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Renal cell carcinoma (RCC) is one of the most prevalent categories of cancer worldwide, accounting for approximately 2% of cancer diagnoses and cancer deaths worldwide. various immunotherapeutic strategies have emerged as key approaches to managing advanced RCC. Bioinformatics methodologies were employed to analyze the TCGA-KIRC RNA sequencing dataset, GSE105261, GSE168845, and single-cell dataset GSE121636. Various techniques, including PCR, Western blotting, migration assays, scratch wound assays, flow cytometry, and immunofluorescence staining, were utilized to elucidate the functional characteristics of the tumor. In this study, we obtained transcriptome RNA sequencing data from the Cancer Genome Atlas (TCGA) database. Using the ESTIMATE algorithm, 249 immune-related genes were identified. Additionally, single-cell RNA sequencing data were integrated to determine the immune-related genes that affect the prognosis of clear cell renal cell carcinoma (ccRCC). Eventually, a total of 42 immune-related genes (IMDGs) were identified. Subsequently, in vitro experiments confirmed that APOC1 is highly expressed in ccRCC and significantly affects the migration and proliferation of ccRCC cells. Furthermore, our findings indicate that APOC1 plays a critical role in modulating cholesterol transport within macrophages and significantly contributes to the polarization of macrophages toward the M2 phenotype.

Background Cuproptosis is a newly discovered programmed cell death dependent on mitochondrial respiratory disorder induced by copper overload. Pyruvate dehydrogenase E1 subunit beta (PDHB) is one of the cuproptosis genesand is a nuclear-encoded pyruvate dehydrogenase, which catalyzes the conversion of pyruvate to acetyl coenzyme A. However, the mechanism of PDHB in clear cell renal cell carcinoma (ccRCC) remains unclear. Methods We used data from TCGA and GEO to assess the expression of PDHB in normal and tumor tissues. We further analyzed the relationship between PDHB and somatic mutations and immune infiltration. Finally, we preliminarily explored the impact of PDHB on ccRCC. Results The expression level of PDHB was lower in tumor tissue compared with normal tissue. Meanwhile, the expression level of PDHB was also lower in high-grade tumors than low-grade tumors. PDHB is positively correlated with prognosis in ccRCC. Furthermore, PDHB may be associated with decreased risk of VHL, PBRM1 and KDM5C mutations. In 786-O cells, copper chloride could promote the expression of cuproptosis genes (DLAT, PDHB and FDX1) and inhibit cell growth. Last but not least, we found that PDHB could inhibit the proliferation and migration of ccRCC cells. Conclusion Our results demonstrated that PDHB could inhibit the proliferation, migration and invasion in ccRCC cells, which might be a prognostic predictor of ccRCC. Targeting this molecular might provide a new therapeutic strategy for patients with advanced ccRCC.

LncRNA AL161431.1 is currently known as a factor that can promote epithelial-mesenchymal transition. However, its role in the prognosis, immune infiltration and progression of bladder cancer (BLCA)patients is still unclear. The expression of AL161431.1 is elevated in BLCA tissues compared to normal tissues according to the TCGA database. By combining this data with clinical information, patients with high AL161431.1 expression have more advanced clinicopathological stages and shorter survival periods. Furthermore, AL161431.1 was identified as an independent prognostic factor for bladder cancer. We further analyzed the differences in immune infiltration, tumor mutation burden (TMB), immune checkpoints, and sensitivity to immunotherapy between groups with different levels of AL161431.1 expression. Enrichment analysis demonstrated that AL161431.1 is associated with numerous immune signaling pathways. High expression of AL161431.1 in cancer tissues was confirmed by qRT-PCR. CCK8, transwell, and wound healing demonstrated the oncogenic effects of AL161431.1. In conclusion, AL161431.1 is associated with immune infiltration in bladder cancer and has the potential to become a biomarker for predicting the prognosis of BLCA.

Background This meta‐analysis aimed to clarify the connection between six polymorphisms in the FTO gene and susceptibility to cancer. Methods The relevant literature on the relationship between FTO variants and cancer susceptibility was comprehensively gathered from PubMed, Scopus, Embase, Medline, and Web of Science prior to May 20, 2024. Results Our analysis revealed that FTO rs9939609 had a certain correlation with an elevated cancer risk within the Asian demographic q vs. r (OR = 1.22, 95% CI = 1.07–1.39, p = 0.003); rq + qq vs. rr (OR = 1.18, 95% CI = 1.04–1.35, p = 0.011); qq vs. rr + rq (OR = 1.78, 95% CI = 1.39–2.27, p = 0.001). Additionally, FTO rs1477196 was linked to a higher risk of thyroid cancer qq vs. rr + rq (OR = 1.47, 95% CI = 1.13–1.91, p = 0.004) and remarkably relevant to an increased cancer susceptibility for Caucasians (q vs. r (OR = 1.29, 95% CI =1.06–1.57, p = 0.009); rq + qq vs. rr (OR = 1.37, 95% CI = 1.04–1.80, p = 0.024)). In the stratified analysis of rs8047395, the results indicated that rs8047395 had a certain correlation with cancer susceptibility for thyroid cancer q vs. r (OR = 1.23, 95% CI = 1.01–1.51, p = 0.041) and qq vs. rr + rq (OR = 1.53, 95% CI = 1.24–1.91, p < 0.01). Conclusion FTO rs9939609 showed a correlation with cancer risk among individuals of Asian descent. FTO rs1477196 was correlated with an increased risk for thyroid cancer and remarkably relevant to an increased cancer susceptibility for Caucasians. FTO rs8047395 was associated with the risk of thyroid cancer.

LncRNA AL161431.1 is currently known as a factor that can promote epithelial-mesenchymal transition. However, its role in the prognosis, immune infiltration and progression of bladder cancer (BLCA)patients is still unclear. The expression of AL161431.1 is elevated in BLCA tissues compared to normal tissues according to the TCGA database. By combining this data with clinical information, patients with high AL161431.1 expression have more advanced clinicopathological stages and shorter survival periods. Furthermore, AL161431.1 was identified as an independent prognostic factor for bladder cancer. We further analyzed the differences in immune infiltration, tumor mutation burden (TMB), immune checkpoints, and sensitivity to immunotherapy between groups with different levels of AL161431.1 expression. Enrichment analysis demonstrated that AL161431.1 is associated with numerous immune signaling pathways. High expression of AL161431.1 in cancer tissues was confirmed by qRT-PCR. CCK8, transwell, and wound healing demonstrated the oncogenic effects of AL161431.1. In conclusion, AL161431.1 is associated with immune infiltration in bladder cancer and has the potential to become a biomarker for predicting the prognosis of BLCA.

The Beijing-Tianjin-Hebei (Jingjinji) region is the most densely populated region in China and suffers from severe water resource shortage, with considerable water-related issues emerging under a changing context such as construction of water diversion projects (WDP), regional synergistic development, and climate change. To this end, this paper develops a framework to examine the water resource security for 200 counties in the Jingjinji region under these changes. Thus, county-level water resource security is assessed in terms of the long-term annual mean and selected typical years (i.e., dry, normal, and wet years), with and without the WDP, and under the current and projected future (i.e., regional synergistic development and climate change). The outcomes of such scenarios are assessed based on two water-crowding indicators, two use-to-availability indicators, and one composite indicator. Results indicate first that the water resources are distributed unevenly, relatively more abundant in the northeastern counties and extremely limited in the other counties. The water resources are very limited at the regional level, with the water availability per capita and per unit gross domestic product (GDP) being only 279/290 m3 and 46/18 m3 in the current and projected future scenarios, respectively, even when considering the WDP. Second, the population carrying capacity is currently the dominant influence, while economic development will be the controlling factor in the future for most middle and southern counties. This suggests that significant improvement in water-saving technologies, vigorous replacement of industries from high to low water consumption, as well as water from other supplies for large-scale applications are greatly needed. Third, the research identifies those counties most at risk to water scarcity and shows that most of them can be greatly relieved after supplementation by the planned WDP. Finally, more attention should be paid to the southern counties because their water resources are not only limited but also much more sensitive and vulnerable to climate change. This work should benefit water resource management and allocation decisions in the Jingjinji region, and the proposed assessment framework can be applied to other similar problems.

Dynamic graph anomaly detection (DGAD) is critical for many real-world applications but remains challenging due to the scarcity of labeled anomalies. Existing methods are either unsupervised or semi-supervised: unsupervised methods avoid the need for labeled anomalies but often produce ambiguous boundary, whereas semi-supervised methods can overfit to the limited labeled anomalies and generalize poorly to unseen anomalies. To address this gap, we consider a largely underexplored problem in DGAD: learning a discriminative boundary from normal/unlabeled data, while leveraging limited labeled anomalies when available without sacrificing generalization to unseen anomalies. To this end, we propose an effective, generalizable, and model-agnostic framework with three main components: (i) residual representation encoding that capture deviations between current interactions and their historical context, providing anomaly-relevant signals; (ii) a restriction loss that constrain the normal representations within an interval bounded by two co-centered hyperspheres, ensuring consistent scales while keeping anomalies separable; (iii) a bi-boundary optimization strategy that learns a discriminative and robust boundary using the normal log-likelihood distribution modeled by a normalizing flow. Extensive experiments demonstrate the superiority of our framework across diverse evaluation settings.

Most recommendation benchmarks evaluate how well a model imitates user behavior. In financial advisory, however, observed actions can be noisy or short-sighted under market volatility and may conflict with a user's long-term goals. Treating what users chose as the sole ground truth, therefore, conflates behavioral imitation with decision quality. We introduce Conv-FinRe, a conversational and longitudinal benchmark for stock recommendation that evaluates LLMs beyond behavior matching. Given an onboarding interview, step-wise market context, and advisory dialogues, models must generate rankings over a fixed investment horizon. Crucially, Conv-FinRe provides multi-view references that distinguish descriptive behavior from normative utility grounded in investor-specific risk preferences, enabling diagnosis of whether an LLM follows rational analysis, mimics user noise, or is driven by market momentum. We build the benchmark from real market data and human decision trajectories, instantiate controlled advisory conversations, and evaluate a suite of state-of-the-art LLMs. Results reveal a persistent tension between rational decision quality and behavioral alignment: models that perform well on utility-based ranking often fail to match user choices, whereas behaviorally aligned models can overfit short-term noise. The dataset is publicly released on Hugging Face, and the codebase is available on GitHub.