LRP1B mutation: a novel independent prognostic factor and a predictive tumor mutation burden in hepatocellular carcinoma

Pathogenic loss-of-function mutations in LRP1B are associated with poor survival in head and neck cancer patients

OBJECTIVE

Head and neck squamous cell carcinoma (HNSCC) present a significant challenge in the medical field due to treatment resistance, which often hinders successful outcomes. The dysregulation of the LRP1B gene is linked to various cancers, but its specific role in HNSCC is poorly understood.

METHODS

This study investigated the link between pathogenic loss-of-function mutations in the LRP1B gene and survival outcomes in HNSCC patients. The Cancer Genome Atlas HNSCC cohort, comprised of 520 tumor and 44 normal tissues, was analyzed using cBioportal, and UALCAN tools. Expression patterns, survival outcomes, and clinical correlations of LRP1B were evaluated. In-depth analyses involved validation of mRNA expression using RT-qPCR and functional exploration using various in-silico tools.

RESULTS

Analysis of data from The Cancer Genome Atlas (TCGA) and cBioPortal revealed a high frequency (25 %) of LRP1B mutations in HNSCC patients. Notably, splice mutation, truncating mutation, and deep deletion, considered potential drivers, are commonly associated with LRP1B mutations. Patients with LRP1B mutations also exhibit poorer overall survival rates compared to those without these mutations. Furthermore, LRP1B mRNA expression is significantly reduced in HNSCC tissues compared to normal tissues and is correlated with advanced tumor stage, higher tumor grade, and nodal metastasis.

CONCLUSION

These findings indicate that LRP1B may function as both a prognostic biomarker and a therapeutic target in HNSCC patients.

Genomic profiling informs therapies and prognosis for patients with hepatocellular carcinoma in clinical practice

Hepatocellular carcinoma (HCC) genomic research has discovered actionable genetic changes that might guide treatment decisions and clinical trials. Nonetheless, due to a lack of large-scale multicenter clinical validation, these putative targets have not been converted into patient survival advantages. So, it's crucial to ascertain whether genetic analysis is clinically feasible, useful, and whether it can be advantageous for patients. We sequenced tumour tissue and blood samples (as normal controls) from 111 Chinese HCC patients at Qingdao University Hospital using the 508-gene panel and the 688-gene panel, respectively. Approximately 95% of patients had gene variations related to targeted treatment, with 50% having clinically actionable mutations that offered significant information for targeted therapy. Immune cell infiltration was enhanced in individuals with TP53 mutations but decreased in patients with CTNNB1 and KMT2D mutations. More notably, we discovered that SPEN, EPPK1, and BRCA2 mutations were related to decreased median overall survival, although MUC16 mutations were not. Furthermore, we found mutant MUC16 as an independent protective factor for the prognosis of HCC patients after curative hepatectomy. In conclusion, this study connects genetic abnormalities to clinical practice and potentially identifies individuals with poor prognoses who may benefit from targeted treatment or immunotherapy.

Analysis of 3760 hematologic malignancies reveals rare transcriptomic aberrations of driver genes

BACKGROUND

Rare oncogenic driver events, particularly affecting the expression or splicing of driver genes, are suspected to substantially contribute to the large heterogeneity of hematologic malignancies. However, their identification remains challenging.

METHODS

To address this issue, we generated the largest dataset to date of matched whole genome sequencing and total RNA sequencing of hematologic malignancies from 3760 patients spanning 24 disease entities. Taking advantage of our dataset size, we focused on discovering rare regulatory aberrations. Therefore, we called expression and splicing outliers using an extension of the workflow DROP (Detection of RNA Outliers Pipeline) and AbSplice, a variant effect predictor that identifies genetic variants causing aberrant splicing. We next trained a machine learning model integrating these results to prioritize new candidate disease-specific driver genes.

RESULTS

We found a median of seven expression outlier genes, two splicing outlier genes, and two rare splice-affecting variants per sample. Each category showed significant enrichment for already well-characterized driver genes, with odds ratios exceeding three among genes called in more than five samples. On held-out data, our integrative modeling significantly outperformed modeling based solely on genomic data and revealed promising novel candidate driver genes. Remarkably, we found a truncated form of the low density lipoprotein receptor LRP1B transcript to be aberrantly overexpressed in about half of hairy cell leukemia variant (HCL-V) samples and, to a lesser extent, in closely related B-cell neoplasms. This observation, which was confirmed in an independent cohort, suggests LRP1B as a novel marker for a HCL-V subclass and a yet unreported functional role of LRP1B within these rare entities.

CONCLUSIONS

Altogether, our census of expression and splicing outliers for 24 hematologic malignancy entities and the companion computational workflow constitute unique resources to deepen our understanding of rare oncogenic events in hematologic cancers.

KHDRBS1 as a novel prognostic signaling biomarker influencing hepatocellular carcinoma cell proliferation, migration, immune microenvironment, and drug sensitivity

Background

Human tumors pose significant challenges, with targeted therapy against specific molecular targets or signaling pathways being a mainstay alongside surgical resection. Previous studies have implicated KHDRBS1 in the oncogenesis of certain human tumors such as colorectal and prostate cancers, underscoring its potential as a therapeutic target. However, the comprehensive expression pattern of KHDRBS1 in hepatocellular carcinoma (HCC) warrants further exploration.

Methods

Integrating and analyzing multi-omics, multi-cohort data from public databases, coupled with clinical samples and molecular biology validation, we elucidate the oncogenic role of KHDRBS1 in HCC progression. Additionally, leveraging HCC single-cell sequencing data, we segregate malignant cells into KHDRBS1-positive and negative subsets, uncovering significant differences in their expression profiles and functional roles.

Results

Our study identifies KHDRBS1 as a tumor-promoting factor in HCC, with its positivity correlating with tumor progression. Furthermore, we highlight the clinical significance of KHDRBS1-positive malignant cells, aiming to further propel its clinical utility.

Conclusion

KHDRBS1 plays a key role in HCC development. This study provides crucial insights for further investigation into KHDRBS1 as a therapeutic target in HCC.

Integrative analysis of metabolism subtypes and identification of prognostic metabolism-related genes for glioblastoma

Increasing evidence has demonstrated that cancer cell metabolism is a critical factor in tumor development and progression; however, its role in glioblastoma (GBM) remains limited. In the present study, we classified GBM into three metabolism subtypes (MC1, MC2, and MC3) through cluster analysis of 153 GBM samples from the RNA-sequencing data of The Cancer Genome Atlas (TCGA) based on 2752 metabolism-related genes (MRGs). We further explored the prognostic value, metabolic signatures, immune infiltration, and immunotherapy sensitivity of the three metabolism subtypes. Moreover, the metabolism scoring model was established to quantify the different metabolic characteristics of the patients. Results showed that MC3, which is associated with a favorable survival outcome, had higher proportions of isocitrate dehydrogenase (IDH) mutations and lower tumor purity and proliferation. The MC1 subtype, which is associated with the worst prognosis, shows a higher number of segments and homologous recombination defects and significantly lower mRNA expression-based stemness index (mRNAsi) and epigenetic-regulation-based mRNAsi. The MC2 subtype has the highest T-cell exclusion score, indicating a high likelihood of immune escape. The results were validated using an independent dataset. Five MRGs (ACSL1, NDUFA2, CYP1B1, SLC11A1, and COX6B1) correlated with survival outcomes were identified based on metabolism-related co-expression module analysis. Laboratory-based validation tests further showed the expression of these MRGs in GBM tissues and how their expression influences cell function. The results provide a reference for developing clinical management approaches and treatments for GBM.

LRP1B-a prognostic marker in tubo-ovarian high-grade serous carcinoma

Low-density lipoprotein (LDL) receptor-related protein 1B (LRP1B) is a member of the LDL receptor family and has often been discussed as a tumor suppressor gene, as its down-regulation is correlated with a poor prognosis in multiple carcinoma entities. Due to the high metastasis rate into the fatty peritoneal cavity and current research findings showing a dysregulation of lipid metabolism in tubo-ovarian high-grade serous carcinoma (HGSC), we questioned the prognostic impact of the LRP1B protein expression. We examined a well-characterized large cohort of 571 patients with primary HGSC and analyzed the LRP1B protein expression via immunohistochemical staining (both in tumor and stroma cells separately), performed precise bioimage analysis with QuPath, and calculated the prognostic impact using SPSS. Our results demonstrate that LRP1B functions as a significant prognostic marker for overall survival (OS) and progression-free survival (PFS) in HGSC on the protein level. High cytoplasmic expression of LRP1B in tumor, stroma, and combined tumor and stroma cells has a significantly positive association with a mean prolongation of the OS by 42 months (P = .005), 29 months (P = .005), and 25 months (P = .001), respectively. Additionally, the mean PFS was 18 months longer in tumor (P = .002), 19 months in stroma (P = .004), and 19 months in both cell types combined (P = .01). Our results remained significant in multivariate analysis. We envision LRP1B as a potential prognostic tool that could help us understand the functional role of lipid metabolism in advanced HGSC, especially regarding liposomal medications.

Loss of LRP1B expression drives acquired chemo and radio-resistance in HPV-positive head and neck cancer

OBJECTIVES

Although human papillomavirus positive (HPV+) oropharyngeal squamous cell carcinoma (OPSCC) patients typically experience excellent survival, 15-20 % of patients recur after treatment with chemotherapy and radiation. Therefore, there is a need for biomarkers of treatment failure to guide treatment intensity.

MATERIALS AND METHODS

Whole genome sequencing was carried out on HPV+OPSCC patients who were primarily treated with concurrent chemotherapy (cisplatin) and radiation. We then explored whether the loss of LRP1Bwas sufficient to drive an aggressive phenotype, and promote a resistance to cisplatin and radiation therapy both in vitro using HPV+ cell lines (93VU147T, UMSCC47, UWO37 and UWO23) and in vivo.

RESULTS

Through integrative genomic analysis of three HPV+OPSCC tumour datasets, we identified that deletion of LRP1B was enriched in samples that recurred following chemo-radiation. Knockdown using siRNA in four HPV+ cell lines (UWO23, UWO37, UMSCC47 and 93VU147T) resulted in increased proliferation of all cases. CRISPR/Cas9 deletion of LRP1B in the same cell line panel demonstrated increased proliferation, clonogenic growth and migration, as well as resistance to both cisplatin and radiation in LRP1B deleted cells compared to their respective non-targeting control cells. Cell line derived xenograft studies indicated that the LRP1B knockout tumours were more resistant to cisplatin and radiation therapy compared to their controls invivo.

CONCLUSION

Taken together, our work implicates LRP1B deletion as a potential biomarker for identifying treatment resistant HPV+ OPSCC cases.

Sequencing-based analysis of clonal evolution of 25 mantle cell lymphoma patients at diagnosis and after failure of standard immunochemotherapy

Our knowledge of genetic aberrations, that is, variants and copy number variations (CNVs), associated with mantle cell lymphoma (MCL) relapse remains limited. A cohort of 25 patients with MCL at diagnosis and the first relapse after the failure of standard immunochemotherapy was analyzed using whole-exome sequencing. The most frequent variants at diagnosis and at relapse comprised six genes: TP53, ATM, KMT2D, CCND1, SP140, and LRP1B. The most frequent CNVs at diagnosis and at relapse included TP53 and CDKN2A/B deletions, and PIK3CA amplifications. The mean count of mutations per patient significantly increased at relapse (n = 34) compared to diagnosis (n = 27). The most frequent newly detected variants at relapse, LRP1B gene mutations, correlated with a higher mutational burden. Variant allele frequencies of TP53 variants increased from 0.35 to 0.76 at relapse. The frequency and length of predicted CNVs significantly increased at relapse with CDKN2A/B deletions being the most frequent. Our data suggest, that the resistant MCL clones detected at relapse were already present at diagnosis and were selected by therapy. We observed enrichment of genetic aberrations of DNA damage response pathway (TP53 and CDKN2A/B), and a significant increase in MCL heterogeneity. We identified LRP1B inactivation as a new potential driver of MCL relapse.

Using a Dual CRISPR/Cas9 Approach to Gain Insight into the Role of LRP1B in Glioblastoma

LRP1B remains one of the most altered genes in cancer, although its relevance in cancer biology is still unclear. Recent advances in gene editing techniques, particularly CRISPR/Cas9 systems, offer new opportunities to evaluate the function of large genes, such as LRP1B. Using a dual sgRNA CRISPR/Cas9 gene editing approach, this study aimed to assess the impact of disrupting LRP1B in glioblastoma cell biology. Four sgRNAs were designed for the dual targeting of two LRP1B exons (1 and 85). The U87 glioblastoma (GB) cell line was transfected with CRISPR/Cas9 PX459 vectors. To assess LRP1B-gene-induced alterations and expression, PCR, Sanger DNA sequencing, and qRT-PCR were carried out. Three clones (clones B9, E6, and H7) were further evaluated. All clones presented altered cellular morphology, increased cellular and nuclear size, and changes in ploidy. Two clones (E6 and H7) showed a significant decrease in cell growth, both in vitro and in the in vivo CAM assay. Proteomic analysis of the clones' secretome identified differentially expressed proteins that had not been previously associated with LRP1B alterations. This study demonstrates that the dual sgRNA CRISPR/Cas9 strategy can effectively edit LRP1B in GB cells, providing new insights into the impact of LRP1B deletions in GBM biology.

B7-H7: A potential target for cancer immunotherapy

Cancer immunotherapy enhances the body's immunity against tumors by mitigating immune escape. Compared with traditional chemotherapy, immunotherapy has the advantages of fewer drugs, a wider range of action and fewer side effects. B7-H7 (also known as HHLA2, B7y) is a member of the B7 family of costimulatory molecules that was discovered more than 20 years ago. B7-H7 is mostly expressed in organs such as the breast, intestine, gallbladder and placenta and is detected predominantly in monocytes/macrophages in the immune system. Its expression is upregulated after stimulation by inflammatory factors such as lipopolysaccharide and interferon-γ. B7-H7/transmembrane and immunoglobulin domain containing 2 (TMIGD2) and killer cell immunoglobulin-like receptor, three Ig domains and long cytoplasmic tail 3 (KIR3DL3)-B7-H7 are the two currently confirmed signaling pathways for B7-H7. An increasing number of studies have demonstrated that B7-H7 is widely present in a variety of human tumor tissues, especially in programmed cell death-1 (PD-L1)-negative human tumors. B7-H7 promotes tumor progression, disrupts T-cell-mediated antitumor immunity, and inhibits immune surveillance. B7-H7 also triggers tumor immune escape and is associated with clinical stage, depth of tumor infiltration, metastasis, prognosis, and survival related to different tumor types. Multiple studies have shown that B7-H7 is a promising immunotherapeutic target. Herein, review the current literature on the expression, regulation, receptors and function of B7-H7 and its regulation/function in tumors.

Mutant-allele dispersion correlates with prognosis risk in patients with advanced non-small cell lung cancer

BACKGROUND

Intra-tumor heterogeneity (ITH) contributes to lung cancer progression and resistance to therapy. To evaluate ITH and determine whether it may be employed as a predictive biomarker of prognosis in patients with advanced non-small cell lung cancer (NSCLC), we used a novel algorithm called mutant-allele dispersion (MAD).

METHODS

In the study, 103 patients with advanced NSCLC were enrolled. Using a panel of 425 cancer-related genes, next-generation sequencing (NGS) was performed on tumor specimens that had been collected. From NGS data, we derived MAD values, and we next looked into their relationships with clinical variables and different mutation subtypes.

RESULTS

The median MAD among 103 NSCLC patients was 0.73. EGFR mutation, tyrosine kinase inhibitor (TKI) therapy, radiotherapy, and chemotherapy cycles were all substantially correlated with the MAD score. In patients with lung adenocarcinoma (LUAD), correlation analysis revealed that the MAD score was substantially linked with Notch pathway mutation (P = 0.021). A significant relationship between high MAD and shorter progression-free survival (PFS) was found (HR = 2.004, 95%CI 1.269-3.163, P = 0.003). In patients with advanced NSCLC, histological type (P = 0.004), SMARCA4 mutation (P = 0.038), and LRP1B mutation (P = 0.006) were all independently associated with prognosis. The disease control rate was considerably greater in the low MAD group compared to the high MAD group in 19 LUAD patients receiving immunotherapy (92.9% vs. 40%, P = 0.037). TKI-PFS was longer in 37 patients with low MAD who received first-line TKI therapy (P = 0.014).

CONCLUSION

Our findings suggested that MAD is a practical and simple algorithm for assessing ITH, and populations with high MAD values are more likely to have EGFR mutations. MAD can be used as a potential biomarker to predict not only the prognosis of NSCLC but also the efficacy of immunotherapy and TKI therapy in patients with advanced NSCLC.

HERVs and Cancer-A Comprehensive Review of the Relationship of Human Endogenous Retroviruses and Human Cancers

Genomic instability and genetic mutations can lead to exhibition of several cancer hallmarks in affected cells such as sustained proliferative signaling, evasion of growth suppression, activated invasion, deregulation of cellular energetics, and avoidance of immune destruction. Similar biological changes have been observed to be a result of pathogenic viruses and, in some cases, have been linked to virus-induced cancers. Human endogenous retroviruses (HERVs), once external pathogens, now occupy more than 8% of the human genome, representing the merge of genomic and external factors. In this review, we outline all reported effects of HERVs on cancer development and discuss the HERV targets most suitable for cancer treatments as well as ongoing clinical trials for HERV-targeting drugs. We reviewed all currently available reports of the effects of HERVs on human cancers including solid tumors, lymphomas, and leukemias. Our review highlights the central roles of HERV genes, such as gag, env, pol, np9, and rec in immune regulation, checkpoint blockade, cell differentiation, cell fusion, proliferation, metastasis, and cell transformation. In addition, we summarize the involvement of HERV long terminal repeat (LTR) regions in transcriptional regulation, creation of fusion proteins, expression of long non-coding RNAs (lncRNAs), and promotion of genome instability through recombination.

Tumor Mutational Burden for Predicting Prognosis and Therapy Outcome of Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC), the primary hepatic malignancy, represents the second-highest cause of cancer-related death worldwide. Many efforts have been devoted to finding novel biomarkers for predicting both patients' survival and the outcome of pharmacological treatments, with a particular focus on immunotherapy. In this regard, recent studies have focused on unravelling the role of tumor mutational burden (TMB), i.e., the total number of mutations per coding area of a tumor genome, to ascertain whether it can be considered a reliable biomarker to be used either for the stratification of HCC patients in subgroups with different responsiveness to immunotherapy, or for the prediction of disease progression, particularly in relation to the different HCC etiologies. In this review, we summarize the recent advances on the study of TMB and TMB-related biomarkers in the HCC landscape, focusing on their feasibility as guides for therapy decisions and/or predictors of clinical outcome.

Prognosis and personalized medicine prediction by integrated whole exome and transcriptome sequencing of hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a clinically and genetically heterogeneous disease. To better describe the clinical value of the main driver gene mutations of HCC, we analyzed the whole exome sequencing data of 125 patients, and combined with the mutation data in the public database, 14 main mutant genes were identified. And we explored the correlation between the main mutation genes and clinical features. Consistent with the results of previous data, we found that TP53 and LRP1B mutations were related to the prognosis of our patients by WES data analysis. And we further explored the associated characteristics of TP53 and LRP1B mutations. However, it is of great clinical significance to tailor a unique prediction method and treatment plan for HCC patients according to the mutation of TP53. For TP53 wild-type HCC patients, we proposed a prognostic risk model based on 11 genes for better predictive value. According to the median risk score of the model, HCC patients with wild-type TP53 were divided into high-risk and low-risk groups. We found significant transcriptome changes in the enrichment of metabolic-related pathways and immunological characteristics between the two groups, suggesting the predictability of HCC immunotherapy by using this model. Through the CMap database, we found that AM580 had potential therapeutic significance for high-risk TP53 wild-type HCC patients.

Distinct tumor microenvironment landscapes of rectal cancer for prognosis and prediction of immunotherapy response

PURPOSE

Tumor microenvironment (TME) affects the progression of rectal cancer (RC), and the clinical relevance of its immune elements was widely reported. Here we aim to delineate the complete TME landscape, including non-immune features, to improve our understanding of RC heterogeneity and provide a better strategy for precision medicine.

METHODS

Single-cell analysis of GSE161277 using Seurat and Cellcall was performed to identify cell-cell interactions. The ssGSEA was employed to quantify the TME elements in TCGA patients, which were further clustered into subtypes by hclust. WGCNA and LASSO were combined to construct a degenerated signature for prognosis, and its performance was validated in two GEO datasets.

RESULTS

We proposed a subtyping strategy based on the abundance of both immune and non-immune components, which divided all RC patients into 4 subtypes (Immune-, Canonical-, Dormant- and Stem-like). Different subtypes exhibited distinct mutation landscapes, biological features, immune characteristics, immunotherapy responses and prognoses. Next, WGCNA and LASSO regression were combined to construct a 10-gene signature based on differentially expressed genes among different subtypes. Subgroups divided by this signature also exhibited different clinical parameters and responses to immune checkpoint blockades. Diverse machine learning algorithms were applied to achieve higher accuracy for survival prediction and a nomogram was further established in combination with M stage and age to provide an accurate and visual prediction of prognosis.

CONCLUSIONS

We identified four TME-based RC subtypes with distinct biological and clinical features. Based on those subtypes, we also proposed a degenerated 10-gene signature to predict the prognosis and immunotherapy response.

Prognostic biomarkers correlated with immune infiltration in non-small cell lung cancer

Lung cancer is the leading cause of cancer-related mortality in men and women globally. Non-small cell lung cancer (NSCLC) is the most prevalent subtype, accounting for 85-90% of all cancers. Although there have been dramatic advances in therapeutic approaches in recent decades, the recurrence and metastasis rates of NSCLC are as high as 30-40% with the 5-year overall survival rate being less than 15%. Therefore, it is necessary to explore the pathogenesis of NSCLC at the genetic level and identify prognostic biomarkers and novel therapeutic targets. Here, we aimed to identify mutated genes with high frequencies in Chinese NSCLC patients using next-generation sequencing and to investigate their relationships with the tumor mutation burden (TMB) and tumor immune microenvironment. A total of 110 NSCLC patients were enrolled to profile the genetic variations. Mutations in EGFR (62.37%), TP53 (61.29%), LRP1B (13.98%), FAT1 (12.90%), KMT2D (11.83%), CREBBP (10.75%), and RB1 (9.68%) were most prevalent. TP53, LRP1B, KMT2D, and CREBBP mutations were all significantly associated with high TMB (P < 0.05 or P < 0.01). The infiltrating levels of immune cells and immune molecules were enriched significantly in the LRP1B mutation group. LRP1B mutations significantly correlated with stimulating and inhibitory immunoregulators. Gene set enrichment analysis revealed that cell cycle, the Notch signaling pathway, the insulin signaling pathway, and the mTOR signaling pathway are related to LRP1B mutations in the immune system. LRP1B mutations may be of clinical importance in enhancing the anti-tumor immune response and may be a promising biomarker for predicting immunotherapy responsiveness.

Feasibility of a novel non-invasive swab technique for serial whole-exome sequencing of cervical tumors during chemoradiation therapy

Background

Clinically relevant genetic predictors of radiation response for cervical cancer are understudied due to the morbidity of repeat invasive biopsies required to obtain genetic material. Thus, we aimed to demonstrate the feasibility of a novel noninvasive cervical swab technique to (1) collect tumor DNA with adequate throughput to (2) perform whole-exome sequencing (WES) at serial time points over the course of chemoradiation therapy (CRT).

Methods

Cervical cancer tumor samples from patients undergoing chemoradiation were collected at baseline, at week 1, week 3, and at the completion of CRT (week 5) using a noninvasive swab-based biopsy technique. Swab samples were analyzed with whole-exome sequencing (WES) with mutation calling using a custom pipeline optimized for shallow whole-exome sequencing with low tumor purity (TP). Tumor mutation changes over the course of treatment were profiled.

Results

216 samples were collected and successfully sequenced for 70 patients (94% of total number of tumor samples collected). A total of 33 patients had a complete set of samples at all four time points. The mean mapping rate was 98% for all samples, and the mean target coverage was 180. Estimated TP was greater than 5% for all samples. Overall mutation frequency decreased during CRT but mapping rate and mean target coverage remained at >98% and >180 reads at week 5.

Conclusion

This study demonstrates the feasibility and application of a noninvasive swab-based technique for WES analysis which may be applied to investigate dynamic tumor mutational changes during treatment to identify novel genes which confer radiation resistance.

Identification of multi-omics biomarkers and construction of the novel prognostic model for hepatocellular carcinoma

Genome changes play a crucial role in carcinogenesis, and many biomarkers can be used as effective prognostic indicators in various tumors. Although previous studies have constructed many predictive models for hepatocellular carcinoma (HCC) based on molecular signatures, the performance is unsatisfactory. Because multi-omics data can more comprehensively reflect the biological phenomenon of disease, we hope to build a more accurate predictive model by multi-omics analysis. We use the TCGA to identify crucial biomarkers and construct prognostic models through difference analysis, univariate Cox, and LASSO/stepwise Cox analysis. The performances of predictive models were evaluated and validated through survival analysis, Harrell’s concordance index (C-index), receiver operating characteristic (ROC) curve, and decision curve analysis (DCA). Multiple mRNAs, lncRNAs, miRNAs, CNV genes, and SNPs were significantly associated with the prognosis of HCC. We constructed five single-omic models, and the mRNA and lncRNA models showed good performance with c-indexes over 0.70. The multi-omics model presented a robust predictive ability with a c-index over 0.77. This study identified many biomarkers that may help study underlying carcinogenesis mechanisms in HCC. In addition, we constructed multiple single-omic models and an integrated multi-omics model that may provide practical and reliable guides for prognosis assessment.

TP53 and LRP1B Co-Wild Predicts Improved Survival for Patients with LUSC Receiving Anti-PD-L1 Immunotherapy

Immunotherapy brought long-term benefits for partial patients with lung squamous cell carcinoma (LUSC). The predictor of anti-PD-L1 therapy was controversial and limited in LUSC. We aimed to explore novel biomarker for LUSC immunotherapy and the potential mechanism. Five hundred and twenty-five Chinese patients (Geneplus cohort) with LUSC underwent targeted sequencing and were involved to explore the genomic profiling. TP53 and LRP1B were the most frequently recurrent genes and correlated to higher tumor mutational burden (TMB). We observed that LUSC patients with TP53 and LRP1B co-wild (co-wild type) were associated with better survival of anti-PD-L1 therapy compared with TP53 mutant or LRP1B mutant (mutant type) in POPAR/OAK cohort. Copy-number variation (CNV) and whole genome doubling (WGD) data from TCGA LUSC cohort were obtained to assess the CNV events. There were fewer CNV alterations and lower chromosome instability in patients with TP53/LRP1B co-wild compared with those with TP53/LRP1B mutant. RNA expression data from the TCGA LUSC cohort were collected to explore the differences in RNA expression and tumor immune microenvironment (TIME) between mutant and co-wild groups. The TP53/LRP1B co-wild type had a significantly increased proportion of multiple tumor-infiltrating lymphocytes (TILs), including activated CD8 T cell, activated dendritic cell (DC), and effector memory CD8 T cell. Immune-related gene sets including checkpoint, chemokine, immunostimulatory, MHC and receptors were enriched in the co-wild type. In conclusion, TP53/LRP1B co-wild LUSC conferred an elevated response rate in anti-PD-L1 therapy and improved survival, which was associated with a chromosome-stable phenotype and an activated immune microenvironment.

Human endogenous retrovirus-H long terminal repeat-associating 2: The next immune checkpoint for antitumour therapy

Human endogenous retrovirus-H long terminal repeat-associating 2 (HHLA2) is a newly emerging immune checkpoint that belongs to B7 family. HHLA2 has a co-stimulatory receptor transmembrane and immunoglobulin domain containing 2 (TMIGD2) and a newly discovered co-inhibitory receptor killer cell Ig-like receptor, three Ig domains, and long cytoplasmic tail (KIR3DL3), which endows it with both immunostimulant and immunosuppression functions in cancer development. In this review, we summarize the HHLA2 expression profile in human cancers, its association with cancer prognosis and clinical features, and its dual roles in regulating cancer immune response through up-to-date literatures. Furthermore, we highlight that precision cancer immunotherapy through manipulating HHLA2-KIR3DL3/TMIGD2 interaction is a promising antitumour strategy.

Downregulation of Low-density lipoprotein receptor-related protein 1B (LRP1B) inhibits the progression of hepatocellular carcinoma cells by activating the endoplasmic reticulum stress signaling pathway

Hepatocellular carcinoma (HCC) has a high recurrence rate and mortality rate even after surgery. Low-density lipoprotein receptor-related protein 1B (LRP1B) has proven to be involved in tumor development and progression of multiple malignancies. However, the function of LRP1B in HCC progression has not been fully elucidated. Thus, we conducted this study to explore the relationship between LRP1B and HCC. Bioinformatic analyses implied that LRP1B was highly expressed in HCC tissues. High LRP1B expression was shown to be related to poor outcomes and the determination of HCC patients’ tumor stage. LRP1B deletion impeded the proliferation, migration, and invasion of HCC cells. Further investigation demonstrated that silencing LRP1B expression enhanced the sensitivity of HCC cells to doxorubicin. LRP1B deletion inhibited HCC progression by regulating the PERK-ATF4-CHOP signaling pathway. Additionally, we probed the genomic alterations of LRP1B in HCC and the impact on the prognosis of patients. Collectively, our results suggest that LRP1B plays an essential role in the promotion of HCC progression by regulating the PERK-ATF4-CHOP signaling pathway, which is a potential prognostic biomarker and a promising therapeutic target of HCC.

LRP1B is a Potential Biomarker for Tumor Immunogenicity and Prognosis of HCC Patients Receiving ICI Treatment

Background

New predictors of the efficacy of hepatocellular carcinoma (HCC) immunotherapy are needed. The ability of a single gene mutation to predict the therapeutic effect of immune checkpoint inhibitors (ICI) in HCC remains unknown.

Methods

The most frequently mutated genes in HCC were analyzed using the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) datasets. Mutant genes that correlated with the tumor mutational burden (TMB) and prognosis were obtained. The mutation pattern and immunological function of one of the most frequently mutated genes, LRP1B, were determined. A pan-tumor analysis of LRP1B expression, association with cancer prognosis, and immunological role was also explored. A retrospective clinical study was conducted using 102 HCC patients who received ICI treatment to further verify whether gene mutations can predict the effectiveness of immunotherapy and prognosis of HCC.

Results

LRP1B is among the most frequently mutated genes in HCC cohorts in TCGA and ICGC datasets. TCGA data showed that the LRP1B mutation activated immune signaling pathways and promoted mast cell activation. Patients with LRP1B mutations had significantly higher TMB than those with wild-type LRP1B. LRP1B expression correlated with the cancer-immunity cycle and immune cell infiltration. High LRP1B expression was also associated with poor survival among HCC patients. Results from the clinical study showed that HCC patients in the LRP1B mutation group had a poor response to ICI and worse prognosis than the wild-type group. The LRP1B mutation group had significantly higher TMB and mast cell infiltration in tumor tissues.

Conclusion

This study is the first to report that a single gene LRP1B mutation is associated with a poor clinical response to ICI treatment and negative outcomes in HCC patients. HighLRP1B expression correlated with tumor immunity and HCC prognosis.

ZCCHC17 Served as a Predictive Biomarker for Prognosis and Immunotherapy in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the common malignant tumors. The prognosis and five-year survival rate of HCC are not promising due to tumor recurrence and metastasis. Exploring markers that contribute to the early diagnosis of HCC, markers for prognostic evaluation of HCC patients, and effective targets for treating HCC patients are in the spotlight of HCC therapy. Zinc Finger CCHC-Type Containing 17 (ZCCHC17) encodes the RNA binding protein ZCCHC17, but its role in HCC is still unclear. Here, 90 paraffin-embedded specimens combined with bioinformatics were used to comprehensively clarify the value of ZCCHC17 in the diagnosis and prognosis of HCC and its potential functions. Paraffin-embedded specimens were used to assess ZCCHC17 protein expression and its correlation with prognosis in 90 HCC patients. the public data sets of HCC patients from TCGA, ICG, and GEO databases were also used for further analysis. It was found that protein and mRNA levels of ZCCHC17 in HCC tissues were significantly higher than those in normal tissues. The abnormally high expression may be related to the abnormal DNA methylation of ZCCHC17 in tumor tissues. The high expression of ZCCHC17 is related to AFP, histologic grade, tumor status, vascular invasion, and pathological stage. Multi-data set analysis showed that patients with high ZCCHC17 expression had a worse prognosis, and multivariate cox regression analysis showed an independent prognostic significance of ZCCHC17. The results of functional analysis, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA), indicate that ZCCHC17 is mainly involved in immune regulation. Subsequently, further single-sample gene set enrichment analysis (ssGSEA) showed that the expression of ZCCHC17 was related to the infiltration of immune cells. Importantly, we also analyzed the relationship between ZCCHC17 and immune checkpoint genes, tumor mutation burden (TMB), microsatellite instability (MSI) and TP53 status in HCC patients and evaluated the role of ZCCHC17 in cancer immunotherapy. In summary, ZCCHC17 is a novel marker for the diagnosis and prognostic evaluation of HCC. Concurrently, it regulates immune cells in the tumor microenvironment (TME) of HCC patients, which has a specific reference value for the immunotherapy of HCC.

Signature Construction and Molecular Subtype Identification Based on Pyroptosis-Related Genes for Better Prediction of Prognosis in Hepatocellular Carcinoma

Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide. However, there is a lack of adequate means of treatment prognostication for HCC. Pyroptosis is a newly discovered way of programmed cell death. However, the prognostic role of pyroptosis in HCC has not been thoroughly investigated. Here, we generated a novel prognostic signature to evaluate the prognostic value of pyroptosis-related genes (PRGs) using the data from The Cancer Genome Atlas (TCGA) database. The accuracy of the signature was validated using survival analysis through the International Cancer Genome Consortium cohort (n = 231) and the First Affiliated Hospital of Wenzhou Medical University cohort (n = 180). Compared with other clinical factors, the risk score of the signature was found to be associated with better patient outcomes. The enrichment analysis identified multiple pathways related with pyroptosis in HCC. Furthermore, drug sensitivity testing identified six potential chemotherapeutic agents to provide possible treatment avenues. Interestingly, patients with low risk were confirmed to be associated with lower tumor mutation burden (TMB). However, patients at high risk were found to have a higher count of immune cells. Consensus clustering was performed to identify two main molecular subtypes (named clusters A and B) based on the signature. It was found that compared with cluster B, better survival outcomes and lower TMB were observed in cluster A. In conclusion, signature construction and molecular subtype identification of PRGs could be used to predict the prognosis of HCC, which may provide a specific reference for the development of novel biomarkers for HCC treatment.

Molecular Signature Expands the Landscape of Driver Negative Thyroid Cancers

Thyroid cancer is the most common endocrine malignancy. However, the cytological diagnosis of follicular thyroid carcinoma (FTC), Hürthle cell carcinoma (HCC), and follicular variant of papillary thyroid carcinoma (FVPTC) and their benign counterparts is a challenge for preoperative diagnosis. Nearly 20-30% of biopsied thyroid nodules are classified as having indeterminate risk of malignancy and incur costs to the health care system. Based on that, 120 patients were screened for the main driver mutations previously described in thyroid cancer. Subsequently, 14 mutation-negative cases that are the main source of diagnostic errors (FTC, HCC, or FVPTC) underwent RNA-Sequencing analysis. Somatic variants in candidate driver genes (ECD, NUP98,LRP1B, NCOR1, ATM, SOS1, and SPOP) and fusions were described. NCOR1 and SPOP variants underwent validation. Moreover, expression profiling of driver-negative samples was compared to 16 BRAF V600E, RAS, or PAX8-PPARg positive samples. Negative samples were separated in two clusters, following the expression pattern of the RAS/PAX8-PPARg or BRAF V600E positive samples. Both negative groups showed distinct BRS, ERK, and TDS scores, tumor mutation burden, signaling pathways and immune cell profile. Altogether, here we report novel gene variants and describe cancer-related pathways that might impact preoperative diagnosis and provide insights into thyroid tumor biology.

The Mutation and Expression Level of LRP1B are Associated with Immune Infiltration and Prognosis in Hepatocellular Carcinoma

Purpose

This study aimed to explore the expression level and mutation of LRP1B in hepatocellular carcinoma (HCC) and to analyse the relationship between its prognostic value and immune invasion.

Methods

HCC mutant gene sets were obtained from the Cancer Genome Atlas and International Cancer Genome Consortium databases. The Kaplan–Meier method was used to evaluate the prognostic value of LRP1B expression and mutation load in HCC. The relationships between LRP1B expression level and immune cells and immune marker molecules were analysed by using the TIMER database. The association of LRP1B expression with drug sensitivity was obtained by using CellMiner. Gene set enrichment analysis and co-expression by Spearman correlation analysis were used to explore the internal mechanism of LRP1B in HCC.

Results

Seventeen most commonly mutated genes were screened out, and LRP1B was the only gene associated with HCC prognosis. The copy number variations were significantly correlated with T cell CD8⁺ (P < 0.05). LRP1B expression level was positively correlated with the infiltration degree of macrophage (P < 0.05, R = 0.132), myeloid dendritic cell (P < 0.05, R = 0.093), neutrophil (P < 0.05, R = 0.134) and T cell CD8+ cells (P < 0.05, R = 0.102) and negatively correlated with B cell (P < 0.05, R = −0.014) and T cell CD4+ (P < 0.05, R = −0.075). LRP1B expression level was significantly correlated with immunomarker molecules and drug sensitivity (all P < 0.05). The prediction of lncRNA RUSC1-AS1/hsa-miR-215-5p/LRP1B axis by bioinformatics may be the potential mechanism underlying LRP1B’s effect on HCC prognosis and progression.

Conclusion

LRP1B plays a vital role in HCC prognostic value, which is expected to be a new potential therapeutic target for HCC. LRP1B provides a theoretical basis for the clinical targeted therapy of HCC.

Correlation between LRP1B Mutations and Tumor Mutation Burden in Gastric Cancer

BACKGROUND

It has been shown that low-density lipoprotein receptor-related protein 1B (LRP1B) mutations correlate with tumor mutation burden (TMB) and prognosis in patients with melanoma and non-small-cell lung cancer, while the relationship between LRP1B mutations and TMB in gastric cancer needs further study. This study is aimed at exploring the relationship between LRP1B mutations and TMB in gastric cancer.

METHODS

Mutation frequency profiles of the genes in patients with gastric cancer in TCGA-STAD dataset were analyzed by bioinformatics analysis. The relationship among LRP1B mutations, TMB, and patient clinical features in gastric cancer was investigated by the chi-square test. The TMB prediction capacity based on LRP1B mutation status was evaluated by ROC curves.

RESULTS

LRP1B is one of the top 10 genes with high gene mutation frequency in gastric cancer. The mutation status of LRP1B in gastric cancer patients was significantly correlated with age and TP53 and MUC16 mutation status. The result of ROC curve analysis revealed that the mutation status of LRP1B could be considered as an indicator of the degree of TMB in patients with gastric cancer.

CONCLUSION

This study presented the relationship between TMB and LRP1B mutations in gastric cancer, providing a novel perspective for gastric cancer prognosis and therapy.

LRP1B: A Giant Lost in Cancer Translation

Low-density lipoprotein receptor-related protein 1B (LRP1B) is a giant member of the LDLR protein family, which includes several structurally homologous cell surface receptors with a wide range of biological functions from cargo transport to cell signaling. LRP1B is among the most altered genes in human cancer overall. Found frequently inactivated by several genetic and epigenetic mechanisms, it has mostly been regarded as a putative tumor suppressor. Still, limitations in LRP1B studies exist, in particular associated with its huge size. Therefore, LRP1B expression and function in cancer remains to be fully unveiled. This review addresses the current understanding of LRP1B and the studies that shed a light on the LRP1B structure and ligands. It goes further in presenting increasing knowledge brought by technical and methodological advances that allow to better manipulate LRP1B expression in cells and to more thoroughly explore its expression and mutation status. New evidence is pushing towards the increased relevance of LRP1B in cancer as a potential target or translational prognosis and response to therapy biomarker.