<i>Background</i>. With an enormous amount of research concerning kidney cancer being conducted, various treatments have been applied to its cure. However, high recurrence and metastasis rates continue to pose a threat to the survival of patients with kidney renal clear cell carcinoma (KIRC). <i>Methods</i>. Data from The Cancer Genome Atlas were downloaded, and a series of analyses were performed, including differential analysis, Cox analysis, weighted gene coexpression network analysis, least absolute shrinkage and selection operator analysis, multivariate Cox analysis, survival analysis, and receiver operating characteristic curve and functional enrichment analysis. <i>Results</i>. A total of 5,777 differentially expressed genes were identified from the differential analysis. The Cox analysis showed 1,853 significant genes (<span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="19.289pt" style="vertical-align:-0.6370001pt" id="M1" height="9.2729pt" version="1.1" viewBox="-0.0498162 -8.6359 19.289 9.2729"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-81" d="M600 480C600 590 528 650 384 650H143L137 622C222 614 225 607 210 531L130 127C113 41 106 36 23 28L17 0H294L300 28C204 36 195 42 212 127L243 284L314 263C327 263 339 263 352 264C465 271 600 337 600 480ZM508 481C508 351 402 304 329 304C289 304 265 311 250 317L295 559C302 594 310 606 323 611C335 616 350 619 367 619C455 619 508 573 508 481Z"/></g><g transform="matrix(.013,0,0,-0.013,11.658,0)"><path id="g117-91" d="M512 -3V55L134 254V256L512 456V514L75 281V230L512 -3Z"/></g></svg><span class="irelop"/><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="21.918pt" style="vertical-align:-0.6370001pt" height="9.2729pt" version="1.1" viewBox="22.8711838 -8.6359 21.918 9.2729"><g transform="matrix(.013,0,0,-0.013,22.921,0)"><path id="g113-49" d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"/></g><g transform="matrix(.013,0,0,-0.013,29.161,0)"><path id="g113-47" d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z"/></g><g transform="matrix(.013,0,0,-0.013,32.125,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,38.365,0)"><path id="g113-50" d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"/></g></svg>).</span></span> Weighted gene coexpression network analysis revealed that 226 genes in the module were related to clinical parameters, including Tumor-Node-Metastasis (TNM) staging. Least absolute shrinkage and selection operator and multivariate Cox analyses suggested that four genes (CDKL2, LRFN1, STAT2, and SOWAHB) had a potential function in predicting the survival time of patients with KIRC. Survival analysis uncovered that a high risk of these four genes was associated with an unfavorable prognosis. Receiver operating characteristic curve analysis further confirmed the accuracy of the risk score model. The analysis of clinicopathological parameters of the four identified genes revealed that they were associated with the progression of KIRC. <i>Conclusion</i>. The gene expression model consisting of CDKL2, LRFN1, STAT2, and SOWAHB is a promising tool for predicting the prognosis of patients with KIRC. The results of this study may provide insights into the diagnosis and treatment of KIRC.

Banlai Ruan

Xianzhen Feng

Xueyi Chen

Zhiwei Dong

Qi Wang

Kai Xu

Jinping Tian

Jie Liu

Ziyin Chen

Wenzhen Shi

Man Wang

Lu Qian

Qianshan Ding

Identification of a Set of Genes Improving Survival Prediction in Kidney Renal Clear Cell Carcinoma through Integrative Reanalysis of Transcriptomic Data

<i>Background</i>. Postoperative early biochemical recurrence (BCR) was an essential indicator for recurrence and distant metastasis of prostate cancer (PCa). The aim of this study was to construct a cancer stem cell- (CSC-) associated gene set-based signature to identify a subgroup of PCa patients who are at high risk of early BCR. <i>Methods</i>. The PCa dataset from The Cancer Genome Atlas (TCGA) was randomly separated into discovery and validation set. Patients in discovery set were divided into early BCR group and long-term survival group. Propensity score matching analysis and differentially expressed gene selection were used to identify candidate CSC-associated genes. The LASSO Cox regression model was finally performed to filter the most useful prognostic CSC-associated genes for predicting early BCR. <i>Results</i>. By applying the LASSO Cox regression model, we built a thirteen-CSC-associated gene-based early BCR-predicting signature. In the discovery set, patients in high-risk group showed significantly poorer BCR free survival than that patients in low-risk group (HR: 4.91, 95% CI: 2.75–8.76, <span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="19.289pt" style="vertical-align:-0.6370001pt" id="M1" height="9.2729pt" version="1.1" viewBox="-0.0498162 -8.6359 19.289 9.2729"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g113-81" d="M600 480C600 590 528 650 384 650H143L137 622C222 614 225 607 210 531L130 127C113 41 106 36 23 28L17 0H294L300 28C204 36 195 42 212 127L243 284L314 263C327 263 339 263 352 264C465 271 600 337 600 480ZM508 481C508 351 402 304 329 304C289 304 265 311 250 317L295 559C302 594 310 606 323 611C335 616 350 619 367 619C455 619 508 573 508 481Z"/></g><g transform="matrix(.013,0,0,-0.013,11.658,0)"><path id="g117-91" d="M512 -3V55L134 254V256L512 456V514L75 281V230L512 -3Z"/></g></svg><span class="irelop"/><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="28.182pt" style="vertical-align:-0.6370001pt" height="9.2729pt" version="1.1" viewBox="22.8711838 -8.6359 28.182 9.2729"><g transform="matrix(.013,0,0,-0.013,22.921,0)"><path id="g113-49" d="M241 635C89 635 35 457 35 312C35 153 89 -12 240 -12C390 -12 443 166 443 312C443 466 390 635 241 635ZM238 602C329 602 354 454 354 312C354 172 330 22 240 22C152 22 124 173 124 313S148 602 238 602Z"/></g><g transform="matrix(.013,0,0,-0.013,29.161,0)"><path id="g113-47" d="M113 -12C146 -12 170 11 170 46C170 78 146 103 114 103S58 78 58 46C58 11 82 -12 113 -12Z"/></g><g transform="matrix(.013,0,0,-0.013,32.125,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,38.365,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,44.605,0)"><path id="g113-50" d="M384 0V27C293 34 287 42 287 114V635C232 613 172 594 109 583V559L157 557C201 555 205 550 205 499V114C205 42 199 34 109 27V0H384Z"/></g></svg>).</span></span> The results were further validated in the internal validation set (HR: 2.99, 95% CI: 1.34–6.70, <span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="19.289pt" style="vertical-align:-0.2064009pt" id="M2" height="8.8423pt" version="1.1" viewBox="-0.0498162 -8.6359 19.289 8.8423"><g transform="matrix(.013,0,0,-0.013,0,0)"><use xlink:href="#g113-81"/></g><g transform="matrix(.013,0,0,-0.013,11.658,0)"><path id="g117-34" d="M535 323V373H52V323H535ZM535 138V188H52V138H535Z"/></g></svg><span class="irelop"/><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="28.182pt" style="vertical-align:-0.2064009pt" height="8.8423pt" version="1.1" viewBox="22.8711838 -8.6359 28.182 8.8423"><g transform="matrix(.013,0,0,-0.013,22.921,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,29.161,0)"><use xlink:href="#g113-47"/></g><g transform="matrix(.013,0,0,-0.013,32.125,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,38.365,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,44.605,0)"><path id="g113-54" d="M153 550H386L412 615L406 623H120L82 318C104 327 142 338 184 338C294 338 347 275 347 187C347 112 305 39 221 39C160 39 119 71 97 89C88 97 80 96 71 90C59 80 50 67 49 57C48 45 52 36 66 23C80 9 123 -12 169 -12C221 -11 288 15 342 59C403 109 431 165 431 225C431 308 366 395 238 395C212 395 165 379 127 364L153 550Z"/></g></svg>).</span></span> Time-dependent ROC at 1 year suggested that the CSC gene signature (<span class="inline_break"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="37.804pt" style="vertical-align:-0.2324905pt" id="M3" height="8.98583pt" version="1.1" viewBox="-0.0498162 -8.75334 37.804 8.98583"><g transform="matrix(.013,0,0,-0.013,0,0)"><path id="g190-66" d="M673 0V28C608 34 594 43 563 129C499 303 432 494 370 665L339 656L132 132C98 43 84 37 20 28V0H238V28C162 35 154 45 175 108C188 150 203 192 219 237H432C454 176 474 122 487 83S491 36 428 28V0H673ZM418 280H234C265 362 296 450 328 535H330L418 280Z"/></g><g transform="matrix(.013,0,0,-0.013,8.226,0)"><path id="g190-86" d="M716 650H478V622C549 617 570 605 575 566C579 535 582 493 582 401V326C582 161 537 40 393 40C269 40 205 133 205 294V521C205 611 213 617 291 622V650H33V622C111 615 120 611 120 521V272C120 154 148 80 208 34C252 0 309 -14 368 -14C434 -14 501 7 548 55C605 116 623 201 623 319V401C623 493 626 536 629 563C633 605 656 618 716 622V650Z"/></g><g transform="matrix(.013,0,0,-0.013,17.794,0)"><path id="g190-68" d="M614 175C564 76 510 21 408 21C256 21 146 149 146 336C146 488 235 629 402 629C510 629 570 586 597 480L626 488C620 541 614 582 606 638C578 643 510 665 429 665C206 665 44 527 44 316C44 157 153 -15 402 -15C474 -15 558 5 586 11C604 45 629 119 643 165L614 175Z"/></g><g transform="matrix(.013,0,0,-0.013,30.173,0)"><use xlink:href="#g117-34"/></g></svg><span class="irelop"/><span class="nowrap"><svg xmlns:xlink="http://www.w3.org/1999/xlink" xmlns="http://www.w3.org/2000/svg" width="28.157pt" style="vertical-align:-0.2324905pt" height="8.98583pt" version="1.1" viewBox="41.3861838 -8.75334 28.157 8.98583"><g transform="matrix(.013,0,0,-0.013,41.436,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,47.676,0)"><use xlink:href="#g113-47"/></g><g transform="matrix(.013,0,0,-0.013,50.64,0)"><path id="g113-57" d="M249 635C141 635 70 555 70 471C70 401 114 353 179 316C143 294 106 267 90 252C68 231 45 202 45 157C45 50 130 -12 237 -12C322 -12 435 52 435 169C435 256 372 304 303 343C349 374 375 398 383 407C401 429 411 458 411 487C411 569 344 635 249 635ZM238 603C285 603 337 567 337 482C337 422 310 385 276 358C205 393 145 426 145 500C145 552 179 603 238 603ZM248 20C183 20 125 70 125 163C125 218 158 268 206 300C284 261 355 217 355 143C355 66 308 20 248 20Z"/></g><g transform="matrix(.013,0,0,-0.013,56.88,0)"><use xlink:href="#g113-49"/></g><g transform="matrix(.013,0,0,-0.013,63.12,0)"><use xlink:href="#g113-49"/></g></svg>)</span></span> possessed better predictive value than any other clinicopathological features in the entire TCGA cohort. Additionally, survival decision curve analysis revealed a considerable clinical usefulness of the CSC gene signature. <i>Conclusions</i>. We successfully developed a CSC-associated gene set-based signature that can accurately predict early BCR in PCa cancer.

Bide Liu

Xun Li

Jiuzhi Li

Hongyong Jin

Hongliang Jia

Xiaohu Ge

Construction and Validation of a Robust Cancer Stem Cell-Associated Gene Set-Based Signature to Predict Early Biochemical Recurrence in Prostate Cancer

The AR signaling pathway plays an important role in initiation and progression of many hormone-related cancers including prostate, bladder, kidney, lung, and breast cancer. However, the potential roles of androgen-responsive long noncoding RNAs (lncRNAs) in hormone-related cancers remained unclear. In the present study, we identified 469 novel androgen-responsive lncRNAs using microarray data. After validating the accuracy of the array data, we constructed a transcriptional network which contained more than 30 transcriptional factors using ChIP-seq data to explore upstream regulators of androgen-responsive lncRNAs. Next, we conducted bioinformatics analysis to identify lncRNA-miRNA-mRNA regulatory network. To explore the potential roles of androgen-responsive lncRNAs in hormone-related cancers, we performed coexpression network and PPI network analyses using TCGA data. GO and KEGG analyses showed these lncRNAs were mainly involved in regulating signal transduction, transcription, development, cell adhesion, immune response, cell differentiation, and MAPK signaling pathway. We also highlight the prognostic value of HPN-AS1, TPTEP1, and LINC00623 in cancer outcomes. Our results suggest that androgen-responsive lncRNAs played important roles in regulating hormone-related cancer progression and could be novel molecular biomarkers.

Dan Wang

Mingyue Li

Jing Li

Xuechao Wan

Yan Huang

Chenji Wang

Pu Zhang

Yangguang Xu

Zhe Kong

Yali Lu

Xinmei Wang

Chuan Liu

Chaoneng Ji

Liang Li

Comprehensive Characterization of Androgen-Responsive lncRNAs Mediated Regulatory Network in Hormone-Related Cancers

Prostate cancer (PCa) is one of the most commonly diagnosed cancers in males worldwide. lncRNAs (long noncoding RNAs) play a significant role in the occurrence and development of PCa. eRNAs (enhancer RNAs) and SE-lncRNAs (superenhancer lncRNAs) are important elements of lncRNAs, but the role of eRNAs and SE-lncRNAs in PCa remains largely unclear. In this work, we identified 681 eRNAs and 292 SE-lncRNAs that were expressed differentially in PCa using a microarray. We also found that eRNAs transcribed from active open chromatin had significantly higher expression than those from active closed chromatin, and SE-lncRNAs had a little higher expression than eRNAs. Next, we constructed a transcriptional regulation network that eRNA-related enhancer and the target genes shared the same TF-binding motifs. Further, we investigated whether CTCF played a role in mediating the transcriptional regulation network. eRNAs, especially those that regulate androgen response genes, may be candidates for prognostic biomarkers and therapy targets. Our work provides a new perspective for developing medical treatments and therapies for prostate cancer.

Xiaona Zhang

Panpan Pang

Min Jiang

Qunfa Cao

Huili Li

Yi Xu

Yao Li

Xue Chen

Junsong Han

eRNAs and Superenhancer lncRNAs Are Functional in Human Prostate Cancer

Maolin Hu

Jiangling Xie

Huiming Hou

Ming Liu

Jianye Wang

Prognostic Value of DNA Methylation-Driven Genes in Clear Cell Renal Cell Carcinoma: A Study Based on Methylation and Transcriptome Analyses

About this journal

<p><em>Disease Markers</em> publishes papers related to the identification of disease markers, the elucidation of their role and mechanism, as well as their application in the prognosis, diagnosis and treatment of diseases.</p>


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<p>Chief Editor Paola Gazzaniga is an Associate Professor in the Department of Molecular Medicine at Sapienza University of Rome, Italy. Her core research focuses on liquid biopsies in patients with solid tumors.</p>


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Disease Markers publishes papers related to the identification of disease markers, the elucidation of their role and mechanism, as well as their application in the prognosis, diagnosis and treatment of diseases.

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Disease Markers | Hindawi

Disease Markers

Erasmus University Medical Center, Rotterdam, Netherlands

<p>This Issue is now open for submissions.</p>
<p>Papers are published upon acceptance, regardless of the Special Issue publication date.</p>


Call for papers

<div><p>
Human urological malignancies (UMs) represent a spectrum of malignancies with huge burden of disease. Conventional clinical management for UMs mainly relies on invasive diagnostics and treatment strategies guided by less precise population-level evidence.
</p><p>
For certain aggressive UMs, such as muscle-invasive bladder cancer and castration-resistant prostate cancer, treatments with adequate effectiveness are yet to be established. These challenges warrant the active discovery of biomarkers which form the basis for accurate and less invasive diagnostic approaches as well as for individualized therapies for UM.
</p><p>
In this Special Issue, we call for research and review articles on the innovation and clinical application of biomarkers for UMs. Research papers reporting multidisciplinary findings, such as those involving omics-based discovery as well as preclinical/clinical validation, are especially welcome.
</p><p>Potential topics include but are not limited to the following:</p><ul class="li_special"><li>
Innovation and application of nucleic acid, protein, and imaging biomarkers for UMs
</li><li>
Liquid biopsy for UMs
</li><li>
Evidence-based medicine for UM biomarkers
</li><li>
Molecular subtyping and personalized medicine for UMs
</li><li>
Molecular epidemiologic biomarkers for UMs
</li><li>
High-tech UM biomarker discovery (single-cell omics, multiplexed imaging, artificial intelligence, etc.)
</li></ul></div>