-out in the protein section, where the Human Proteoform Atlas and GproteinDb are other notable new arrivals. Updates from DisProt, FuzDB and ELM comprehensively cover disordered proteins. Under the metabolism and signalling section Reactome, ConsensusPathDB, HMDB and CAZy are major returning resources. In microbial and viral genomes taxonomy and systematics are well covered by LPSN, TYGS and GTDB. Genomics resources include Ensembl, Ensembl Genomes and UCSC Genome Browser. Major returning pharmacology resource names include the IUPHAR/BPS guide and the Therapeutic Target Database. New plant databases include PlantGSAD for gene lists and qPTMplants for post-translational modifications. The entire Database Issue is freely available online on the Nucleic Acids Research website (https://academic.oup.com/nar). Our latest update to the NAR online Molecular Biology Database Collection brings the total number of entries to 1645. Following last year's major cleanup, we have updated 317 entries, listing 89 new resources and trimming 80 discontinued URLs. The current release is available at http://www.oxfordjournals.org/nar/database/c/.