A novel thinking: DDR axis refines the classification of ccRCC with distinctive prognosis, multi omics landscape and management strategy

Deciphering the molecular heterogeneity of soft tissue sarcoma by integrating multiomics and single cell sequence

BACKGROUND

Soft tissue sarcoma is a highly malignant tumor with extensive heterogeneity across multiple omics. However, a comprehensive multi-omics subtyping system has not yet been established.

METHODS

We integrated sarcoma multi-omics data, including clinical information, transcriptome expression profiles, DNA methylation, and somatic mutations. Using ten advanced clustering algorithms, we identified robust subtypes and validated the reproducibility of our analysis in two independent external datasets. We also identified subtype-specific treatment strategies and analyzed the differences in microenvironments between subtypes using single-cell data.

RESULTS

Based on multi-omics subtyping, we identified two novel sarcoma molecular subtypes, named sarcoma multi-omics subtype 1 (SAMS1) and SAMS2. SAMS2 exhibited a poorer prognosis, with significantly activated Myc, glycolysis, and Wnt beta-catenin signaling pathways. SAMS2 was characterized by a lower abundance of immune cell infiltration and anti-tumor immunity deficiency, which owned a lower response rate to immunotherapy but was sensitive to certain targeted drugs, including pazopanib, axitinib, thapsigargin, and elesclomol. MK886 and NU1025 were identified as effective therapeutic targets for the SAMS2. In SAMS2-like tumor epithelial cells, HOXB13/COL16A1 and BASP1 regulated epithelial-mesenchymal transition. We found that WNT7B was highly expressed in STS and was associated with poor patient prognosis, suggesting its potential as a novel therapeutic target for STS patients.

CONCLUSION

The STS molecular subtyping system based on multi-omics data effectively distinguishes patients with poor prognosis. The subtyping results are robust and reliable, providing new insights for the precise diagnosis and treatment of these patients.

Integration analysis of PLAUR as a sunitinib resistance and macrophage related biomarker in ccRCC, an in silicon and experimental study

Sunitinib remains the preferred systemic treatment option for specific patients with advanced RCC who are ineligible for immune therapy. However, it's essential to recognize that Sunitinib fails to elicit a favourable response in all patients. Moreover, most patients eventually develop resistance to Sunitinib. Therefore, identifying new targets associated with Sunitinib resistance is crucial. Utilizing multiple datasets from public cohorts, we conducted an exhaustive analysis and identified a total of 8 microRNAs and 112 mRNAs displaying significant expression differences between Sunitinib responsive and resistant groups. A particular set of six genes, specifically NIPSNAP1, STK40, SDC4, NEU1, TBC1D9, and PLAUR, were identified as highly significant via WGCNA. To delve deeper into the resistance mechanisms, we performed additional investigations using cell, molecular, and flow cytometry tests. These studies confirmed PLAUR's pivotal role in fostering Sunitinib resistance, both in vitro and in vivo. Our findings suggest that PLAUR could be a promising therapeutic target across various cancer types. In conclusion, this investigation not only uncovers vital genes and microRNAs associated with Sunitinib resistance in RCC but also introduces PLAUR as a prospective therapeutic target for diverse cancers. The outcomes contribute to advancing personalized healthcare and developing superior therapeutic strategies.

A Multi-Omics Prognostic Model Capturing Tumor Stemness and the Immune Microenvironment in Clear Cell Renal Cell Carcinoma

Background: Cancer stem-like cells (CSCs), a distinct subset recognized for their stem cell-like abilities, are intimately linked to the resistance to radiotherapy, metastatic behaviors, and self-renewal capacities in tumors. Despite their relevance, the definitive traits and importance of CSCs in the realm of oncology are still not fully comprehended, particularly in the context of clear cell renal cell carcinoma (ccRCC). A comprehensive understanding of these CSCs' properties in relation to stemness, and their impact on the efficacy of treatment and resistance to medication, is of paramount importance. Methods: In a meticulous research effort, we have identified new molecular categories designated as CRCS1 and CRCS2 through the application of an unsupervised clustering algorithm. The analysis of these subtypes included a comprehensive examination of the tumor immune environment, patterns of metabolic activity, progression of the disease, and its response to immunotherapy. In addition, we have delved into understanding these subtypes' distinctive clinical presentations, the landscape of their genomic alterations, and the likelihood of their response to various pharmacological interventions. Proceeding from these insights, prognostic models were developed that could potentially forecast the outcomes for patients with ccRCC, as well as inform strategies for the surveillance of recurrence after treatment and the handling of drug-resistant scenarios. Results: Compared with CRCS1, CRCS2 patients had a lower clinical stage/grading and a better prognosis. The CRCS2 subtype was in a hypoxic state and was characterized by suppression and exclusion of immune function, which was sensitive to gefitinib, erlotinib, and saracatinib. The constructed prognostic risk model performed well in both training and validation cohorts, helping to identify patients who may benefit from specific treatments or who are at risk of recurrence and drug resistance. A novel therapeutic target, SAA2, regulating neutrophil and fibroblast infiltration, and, thus promoting ccRCC progression, was identified. Conclusions: Our findings highlight the key role of CSCs in shaping the ccRCC tumor microenvironment, crucial for therapy research and clinical guidance. Recognizing tumor stemness helps to predict treatment efficacy, recurrence, and drug resistance, informing treatment strategies and enhancing ccRCC patient outcomes.

Clinical Significance and Expression Pattern of RIP5 and VGLL4 in Clear Cell Renal Cell Carcinoma Patients Treated with Sunitinib

While clear cell renal cell carcinoma (ccRCC) is curable, advanced metastatic (mRCC) remains a clinical challenge. We analyzed clinical, pathohistological, and molecular data (Receptor Interacting Protein 5-RIP5 and Vestigial Like Family Member 4-VGLL4 expression) of 55 mRCC patients treated with first-line treatment with sunitinib. The trend of linear increase in the protein expression of RIP5 was observed with the progression of tumor grade. Overall, 80% of RIP5-positive cells were in the control kidneys and high-grade mRCC. On the contrary, RIP5 displayed low expression in grade 2 mRCC (5.63%). The trend of linear decrease in the expression of VGLL4 was observed with the progression of tumor grade. The highest protein expression of VGLL4 was observed in grade 2 (87.82%) in comparison to grade 3 and 4 and control. High expression of RIP5 mRNA was associated with longer first-line overall survival and longer progression-free survival in mRCC. In addition, a high VGLL4 mRNA expression showed better overall survival in patients with ccRCC. In conclusion, high mRNA expression of RIP5 and VGLL4 are important markers of better survival rates in mRCC patients.

Histone methyltransferase SETD2: An epigenetic driver in clear cell renal cell carcinoma

SET domain-containing 2 (SETD2) is a lysine methyltransferase that catalyzes histone H3 lysine36 trimethylation (H3K36me3) and has been revealed to play important roles in the regulation of transcriptional elongation, RNA splicing, and DNA damage repair. SETD2 mutations have been documented in several cancers, including clear cell renal cell carcinoma (ccRCC). SETD2 deficiency is associated with cancer occurrence and progression by regulating autophagy flux, general metabolic activity, and replication fork speed. Therefore, SETD2 is considered a potential epigenetic therapeutic target and is the subject of ongoing research on cancer-related diagnosis and treatment. This review presents an overview of the molecular functions of SETD2 in H3K36me3 regulation and its relationship with ccRCC, providing a theoretical basis for subsequent antitumor therapy based on SETD2 or H3K36me3 targets.

Identification and verification of prognostic cancer subtype based on multi-omics analysis for kidney renal papillary cell carcinoma

Background

Identifying Kidney Renal Papillary Cell Carcinoma (KIRP) patients with high-risk, guiding individualized diagnosis and treatment of patients, and identifying effective prognostic targets are urgent problems to be solved in current research on KIRP.

Methods

In this study, data of multi omics for patients with KIRP were collected from TCGA database, including mRNAs, lncRNAs, miRNAs, data of methylation, and data of gene mutations. Data of multi-omics related to prognosis of patients with KIRP were selected for each omics level. Further, multi omics data related to prognosis were integrated into cluster analysis based on ten clustering algorithms using MOVICS package. The multi omics-based cancer subtype (MOCS) were compared on biological characteristics, immune microenvironmental cell abundance, immune checkpoint, genomic mutation, drug sensitivity using R packages, including GSVA, clusterProfiler, TIMER, CIBERSORT, CIBERSORT-ABS, quanTIseq, MCPcounter, xCell, EPIC, GISTIC, and pRRophetic algorithms.

Results

The top ten OS-related factors for KIRP patients were annotated. Patients with KIRP were divided into MOCS1, MOCS2, and MOCS3. Patients in the MOCS3 subtype were observed with shorter overall survival time than patients in the MOCS1 and MOCS2 subtypes. MOCS1 was negatively correlated with immune-related pathways, and we found global dysfunction of cancer-related pathways among the three MOCS subtypes. We evaluated the activity profiles of regulons among the three MOCSs. Most of the metabolism-related pathways were activated in MOCS2. Several immune microenvironmental cells were highly infiltrated in specific MOCS subtype. MOCS3 showed a significantly lower tumor mutation burden. The CNV occurrence frequency was higher in MOCS1. As for treatment, we found that these MOCSs were sensitive to different drugs and treatments. We also analyzed single-cell data for KIRP.

Conclusion

Based on a variety of algorithms, this study determined the risk classifier based on multi-omics data, which could guide the risk stratification and medication selection of patients with KIRP.